Has AI already peaked? New research points to a decline in AI adoption rates at large US companies.

Every two weeks, the US Census Bureau surveys 1.2 million American companies, asking a range of questions. Since 2023, that has included a question about whether or not a company made use of AI in the last two weeks.

While the general trend is upwards, there's been a decline in the use of AI for companies with more than 250 employees. That cohort is the group most likely to use AI, according to the bureau, peaking just shy of 14% and falling back down to 12% over summer surveys.

While this represents a small – and perhaps temporary – decline, it does suggest some degree of hesitation on the part of large enterprises, some of which are growing frustrated at poor returns on investment.

"The bottom line is that the biweekly Census data is starting to show a slowdown in AI adoption for large companies," noted Torsten Sløk, chief economist at Apollo Academy, in a post on the company's website.

In a post on social media, Arpit Gupta, an associate professor of finance at NYU Stern shared Apollo's chart and was asked what he considered the key takeaway.

Gupta noted that "trillions in AI cap ex should probably be reconsidered."

Those results come as companies continue to spend huge sums on data centers and other infrastructure to support AI rollouts.

Similarly, a report from MIT last month revealed 95% of companies are struggling to deliver financial return, fueling claims of a pending AI ‘bubble’.

AI adoption rates remain strong overall

Across all companies, the picture is a bit different. The 1.2 million companies were asked whether they used any type of AI over the last two weeks, with 9.7% saying yes — an increase from the survey two weeks prior at 8.8%.

However, that result was a significant dip from previous editions, which were trending above 9%. At the end of 2024, the figure was 6.3%, suggesting a slow upward trend.

In the most recent survey, 83.8% said they had not used AI of any type over the last two weeks, with 6.5% of companies reporting they weren't sure.

Indeed, UBS analysis from June noted that growth was steadily increasing — though that was written before the most recent dip.

"This means AI adoption is likely to soon cross the 10% threshold that took US e-commerce 24 years to reach," UBS noted.

Questionable question

These AI usage rates may sound low given the amount of attention the technology has been given of late.

While that may be partly down to hype surrounding the nascent technology, another issue is the phrasing of the question asked by the Census Bureau: "In the last two weeks, did this business use Artificial Intelligence (AI) in producing goods or services?"

Plenty of businesses will be using AI for automating customer service or other administrative tasks, for marketing, or for project marketing, noted one analyst – tasks that respondents may not see as part of directly producing a good or service.

The survey also asks whether the company expects to be using AI in the next six months, again to make products or services. In the most recent poll, 13.7% of all respondents said yes, up from 9.2% at the end of last year.

However, nearly two-thirds still said they don't expect to be using AI within that time frame.

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Welcome to ITPro's coverage of AMD Advancing AI 2025, coming to you live from the San Jose McEnery Convention Center. Hundreds of delegates are expected to fill the auditorium today to hear AMD CEO Lisa Su reveal the company's latest strategic plans for AI.

Su is expected to take to the stage at 9.30am PST and with her keynote slated to last for two hours, we can undoubtedly expect guests from both AMD and its partners to grace the stage.

With a little under two hours to go before Lisa Su's keynote starts, it's worth reflecting on what happened at the company's last Advancing AI conference, which only took place eight months ago in October 2024.

At that event, Su launched the enterprise-focused Ryzen AI Pro 300 Series CPU range, revealed the Pensando Selina data processing unit, and announced the arrival of Instinct MI325x accelerators. It's unlikely that the company has invited customers, partners, and press to hear updates on these products, but it could indicate the direction of travel.

At Computex in June 2024 Su announced the company was moving to an annual release cadence for its GPUs and with the launch of the Instinct MI325x accelerator series at the last Advancing AI we could see an update in this area.

The auditorium is starting to fill up at the San Jose McEnery Convention Center as we wait for Lisa Su to take to the stage for her keynote address this morning.

And we are underway with an opening video – apparently the key for the AI future is trust.

And here is the woman of the hour, Dr Lisa Su, CEO of AMD.

"I'm always incredibly proud to say that billions of people use AMD technology every day, whether you're talking about services like Microsoft Office 365 or Facebook or zoom or Netflix or Uber or Salesforce or SAP and many more you're running on AMD infrastructure," says Su. "In AI, the biggest cloud and AI companies are using instinct to power their latest models and new production workloads. And there's a ton of new innovation that's going on with the new AI startups."

As with many companies now – including partner Dell and rival Nvidia – AMD is talking up AI agents and its (positive) impact on hardware.

Su says that agentic AI represents "a new class of user".

"what we're actually seeing is we're adding the equivalent of billions of new virtual users to the global compute infrastructure. All of these agents are here to help us, and that requires lots of GPUs and lots of CPUs working together in an open ecosystem," she says.

AI isn't just a cloud or data center question now, it's also an endpoint question, and particularly the PC. "We expect to see AI deployed in every single device," she says.

Su hits on another theme that's becoming popular among many enterprise hardware providers: open source and openness.

"AMD is the only company committed to openness across hardware, software and solutions," Su claims. "The history of our industry shows us that time and time again, innovation truly takes off when things are open."

"Linux surpassed Unix as a data center operating system of choice when global collaboration was unlocked," she continues. "Android's open platform helped scale mobile computing to billions of users, and in each case, openness delivered more competition, faster innovation and eventually a better outcome for users. And that's why for us at AMD, and frankly, for us as an industry, openness shouldn't be just a buzzword. It's actually critical to how we accelerate the scale adoption and impact of AI over the coming years."

Su is now joined on stage by her first guest, Xia Sun of xAI, to talk about how the company behind Grok is working with AMD.

Sun focuses on how his small team is being bolstered by AMD hardware like MI300x and – of course – openness

Sun leaves the stage and now we have our first product announcement, the AMD Instinct MI350 Series.

"With the Mi 350 series, we're delivering the largest generational performance lead in the history of instinct, and we're already deep in development of Mi 400 for 2026," says Su.

"Today, I'm super excited to launch the Mi 350 series, our most advanced AI platform ever that delivers leadership performance across the most demanding models this series," she says, adding that while AMD will talk about the MI355 and the MI350 "they're actually the same silicon, but MI355 supports higher thermals and power envelopes so that we can even deliver more real world performance."

Su claims that the MI350 Series "delivers a massive 4x generation leap in AI compute to accelerate both training and inference". Some of the key claimed specs are:

• 288GB memory
• Running up to 20 billion paramaters on a single GPU
• Double the FP throughput compared to competitors
• 1.6x more memory than competitors

(These are AMD claims that haven't been independently verified by ITPro)

And now another guest: Yu Jin Song, Meta VP of engineering – Meta, of course, being the force behind Llama LLM.

"We're seeing critical advancements that started with [inferencing[ and now extending to all of your AI offering," says Yu Jin Song. "I think AMD and Meta have always been strongly aligned on ... neural net solutions."

"We try to shoot problems together, and we're able to deploy optimized systems at school. So our team really see AMD as a strategic and responsive partner and someone that we really like. Well, we love working with your engineering team. We know that we love the feedback, and you also hold the high standard. You are one of our earliest partners in AI," he adds.

Yu Jin Song explains that MI350 will become a key part of the company's AI infrastructure (it's already using MI300x accelerators).

"We're also quite excited about the capabilities of MI350x," he says. "It brings significantly more compute power and acceleration memory and support for FP4, FP6 all while maintaining the safety pull factor at mi 300 so we can deploy quickly."

These are some of the Instinct MI350 Series partners, which will have products coming to market with the chips in Q3 2025, including Oracle, Dell Technologies, HPE, Cisco, and Asus.

We're going from hardware to software, now, with Su saying this is "really [what] unlocks the full potential of AI". She leaves the stage and Vamsi Boppana, SVP of AI at AMD, takes the metaphorical mic to talk about ROCm.

"We have been relentlessly focused on what matters most, making it easy for developers to build better out of the box capabilities, easy setup, more collateral, stepping up community engagements," he says.

"We've been running hackathons, contests, meetups and mail, and our customers are deploying AI capabilities at unprecedented pace, and that's why we've significantly accelerated hour release cadence," he continues. "New features and optimizations are now shipping every two weeks. Leading models like llama and DeepSeek work on day zero."

He also points out AMD's partnerships with Hugging Face, Pytorch, OpenAI Triton, and Jax.

Now for his announcement: ROCm 7. This will, he says, "bringing exciting new capabilities to address ... emerging trends, and brings support for our MI350 series of GPUs".

"[It] introduces the latest algorithms, advanced features like distributed inference, support for large scale training and new capabilities that make it easy for enterprises to deploy AI effortlessly within ROCm 7," he says, adding that inference has been "the largest area of focus [and] we've innovated and invested at every layer of inference".

Boppana now invites his own guest to the stage, Eric Boyd CVP of AI platforms at Microsoft.

The two are discussing Microsoft and AMD's longstanding partnership, with Boyd saying Microsoft has been using "several generations of Instinct".

"It's been a key part of our inferencing platform, and we've integrated ROCm to our inferencing stack, making it really easy for us to take and deploy the models on the platform," he says.

Boppana is now talking about what he describes as a new opportunity for driving down inference costs: distributed inference.

"In any LLM serving application, there are two phases. There's the pre fill phase and there's decode phase," explains Boppana. "These two phases of the model are typically handled on the same GPU, but now if you [use] the same GPU, it often becomes a bottleneck for large models, or when demand spikes happen and you can get limited in performance or flexibility."

Disaggregating the pre fill and decode phase can "significantly improve throughput, reduce cost, and boost responsiveness", he says.

Here is that solution: ROCm Enterprise AI. "ROCm Enterprise AI makes it easy to deploy AI solutions with new cluster management software, it ensures reliable, scalable and efficient operation of AI cluster," says Boppana.

"Our mlops platform allows fine tuning and distillation of models with your own data and a growing catalog of models will come for specific industries," he adds.

Another announcement from Boppana: AMD Developer Cloud and Developer credits. All developers who are here will have a credit voucher in their email inbox right now, he says, which elicits cheers from the assembled crowd.

Boppana hands over to Anush Elangovan, CVP of software development, and Sharon Zhou, CVP of AI. Elangovan channels Steve Balmer: "Developers! Developers! Developers!" to yet more cheers – although he stopped after just three.

"We are serious about bringing rocket everywhere and to everyone, from client to the cloud," he continues. "In AI, speed is your mode. Access to compute is paramount. We've been delivering on speed, so now let's get you access to compute."

"With the developer cloud, anyone with a GitHub ID or an email address can get access to an instant GPU with just a few clicks," he says, directing developers who want to give it a go to dev.amd.com.

"We've also included a lot of easy to use frameworks like vlln, SG, Lang, pytorch, etc," he says. "You just select one of those frameworks, add your SSH key and then create and you're set."

Zhou briefly encourages developers in the crowd to "come say hi" at hackathons, university labs and elsewhere, before handing back to Boppana, who reveals the next of today's announcements: The ability to access ROCm 7 on Windows or Linux, on device or on client.

Boppana hands over to Forrest Norrod, EVP and GM of data center solutions, who is underlining the importance of agentic AI for AMD, as well as the performance improvements of fifth-generation Epyc CPUs.

He also talks up what AMD says is the continued importance of CPUs.

"Some will naively tell you that CPUs are less important in the age of AI, but that's not correct," he says. :With agentic AI, we see an explosion of autonomous agents accessing data and enterprise applications. This increases the needs for efficient, high performance x86 compute across the data center, then within the AI server itself, the CPU serves the demands of pre processing and workload orchestration to keep the GPUs working efficiently."

"Let me show you a few examples of how the right CPU can manage GPUs work better," he says. "Choosing poorly can create bottlenecks and stranded valuable resources. As you can see across a range of models and use cases, our fifth generation epic CPUs can boost the inference performance of the entire system from six to 17% that makes a huge impact on the overall TCO and performance of The AI deployment, and it's a critical element in designing the best possible AI system. So get much more out of your GPUs with the right CPU."

Lisa Su is back on stage to give the audience "a first look at the next big step for [AMD's] AI roadmap, the MI400 series". She quickly pivots from there to introduce Helios AI Rack.

"Think of Helios as really a rack that functions like a single, massive compute engine," Su says. " It connects up to 72 GPUs with 260 terabytes per second of scale up bandwidth. It enables 2.9 exaflops of FP four performance, and that is a great number."

Naturally, Su also takes a moment to make comparisons with AMD's competition, saying Helios supports "50% more HBM ... memory bandwidth, and scale out bandwidth".

And now, a surprise guest, "someone who's really an icon in AI", says Su: Sam Altman, CEO of OpenAI.

Altman talks about his company is seeing in the market.

"I think the models have gotten good enough that people have been able to build really great products, text, images, voice, all kinds of reasoning capabilities," he says.

"We've seen extremely quick adoption in the enterprise now, coding has been one area people talk a lot about, but I think what we're hearing again and again in all these different ways, is that these tools have gone from things that were, you know, fun and curious to truly useful," he says.

Asked by Su how compute demands are changing, Altman says: "One of the biggest differences has been, we've moved to these reasoning models. So we have these very long roll outs where a model that often think about come back with a better answer.

"This is really the pressure on model efficiency and long context for all this, we need tons of computers, tons of memory, tons of CPUs as well. And our infrastructure ramp over the last year and what we're looking over the next year has just been a crazy, crazy thing to watch."

Turning to the hardware used to support OpenAI's platforms like ChatGPT, Altman says the company is already running some work on the MI300x, "but we are extremely excited for the MI450, the memory architecture is great for inference. It can be an incredible option for training as well".

Su asks Altman what he sees in the future for AI. "How will the workloads evolve? What happens with – quote, unquote – AGI (artificial general intelligence)," she says.

"I think we're going to maintain the same rate of progress rated models for the second half of the decade as we did for the first," Altman responds. "I wasn't so sure about that a couple of years ago for a new research thing to figure out, but now it looks like we'll be able to deliver on that."

"So if you think forward to 2030 ... these systems will be capable of remarkable new stuff, about scientific discovery, running extremely complex functions throughout society, and things that we just couldn't even imagine," he continues. "It's really going to take these are huge systems now, very complex engineering projects, very complex research, and to keep on this [path of] scaling, we've got to work together across research, engineering, hardware, how we're going to deliver these systems and products.

"This has gotten quite complex, but if we can do on that, if we if we can deliver on that drives collaboration across the whole industry, we will keep this curve going," he concludes.

Altman leaves the stage and Su delivers some final thoughts to the audience.

"When I think about this past year, it's really redefined what progress in AI looks like. It's really moved at a pace unlike anything that we have seen in modern computing ... anything that we've seen in our careers, and, frankly, anything that we've seen in our lifetime," Su says.

"The future of AI is not going to be built by any one company or in a closed ecosystem. It's going to be shaped by open collaboration across the industry. It's going to be shaped because everyone is bringing their best ideas, and it's going to be shaped because we're innovating together. So on behalf of all of us at AMD, we look forward to changing the world with you together," Su concludes, and with that the keynote is over.

Cisco and ServiceNow have kicked off RSAC Conference 2025 in San Francisco by announcing a new aspect of their seven-year collaboration. It brings together the former’s AI Defense product with the latter’s SecOps, with the companies claiming the integration will provide “more holistic AI risk management and governance”.

Speaking ahead of the announcement, Cisco’s EVP and chief product officer Jeetu Patel told reporters: “We think the hardest thing in the history of security has been with AI – and the largest opportunity in the history of security has been with AI. And you will see as evidence of that right now is attackers are getting far more sophisticated.”

“We need to operate as an ecosystem and make sure that we’re leveraging each other’s strengths,” he added.

For mutual customers of Cisco and ServiceNow, this means an integration between Cisco AI Defense, which it launched in January 2025, and ServiceNow SecOps.

AI Defense will act as an enforcement layer, according to the companies, identifying threats and vulnerabilities specific to AI. SecOps, meanwhile, will bring workflows and automation to allow IT and security teams to respond to the potential issues surfaced by AI Defense.

Commenting on the partnership, Amit Zavery, chief product officer and chief operating officer at ServiceNow, said: “If customers are looking at ways Cisco is helping them and they want service now be part of that integrated solution, we wanted to make sure we do that properly and provide a much more seamless experience for our customers as well, so that we can solve the end to end problem instead of customers having to deal with all those things themselves.

“The work Cisco has been doing around AI security, as well as the broader security portfolio Cisco has, was very attractive to all of us here at ServiceNow, and we've been able to… work together to really create something unique for the customers.”

Patel, meanwhile, said that bringing the two services together “could have an exponential kind of return back to the customer”.

“Virtually every customer in the enterprise is a Cisco customer and a ServiceNow customer. So we already have the customers, they already had invested in both of us. They were interested in making sure that we could help them do a better job at harnessing the investment. So this seemed like a match made in heaven,” said Patel.

“We're going to be very methodical about getting something out, getting it validated in the market, making sure that there's success, and continue to keep innovating on it,” he added.

In terms of availability, organizations that are customers of both companies can expect to make use of this integration from the second half of 2025.

Open source project maintainers are drowning in a sea of AI-generated 'slop security reports', according to security report triage worker Seth Larson.

Larson said he’s witnessed an increase in poor-quality reports that are wasting maintainers' time and contributing to burnout.

"Recently I've noticed an uptick in extremely low-quality, spammy, and LLM-hallucinated security reports to open source projects. The issue is in the age of LLMs, these reports appear at first-glance to be potentially legitimate and thus require time to refute," he wrote in a blog post.

"This issue is tough to tackle because it's distributed across thousands of open source projects, and due to the security-sensitive nature of reports open source maintainers are discouraged from sharing their experiences or asking for help."

Larson wants to see platforms adding systems to prevent automated or abusive creation of security reports, and allow them to be made public without publishing a vulnerability record - essentially letting maintainers name-and-shame offenders.

They should remove the public attribution of reporters that abuse the system, take away any positive incentive to reporting security issues, and limit the ability of newly registered users to report security issues.

Meanwhile, Larson called on reporters to stop using LLM systems for detecting vulnerabilities, and to only submit reports that have been reviewed by a human being. Don't spam projects, he said, and show up with patches, not just reports.

As for maintainers, he said low-quality reports should be treated as if they are malicious.

"Put the same amount of effort into responding as the reporter put into submitting a sloppy report: ie, near zero," he suggested.

"If you receive a report that you suspect is AI or LLM generated, reply with a short response and close the report: 'I suspect this report is AI-generated/incorrect/spam. Please respond with more justification for this report'."

Larson isn't the only maintainer to raise the issue of low-quality AI-generated security reports.

Earlier this month, Daniel Stenberg complained that, while the Curl project had always received a certain number of poor reports, AI was now making them look more plausible - and thus taking more time to check out.

"When reports are made to look better and to appear to have a point, it takes a longer time for us to research and eventually discard it. Every security report has to have a human spend time to look at it and assess what it means," he said.

"The better the crap, the longer time and the more energy we have to spend on the report until we close it. A crap report does not help the project at all. It instead takes away developer time and energy from something productive."

AI adoption is slowing among workers globally as employees report hiding their use of the technology from leaders amid concerns over how it reflects on them.

Slack’s Autumn 2024 Workforce Index surveyed 10,000 desk workers around the world and found the growth in AI usage has cooled, with the study noting that usage rates plateaued after steady growth earlier this year.

This deceleration was particularly pronounced in the US, where AI usage grew from 32% to 33% in the last three months, compared to a 9% growth recorded during a three-month period earlier this year.

Slack found many workers are actually hiding their use of AI out of fear or confusion over whether it’s professionally acceptable to use the technology at work.

When presented with a list of 11 common workplace tasks, including writing messages, generating new ideas, analyzing data, or writing code, nearly half 48% of all desk workers said they were uncomfortable admitting to their manager that they used AI during these activities.

For those who reported feeling uncomfortable about their AI use, the most popular reasons offered were feeling like using AI is cheating (47%), the fear of being seen as less competent by using AI (46%), and the fear of being seen as lazy (46%).

Slack noted that the least common reason given for not being comfortable using AI was that ‘AI use is discouraged or not allowed by company policy, indicating its the perception around using AI rather than its permissibility that is putting employees off.

Christina Janzer, head of Workforce Labs at Slack, said this underscores an important takeaway for business leaders - that shifting the norms around AI could unlock confidence in their teams to leverage the technology in their workflows.

“Our research shows that even if AI helped you complete a task more quickly and efficiently, plenty of people wouldn’t want their bosses to know they used it,”she said.

“Leaders need to understand that this technology doesn’t just exist in a business context of ‘can I get the job done as quickly and effectively as possible,’ but in a social context of ‘what will people think if they know I used this tool for help?'”

Excitement for AI slows as workers struggle with norms around its use

Excitement around AI is also falling, according to Slack, which found that hype around the technology fell 6% among the global population from March to August this year.

Once again, this fall off in AI excitement was most prominent in the US, which fell by 9% over the same time frame.

Slack cautioned that this pattern matches a typical maturity curve for new technologies, but its data has highlighted a number of blockers that could be inhibiting confidence in the technology.

First among these was discomfort with AI norms, with workers unsure about what types of AI are permitted in their organization, and when it's professionally and socially acceptable to use the technology at work.

Some workers also raised the view that AI is not yet living up to the hype it has generated, with Slack’s data revealing a disconnect between workers and leaders on how they should spend the new time AI saves them every week.

“Employees are worried that the time they save with AI will actually increase their workload – with leaders expecting them to do more work, at a faster pace,” Janzer added.

“This presents an opportunity for leaders to redefine what they mean by ‘productivity,’ inspiring employees to improve the quality of their work – not just the quantity.”

Finally, workers said a lack of clear company permissions and training around AI was limiting their ability to fully embrace the technology.

For example, just 7% of desk workers consider themselves AI experts, whereas 61% of respondents said they had spent less than five hours learning how to use AI and 30% of workers said they’ve had no AI training at all.

As a result, Slack recommended that leaders ensure they give explicit permission for workers to use AI tools, and create a mix of employer and employee-led training programs, which 70% of workers said would be the most beneficial approach to AI training.

Slack also suggested businesses try to use AI more publicly to reconfigure company norms around its use, as well as making AI use more visible by creating specific communication channels for AI support, troubleshooting, and training.

The survey data revealed that those who are comfortable sharing AI use with their manager are 67% more likely to have used it for work, indicating that shifting these norms could go a long way in boosting adoption among staff.

The C-suite has enormous faith in AI, according to research from TeamViewer, with more than six-in-ten executives saying they'd even trust it to make decisions without human oversight.

The study also found that 70% trust AI to forecast future business scenarios.

However, IT decision makers were rather more cautious than the C-suite, with just half saying they'd trust AI to forecast future business scenarios and four-in-ten admitting they'd trust it to make decisions without human oversight.

IT leaders in the UK were warier still, with just three-in-ten comfortable with AI making decisions without human oversight.

Despite lingering concerns over C-suite perceptions of AI and human oversight, IT leaders did acknowledge the benefits of the technology. Three-quarters, for example, said AI allows them to focus on more high-level strategic work and that it's helped them acquire new skills they otherwise wouldn’t have learned.

"As AI adoption grows – 82% of UK decision-makers now engage with AI at least weekly, a notable rise from last year’s 52% – the business results and benefits are becoming increasingly evident," said Teamviewer.

"For instance, globally, IT professionals are leading the way in using AI tools, currently saving an average of 16 hours each month, compared to the six hours saved by public sector employees, who the research shows are less advanced in their AI adoption."

AI is viewed as a powerful tool for improving equality, with three-quarters of global respondents with disabilities or chronic illnesses saying it can increase accessibility, and two-thirds of parents or caregivers that it can help create equal job opportunities.

"AI offers immense potential for fostering equal opportunities in the workplace by supporting employees in overcoming challenges and streamlining tasks at all levels," said Constanze Backhaus, TeamViewer’s CHRO.

"Beyond just enhancing productivity, these tools enable more seamless collaboration across departments and roles, ensuring that every individual, from desk workers to frontline staff, can contribute effectively to the organization’s success."

AI will deliver financial benefits

Notably, two-thirds of UK decision makers believe that AI will have a favorable impact on revenue in the coming year, with C-level executives reckoning that increase could average 270%.

And they're confident about their AI security, with seven-in-ten saying they use AI cautiously with security measures in place.

Indeed, two-thirds of UK decision makers say they'd be willing to make bets on their organization’s ability to manage AI-related risks, with half of C-level executives even willing to bet a week or month’s pay.

Virtually all agree that more training is required, but eight-in-ten expect to get it in the next six to twelve months.

It's possible that the C-suite's high confidence in the reliability of AI is based, at least in part, on a lack of understanding.

Research published by Pegasystems this summer found that business leaders overwhelmingly overestimated their knowledge of AI. Virtually all claimed to have a good understanding of the technology and how it works - despite the fact that two-thirds were unable to pick an accurate description of generative AI.

The UK government is running a private beta of a new chatbot designed to help people set up small businesses and find support.

The generative AI chatbot, based on OpenAI’s GPT-4o technology, will be tested out by up to 15,000 business users, after earlier trials went well.

GOV.UK Chat is linked from 30 government business support pages, including 'set up a business' and 'search for a trade mark'. Users can ask questions about tax and the support available to them.

Technology secretary Peter Kyle said the use of chatbots could markedly improve efficiency and enable the government to provide timely support for small businesses across the country.

"Outdated and bulky government processes waste people’s time too often, with the average adult in the UK spending the equivalent of a working week and a half dealing with public sector bureaucracy every year," Kyle said.

"We are going to change this by experimenting with emerging technology to find new ways to save people time and make their lives easier, as we are doing with GOV.UK Chat."

If this trial goes well, it could ultimately be rolled out across the full government website, made up of 700,000 pages. This attracts over 11 million users per week and is the best-known digital service in the UK, according to YouGov.

"With all new technology, it takes time to get it right so we’re taking it through extensive trials with thousands of real users before it is used more widely," said Kyle.

"This is an essential part of our ambition to use AI to improve public services in a safe and reliable way, making sure the UK government leads by example in driving innovation forward."

During the first trial, nearly seven-in-ten users said the chatbot was helpful, with only 15% disagreeing.

However, the trial did throw up some problems with accuracy, generating a number of 'hallucinations'. Some of these were “challenging, or abusive, or even seductive”, according to Paul Willmott, chair of the government’s Central Digital and Data Office

Since then, developers behind the chatbot said it's improved significantly. The team has been working with subject matter experts at HMRC to score the accuracy of the chatbot's answers, assess AI answers against example answers written by content designers, and monitor for inaccurate or inappropriate answers and investigate any they find.

There's now also a message included in the onboarding process explaining the risk of inaccurate answers to users, and a link underneath every answer so that users can check the source guidance.

"Unlike ChatGPT, GOV.UK Chat is designed to draw on GOV.UK as the source of its answers,” according to Sam Dub and Josh Davey of the development team. “This means we can ensure it’s always using the most up-to-date guidance, and users can trust the answer comes from government."

There have also been improvements aimed at preventing the chatbot from giving harmful responses such as illegal answers, the sharing of sensitive financial information, or the adoption of a political position.

GitHub has revealed a new feature for its Copilot pair programmer that will inform developers when code suggested to them by AI is taken from public repositories.

It said the reasoning behind the move is to offer developers greater transparency when it comes to using Copilot’s code suggestions. 

“Some want to learn from others’ work, others may want to take a dependency rather than introduce new app logic, and still others want to give or receive credit for similar work. Whatever the reason, it’s nice to know when similar code is out there,” it said.

Currently in private beta, Copilot’s new ‘code referencing’ feature will check code suggestions - including surrounding code of approximately 150 characters - against all public code on the GitHub platform. Any matches, along with repository information, will be shown to the developer.

It is then up to the developer to accept the code suggestion - including information about matches - or block the suggestion. 

GitHub estimates that matches will occur in less than 1% of Copilot suggestions, although that figure depends on context. 

“In the context of an existing application with surrounding code, we almost never see a match,” it said. “But in an empty or nearly empty file, we see matches far more often.”

The code referencing solution could make ground on easing the concerns shared among developers and organizations regarding the potential legal issues that could arise from using suggestions from Copilot.

The release will be seen as an acceptance from GitHub that developers wish to know when the suggestions match public code. 

It comes amid scrutiny of the company over the training of Copilot on publicly available code, including public GitHub repositories.

GitHub continues to wrestle with a class action lawsuit it currently faces, one that was filed in the San Francisco US federal court last year. 

The complainants’ case centers around allegations that the company committed “software piracy on an unprecedented scale” by allowing Copilot to suggest code snippets from publicly available open source code repositories.

The case noted that open source projects are protected by a number of different licenses which require developers to correctly attribute the project’s authors’ names and copyright when using its code in other applications.

GitHub faces allegations that by suggesting code snippets through Copilot, it is removing the licenses that protect the open source projects from which the code was suggested, illegally violating their terms of reuse.

GitHub and Microsoft, which owns the platform, have filed multiple motions to dismiss the case, including its most recent effort in June 2023. The former was rejected and the latter will be heard in court in September.

Why do developers need code referencing?

GitHub Copilot was launched in 2021 and is designed to assist developers by suggesting blocks of code based on context. 

GitHub describes the tool as the world’s first at-scale AI pair programmer and, according to the company, has trained it on billions of lines of public code.

Recent figures from GitHub suggest it has been used by more than 1 million developers and more than 27,000 organizations to build code faster.

However, GitHub has acknowledged that developers would also like to know when suggestions made by Copilot match public code. 

It has been possible to block matching code since 2022. However, in November last year, Ryan Salva, vice president of product at GitHub, said: “While useful in some contexts, blocking matching suggestions doesn’t address all use cases”.

With code referencing, the developer is shown the matching code, the repos where the code appears, and the license governing each repo. 

Since the same code might appear in multiple places with occasionally conflicting licenses, developers can decide for themselves on attribution rather than having the matches blocked from the outset.

Interactions handled by AI in contact centers will grow from 3% to 14% as digital transformation strategies reduce reliance on human agents.

Research published by Gartner has shown businesses are expected to spend $18.6 billion on contact center (CC) conversational AI and digital assistants in 2023, up 16.2% from 2022’s figures. 

Further increases in investment are also expected in 2024, with the growth rising to 24% as businesses seek to reduce reliance on live agents.  

Contact center as a service (CCaaS) budgets are also set to grow as cloud-based solutions are implemented in modernization and digital transformation projects. 

Gartner observed that while the number of customer service interactions touched by AI has continued to increase, most were augmented rather than fully offloaded to a virtual agent. 

However, while 3% of interactions are expected to be handled by AI in 2023, analysts have forecasted that the figure will grow to 14% in 2027 as investment continues to increase. 

While the global conversational AI and virtual assistant market represented the fastest-growing segment in the contact center forecast, according to Gartner, the 16.2% growth is slightly down on 2022’s 17.6% figure. Analysts have attributed this to budget restrictions driven by economic and geopolitical uncertainty. 

That same uncertainty, however, benefits customer-facing projects as businesses seek to preserve revenue through differentiation and efficiency improvements. 

British Telecom is an example of a company seeking to cut costs through technologies, including artificial intelligence. In May, it announced plans to shed up to 55,000 jobs by 2030, with many coming from customer services as some staff are replaced by AI technologies. 

Looking to the longer term, Megan Marek Fernandez, director analyst at Gartner, said: “Generative AI and growing maturity of conversational AI will accelerate contact center platform replacement as customer experience (CX) leaders look to simultaneously improve the efficiency of customer service operations and the overall customer experience”. 

CCaaS adoption set to grow 

Gartner expects CCaaS investment growth to accelerate and receive funding associated with broader corporate digital transformation budgets. 

Adoption among contact centers with thousands of agents, which have been slow to move to CCaaS, is similarly expected to increase. 

Marek Fernandez said: “Customer service and support initiatives that have the potential to differentiate the customer experience or streamline customer service operations could receive easier investment ‘buy-in.” 

The customer perspective 

Other research published by Gartner has shown that the customer experience can be negatively affected by the use of AI technologies. 

A recent survey showed that only 8% of customers used a chatbot during their most recent customer service experience; of those, only 25% said they would do so again. 

Michael Rendelman, senior specialist, research, in the Gartner Customer Service and Support practice, said: “As generative AI makes [chatbots] more advanced, customer confusion about what chatbots can and can’t do is likely to get worse.” 

Google has announced the launch of a new scheme to help businesses across the UK train staff in artificial intelligence (AI) skills. 

The ‘New Fundamentals’ training scheme offers free courses for both individuals and businesses to gain practical AI skills and fine-tune their understanding of AI technologies. 

Created by AI experts at the tech giant, the new program offers ten ‘easy-to-follow’ modules, two of which have been rolled out at launch. 

This includes a course designed specifically for SMB leaders that will provide a crash course in machine learning and how this can be used to improve efficiency in small businesses, reduce costs, and drive growth. 

“It covers ways to apply different ML models, when and how to invest, and how to prepare data and resources for a machine learning project,” Google said in an announcement today. 

A second course on how businesses can enhance productivity with AI tools has also been rolled out upon launch, allowing workers to learn how to use AI products and tools in day-to-day activities. 

The launch of the SMB leader-focused course comes in response to a study conducted by the Federation of Small Businesses (FSB) that found just 2% of small businesses were using machine learning tools. 

Craig Beaumont, chief of external affairs at the FSB, welcomed the announcement, adding that SMBs across the UK have a great opportunity to capitalize on recent developments in the AI space and drive growth. 

“While only one in 50 small firms have started using machine learning, the potential of AI to fuel innovation and supercharge operations is starting to make waves in the business community,” he said. 

“We welcome Google’s early work on this to help small business owners adapt to take full advantage of the upcoming tech boom, and get their fair slice of a significant £400 billion ($515 billion) of economic benefit by 2030.”

The new AI training scheme from Google has launched in the wake of a report it published on the economic benefits of AI over the next decade. 

The UK Economic Impact Report, compiled by Public First, found that AI innovation could potentially create more than £400 billion in value for the UK economy by 2030 and “supercharge productivity”. 

Long-term, the report found that AI tools could help the average UK worker save over 100 hours a year, equivalent to nearly a full week of work. 

“Today, the UK has an exciting opportunity to unlock new economic growth with AI – and that growth becomes more sustainable if it’s inclusive,” said Ruth Porat, SVP and CFO at Google and Alphabet. 

“Google’s new training is designed to equip everyone with AI skills to work more efficiently, grow their business or career, and help ensure the benefits of AI-powered growth are felt in every industry and every part of the country.” 

No government in 2023 can afford to put its head in the sand when it comes to AI. Whether we like it or not, the genie is out of the bottle and artificial intelligence is only going to become more prominent in all our lives.

Amid concerns that UK AI regulation is being outpaced by the EU, the Labour Party has set out its stall for how it would approach AI if it’s successful in forming a government at the next election. While this is to be welcomed, it must carefully avoid ethical and legal pitfalls.

One such proposal is to use AI systems at the Department of Work and Pensions (DWP), to identify benefits fraud and error, help match unemployed people with suitable jobs, and process claims more efficiently.

It’s an admirable goal, but one riddled with potential problems. Any government looking to use AI to tackle fraud to a greater degree, particularly based on lessons derived from historical data, would do well to remember the Dutch childcare benefits scandal.

In short, the affair centered around a machine learning (ML) algorithm used by the Dutch government to identify fraudulent childcare benefits claimed. 

The system wrongly flagged applicants as risky and was later found by an investigatory committee to contain institutional biases, which were reflected in the algorithm’s output. The result was tens of thousands of innocent parents accused of benefits fraud.

Although there’s a clear political lesson here - the scandal led to the downfall of the Dutch government - there’s also a clear technological one too. AI is best applied to areas in need of improved productivity, but must always play second fiddle to human workers. 

The DWP’s current use of machine learning is actually a perfect example of this in action. It uses an algorithm trained on bundles of historical claims data to spit out potentially fraudulent Universal Credit claims.

Cases flagged by the algorithm are then reviewed by humans - a shining example of how tech can be used as an accelerant rather than an agent of total reform.

Shadow secretary of state for work and pensions Jonathan Ashworth has indicated that human workers would still make final decisions under Labour’s AI plans, which raises the question of how the party thinks its approach would improve upon DWP’s existing algorithm.

At the end of the day, if Labour is serious about using AI to improve the economy and recoup lost government spending, it could do a far better job applying it to HMRC than the DWP, as the former needs far greater scrutiny.

Benefits overpayments totaled £8.3 billion in FY2023, while underpayments totaled £3.3 billion. In comparison, a parliamentary report into tax compliance found that HMRC has failed to chase £42 billion in unpaid taxes and only around £1.1 billion of an estimated £4.5 billion lost during the pandemic to erroneous and fraudulent support claims.

Applied sensitively, AI would play a far more significant role in buttoning up the UK’s tax landscape than it could in working on the margins of unemployment.

A more effective HMRC, bolstered by thoughtful AI implementation, would bring an immense boost to the UK economy. This would come as a benefit to all operating in the UK, with businesses able to operate in a more thriving market and the government in a more secure place to provide corporate subsidies.

But while Labour rightfully highlights the threat of AI to jobs - supported by the recent cases of AI-driven mass layoffs and Big Tech hiring overhauls - the boosts to productivity and system efficiency for organizations that are struggling for staff are too tantalizing to pass up.

With HMRC having been identified as one such area in need of improvement, it would be foolish to not pursue this further.

GitHub has announced a public beta release of its Copilot Chat solution for all business users on Visual Studio and VS Code, marking the company’s first step in taking its wider Copilot X offering to the enterprise.

Announced back in March, Copilot X is the latest iteration of GitHub’s original Copilot AI pair programming assistant with features that leverage the GPT-4 model from OpenAI. 

GitHub said this latest evolution turns Copilot into a context-aware conversational assistant within the integrated development environment (IDE), which allows developers to execute complex tasks using simple prompts. 

It’s described as a “Chat-GPT-like experience” from within Visual Studio and VS Code, enabling efficient analysis of code blocks, as well as the provision of bug fixes.

As the centerpiece of Copilot X, GitHub said Copilot Chat will “swing the doors wide open” to an age where natural language powers the coding experience.

“Every developer on your team, from the least to the most experienced, will be able to build entire applications or debug vast arrays of code in a matter of minutes instead of a matter of days,” said Mario Rodriquez, vice president of product at GitHub. 

“Weeks or months spent slogging over unit tests and endless backlogs of boilerplate code will be a thing of the past.”

Once Copilot X becomes generally available, GitHub predicts every developer could be made ten times more productive, enabling a new generation of developers to be able to learn and build at speed.

The company highlighted data from Stack Overflow’s most recent developer survey, which revealed that developers often spend 30 minutes and sometimes up to two hours a day searching for solutions to problems.

Copilot Chat is expected to reduce these times dramatically, improving developer productivity and freeing up more time to work on more innovative projects.

Contextual awareness

With Copilot Chat, GitHub said it’s taking what works for general-purpose generative AI chat assistants and adapting it to the developer environment. 

Located inside the IDE, Copilot Chat works by being contextually aware of the code a developer has written or the error messages shown to them.

The aim is to “keep developers in the flow” at a time when they are having to juggle multiple languages, cloud computing, programming frameworks, CI/CD workflows, open source software, and more.

To tackle this, Copilot Chat offers context-specific support to developers, as well as real-time guidance and suggestions for best practices, tips, and solutions for specific coding challenges. 

The tool also makes suggestions for fixing security issues, and can provide explanations and analysis of code for developers, as well as general troubleshooting. 

“We want to help developers spend their time on what matters most: building what’s next,” Rodriguez said. “And this work started with GitHub Copilot offering code suggestions right in the IDE – but now with Copilot Chat, developers can not only get code suggestions, but ask questions, get explanations, offer prompts for code, and more. That means they’re spending more time in the IDE – and in the flow.”

GitHub Copilot for Business administrators can provide access to the beta to development teams via the settings in the ‘Copilot’ tab. 

The Copilot Chat beta settings can then be found under the ‘Policies’ section, where admins can accept the terms and conditions of the tool. Once accepted, all users within the organization will receive detailed installation instructions via email.

Wikipedia co-founder Jimmy Wales has warned that the EU risks being outpaced by the US on AI development because of the stringent legislative approach it’s adopting with the rapidly developing technology.

Referencing the EU’s approach with its proposed AI Act, he said it represented the ‘typical’ attitude taken by the Union whenever it wants to control a potentially harmful technology.

“The EU AI act is going to be a classic European attempt to regulate something that’s just going to completely leave Europe behind the US.”

Wales’ comments were made during a roundtable discussion at which Amanda Brock, CEO at OpenUK, was also present.

Brock agreed with Wales regarding the dramatic pace of change over recent months. She called for an agile approach to legislation that was flexible and could adapt as AI evolved.

With the Wikipedia co-founder in agreement, Brock warned that AI regulation in the EU would present issues, saying: “It’s very prescriptive, it’s very detailed, it’s going to be very hard to follow and the open-source exclusions don’t go far enough”.

“It’s not going to work,” she said.

What is the EU AI Act?

The purpose of the act is to regulate the use of AI in the EU. At a high level, it aims to ensure that AI systems in the EU are “safe, transparent, traceable, non-discriminatory and environmentally friendly”.

Significantly, the European Parliament also wants to ensure that AI systems are overseen by people rather than automation in order “to prevent harmful outcomes”. 

It also wishes to establish a technology-neutral, uniform definition for AI that has future applications.

The concern regarding the EU’s plans is around the inhibitive effect of regulation. 

Brock noted that existing regulations already concern the handling of data depending on the use case - citing healthcare and finance as examples - and said “the amount of regulation around AI itself should probably be quite light and minimal”.

While the UK is no longer part of the EU, the actions taken by the Union and the flow of AI venture capital are two points of concern.

The UK, AI, and open source

According to OpenUK’s report, the Organisation of Economic Cooperation and Development (OECD) - using data from GitHub - found that the UK was the third largest contributor to public AI projects, accounting for 4% in 2022. India was top at 23%, and the 27 EU member states and the USA accounted for 14% each.

So far in 2023, the UK has received $29.2 billion in venture capital investment for AI, an 8.8% increase over 2022. The US’s total so far of $461.4 billion, however, stands 20.7% over its 2022 figure. The numbers indicate the UK still has a way to go before it can truly declare itself a leader in global AI.

The report also examined attitudes towards AI and how it intersects with open source.

For example, although 40% of respondents to the State of Open Survey 2023 said that open source software offered a solution to concerns regarding AI ownership, 45% were neutral on the matter.

Transparency was seen as hugely important to respondents, with 85% of them saying that datasets used to train an AI should be included in the software bill of materials (SBOM), while only 2% disagreed.

Brock told ITPro she was concerned that AI was in a similar position to other key technologies developed over the last few decades. She said that unless action was taken to “open it up and democratize it” there was every chance it might end up in the hands of a few.

She said: “The fundamental decision to be made is a simple one”. 

“Will our government’s approach be one of control established through transparency and building trust engendered by openness, or will it be a decision to seek control by closing down innovation, thereby leaving the outputs of this most important of innovations, in a proprietary ‘black box’ without transparency?”

Microsoft has announced the launch of the new Microsoft AI Cloud Partner Program, which infuses AI technology into its existing cloud partner ecosystem.

Unveiled this week at its Inspire 2023 conference by CEO Satya Nadella, the Redmond giant said it added AI-specific capabilities to the program to provide a portfolio that utilizes the entire partner lifecycle – including onboarding, skilling, go-to-market, incentives, and co-selling. 

The new initiative replaces the Microsoft Cloud Partner Program, which launched back in October last year, with existing members being automatically moved to the new setup. 

Partners’ existing benefits and designations will also move across with them, Microsoft said.

In a blog post, Microsoft’s partner chief Nicole Dezen said businesses are already realizing the benefits of integrating AI capabilities through every level of their organizations.

“Through the Microsoft AI Cloud Partner Program, we’re providing partners with a comprehensive portfolio of investments for all partner business models, at every stage of maturity, from startups to independent software vendors, services to device partners,” she said.

As part of this AI reshuffle, Microsoft revealed it is investing $100 million in Azure Innovate, a program that aims to help partners build AI solutions that leverage analytics and AI functionality. 

The firm said the investment is a direct response to increased demand for analytics and AI, specifically its Microsoft Fabric and Azure OpenAI Service offerings.

Among the Microsoft AI Cloud Partner Program’s new AI-focused features is a new AI Transformation Playbook which serves up AI practice guidance and covers elements from skilling and innovating, to marketing, and selling.

A new Microsoft Azure specialization also aims to help partners demonstrate technical expertise and success in building intelligent apps across AKS, Cosmos DB, and AI, complete with unlockable co-sell benefits.

Additionally, Microsoft said partners will soon have access to a forthcoming ‘industrial metaverse’ which the company says will enter public preview in early 2024.

Elsewhere, other notable additions include a new ‘Era of AI’ suite of ready-to-go marketing assets, an opportunity to participate in Microsoft’s Security Copilot design advisory council, as well as access to the new AIM program, which provides resources for those helping customers migrate to the cloud.

“Moving forward, the capabilities of AI and the Cloud will be integrated and inseparable,” Dezen said. “Our enriched AI services portfolio empowers partners and customers to innovate on the most interconnected technology ecosystem.” 

IT spending around the world will total $4.7 trillion in 2023, driven by a surge in software spending, according to a new forecast by Gartner.

The new spending represents 4.3% increase year-on-year and has been driven largely by software and IT services.

The report projects that worldwide IT spending could grow as much as 8.8% in 2024, to a total of $5.1 trillion.

Gartner found that at present, AI has not had a large impact on IT spending and that in the future organizations are likely to implement it using existing budgets.

For example, Google Duet AI and Microsoft Copilot are being added to product suites that are free or already factored into budgets, while other businesses will gain AI capabilities as part of contracts with their enterprise software providers.

“A great deal of software out there will get AI without a price increase, so at no cost to the end-user,” John-David Lovelock, distinguished VP analyst at Gartner told ITPro.

Lovelock argued that AI is impossible to track due to the fact that it is both a technology and an approach, with the example of chatbots and image generators as two AI tools operating in completely different markets.

“AI and generative AI is unique, in that it can use every form of technology as a channel to market,” he said.

“It'll be part of printers, it'll be part of mobile phones, it’ll be part of your software, it’ll will be part of your telecom services, your security services. Even your consulting services and outsourcing services will either use AI to deliver a better product to their end users, or they will add AI to the product feature mix for their end users.”

Lovelock also predicted that companies such as Accenture, Deloitte, PwC, and Ernst and Young will start using artificial intelligence for better product delivery, scale, and reduce costs without adding more staff.

‘Higher-level’ strategy shift

Lovelock has previously stated that IT spending is “recession-proof” and that companies that failed to increase IT spending could lose out. 

He told ITPro that this trend had continued, with some laggard companies focused on margin and cost control while others had shifted their exterior revenue-growth focus to making improvements to internal processes.

“In a sense, there is a higher-level change, where corporations coming out of 2022 into 2023 were primarily focused on revenue growth,” he told ITPro.

“Starting in maybe the end of 2022, beginning of 2023, they started to put into those digital transformation projects more work into efficiencies on the interior, on how they can make their departments more effective so that they can get to the new revenue stream?”

This drive for internal efficiencies has coincided with increased interest in infrastructure software, and Lovelock said that Gartner has seen “really strong growth” in robotic process automation (RPA).

Addressing job losses to AI, Lovelock acknowledged that it is a “tricky” issue, but maintained that it would ultimately come down to the business decisions of employers and whether they wanted to merely maintain productivity or improve it.

“For every job, there is a percentage of the time that is toil and a percentage of time that is value added. If you use AI to completely take away the toil, you wouldn’t get rid of the person because they’re adding value.

“In fact, you may start adding more people because the value that you used to get from one employee has gone up because they're no longer doing toil. So if you reduce their toil by four hours a day, now you're getting eight hours per day of value rather than four and that changes the cost equation for that role.”

The devices segment continues to decline, in line with another recent piece of research by IDC, down 8.6% in 2023 to $700 billion.

Lovelock cited mobile phones as an example, the sales of which are not expected to return to pre-pandemic levels until 2025. Gartner findings indicate that following this brief recovery, the devices will return to “close to zero percent growth” by 2027.

Splunk has launched new AI tools for its security and observability platform as well as its new Edge Hub for more streamlined edge data analytics. 

The Splunk AI Assistant - now in preview - is designed to assist users with the authoring of Splunk Processing Language (SPL) queries.  

Generative AI is leveraged via an AI chatbot to write or explain SPL, something the company says will improve time to value and make SPL more accessible. 

Mark Woods, chief technical advisor EMEA at Splunk, told ITPro that training for the model behind the generative AI was based on the company’s own knowledgebase and that the limited release preview had been based on the company’s own internal experts. 

Woods’ words stand to reassure customers concerned that sensitive data might turn up in the AI. 

He said: “We wouldn’t have any visibility of the actual query that you’re putting in place, nor of any of the data that you are taking”. 

As well as the Generative AI tooling, Splunk also previewed ML-Assisted Thresholding to use historical data and patterns to create dynamic thresholds.  

Its IT Service Intelligence also features greater detection accuracy, with the Outlier Exclusion for Adaptive Thresholding aimed at detecting and omitting abnormal data points and outliers. 

A preview of the OpenTelemetry Collector was also announced, aimed at providing a unified view of an enterprise’s infrastructure and services.  

Splunk Edge Hub 

The new Edge Hub device was also shown off and is aimed at simplifying the ingestion of data into Splunk’s analytics platform. 

The compact device weighs in at just over 600g and designed to be mounted in a variety of customer environments. Splunk envisages customers simply plugging it in and using the device to stream environmental data back for analysis. 

Onboard sensors comprise temperature, humidity, light, and sound. The device also features an accelerometer and gyroscope, and it can monitor air pressure and air quality. Typical applications of the latter sensor - according to its manufacturer, Bosch - include spotting unusual gases - indicating a possible leak - and spoiled food detection. 

The device also features a screen with a dashboard showing the current sensor readings and can be connected via WiFi, Bluetooth, LTE, or Ethernet. Power is supplied by either USB-C or Power-Over-Ethernet. 

The Cortex-based processor within the device - a Verdin IMX8M+ Quad with 8GB of RAM - has sufficient power for collection and collation of data from the sensor suite. 

Woods said to ITPro that he expected the device would open customers’ eyes to the data in their OT and IoT environments: “They suddenly start to see the data that they already have all around them”. 

“All those functional domain-specific environments, they become really tangible and valuable to customers.” 

Who is Splunk’s Edge Hub for? 

Woods identified three main user groups for the device. Compared to fitting out an environment with sensors, Splunk feels the Edge Hub represents a relatively low barrier to entry, so the first user group targeted is the one that has yet to use sensors and the data does not exist. 

The second group, according to Woods, might have some sensors installed, but the systems are closed and Splunk’s device represents a useful bridge. 

Finally, there is the group that is already equipped with sensors in their environment - (Woods cited Mars as an example - and the Edge Hub provides additional resilience or a way to experiment without adding a toll to an operation system. 

Splunk said that data from the device could be used as part of its predictive analytics platform to spot anomalies in manufacturing processes as well as detect, investigate and remediate physical threats from a single platform. 

The Splunk Edge Hub is US-only for now - although the company plans to roll it out in EMEA and APAC. It is also only available through the company’s partners. 

8x8 has announced its new partner ecosystem which will allow organizations to embed technologies such as AI into its unified communications platform to enhance business intelligence, insights, and analytics.

The new 8x8 Technology Partner Ecosystem has been designed to democratize next-generation integrations without requiring complex, custom development, or expensive overhead costs, 8x8 said. 

Fresh integrations with more than 20 new partners mean businesses of all sizes can add AI-driven customizable experiences to help with their customer experience goals. 

“Organizations expect their technology solutions and providers to innovate, adapt, and evolve to meet their current and future business and customer needs,” said Hunter Middleton chief product officer at 8x8. 

“We are thrilled to announce the 8x8 Technology Partner Ecosystem, and even more excited to partner with so many industry-leading, innovative CX vendors.

“The program complements our native AI-driven 8x8 XCaaS platform capabilities, giving enterprises the flexibility to build the right solutions to meet and exceed their customer experience goals.”

The move comes as the pace of innovation in the AI space is being rapidly accelerated by “billions of dollars of venture-backed investments in thousands of startups,” according to Zeus Kerravala, founder and principal analyst at ZK Research.

"Getting locked into a single communications provider's AI technology is holding back organizations from truly benefitting from the phenomenal rate of innovation taking place,” he said. 

“8x8's new Technology Partner Ecosystem allows enterprises to easily plug in the right AI solutions that best meet their business needs and transform the contact center and customer engagement."

The ecosystem is built on the 8x8 experience communications as a service (XCaaS) open architecture, an integrated cloud contact center, and a unified communications platform that bundles together contact center, business phone, video meetings, team chat, and SMS capabilities.

8x8 said it now has more than 30 total members, thanks to the latest additions of Cognigy, Avoira, Awaken, Balto, Converse360, Cresta, Engage Hub, LogicDialog, PromptVoice, Roboyo, Skybox Communications, and Syndeo.

The partner network aims to help customers break down data barriers and surface data across a variety of apps and interfaces, as well as ensure conversational context is maintained during any handoffs between the XCaaS platform and partner integrations.

Ultimately, 8x8’s aim is for customers to be able to access next-generation technology, create tailored solutions without expensive custom development, as well as leverage curated, seamless integrations.

Members of the Technology Partner Ecosystem will also be able to collaborate on go-to-market strategies, including promotional support, sales enablement, joint field engagements, and lead sharing.

“Customers want to engage across new channels and through emerging technologies, so we are excited to partner with 8x8 on delivering transformative AI-powered digital and voice CX solutions to their global customer base leveraging Cognigy's industry-leading conversational and generative AI automation platform," said Hardy Myers, SVP of business development and strategy at Cognigy. 

"The 8x8 Technology Partner Ecosystem has already demonstrated remarkable success propelling 8x8 to the forefront of Cognigy's strategic partners, enabling us to penetrate untapped markets and attract new customers.”  

KPMG has announced a new five-year commitment to Microsoft cloud and artificial intelligence (AI), in a collaboration aimed at modernization and the expansion of AI across the industry.

Tools within the Microsoft 365 and Azure OpenAI service will be available through early access to business units throughout KPMG, such as its audit, tax, and advisory groups.

KPMG expects that clients will be able to derive a number of benefits from access to Azure OpenAI Service and Microsoft Fabric. 

The Big Four’s auditing platform, KPMG Clara, will offer improved AI-led data analytics after having Azure Cognitive Services integrated. 

It said that its 85,000 auditors will have time freed up to focus on “high-risk areas of the audit, sector-specific risks, and challenges” during the hundreds of thousands of audits the company completes every year.

Microsoft’s Azure OpenAI and Fabric will also interface with KPMG Digital Gateway, including KPMG’s entire legal and tax suite. It is hoped this will lead to greater oversight and management of tax

The companies have trialed using Azure OpenAI to produce environmental, social, and governance (ESG) tax transparency reports through the analysis of a company’s ESG data and patterns.

“Our renewed and strengthened relationship with Microsoft is an exciting moment for our people and our clients,” said Bill Thomas, global chairman and CEO at KPMG International.

“It will help harness the power of our multidisciplinary model by ensuring that our people always have the right expertise, skills, and tools to overcome challenges and provide the very best advice to clients. It will also help make KPMG a more agile and resilient business that continues to be an interesting and exciting place to work.”

The professional services firm has a worldwide workforce of 265,000, and it is hoped that AI can be used to improve productivity across the board. The deal could be worth $12 billion in growth opportunity over the period.

Global IT consultancy Xebia has already used Azure Synapse Analytics and KPMG One Data Platform to better track its data infrastructure. With the platform, it has been able to improve the detail of its ESG reporting as well as its data-driven decision-making.

Coca-Cola EuroPacific Partners (CCEP) has also worked with KPMG and Microsoft to improve its internal operations, using generative AI to automate its back office.

In 2022, KPMG doubled its technical staff as it aimed to consolidate its suite of digital transformation tools. 

These included the big data analytics product ESG IQ, which allows KPMG clients to draw together structured and unstructured data from partners, social media, research papers, and legal documents using natural language processing (NLP).

Using the tool, finance companies can obtain a bird’s eye view of their assets through the lens of environmental responsibility, across a range of bonds and securities.

KPMG and Microsoft are signatories of the Carbon Call, which urges companies to improve carbon accounting for better emissions tracking. 

Knowledge workers are active across many industry sectors – anywhere, in fact, where a person needs to use their intellect and judgment to synthesize information and make decisions. Artificial intelligence (AI) is proving to be a help to knowledge workers in numerous ways, across a wide range of sectors. 

It might read as rather stating the obvious, but knowledge workers need insights to work with. This only exists when raw data is put through some sort of filter, but processing data can take a human much longer than it can take a machine, even when they’re working with coherent data sets. When more data sets are added in, with variables of differing nature, perhaps also with weighting, the human brain can become overwhelmed.

Not only are computers faster at working with big data, but they also don’t make mistakes due to oversight or fatigue, and can keep going 24 hours a day. With data analytics, machines can identify trends, spot anomalies, and understand nuances – and bring to our attention things we might easily dismiss or overlook. They can flag up the expected, the unexpected, and the unusual. 

The key benefit of industrialized AI is it helps knowledge workers spend more time acting on knowledge, or “insights”, and less on actually discovering that knowledge in the first place. That way, people can optimize their use of time. We look at four very different industry sectors and show how AI helps knowledge workers in them do a better job.

Medical diagnosis

Highly trained specialists need to analyze increasing volumes of data to help diagnose more and more patients with potential illnesses. Indeed, they have a data mountain to climb. The last thing medical specialist want to do is miss out on opportunities to help people as they sift these mountains of data.

In many areas of diagnostic medicine, AI is taking on the heavy lifting. It can do this by reviewing large amounts of data at speed, alerting professionals to anomalies and to specific characteristics it has been asked to identify. 

Moorfields Eye Hospital

At Moorfields, a team has developed a way to use AI to assess the severity of a form of age-related macular degeneration (AMD), as well as how it’s progressing and responding to treatment. Manually doing this work can take up to an hour per eye, and is prone to human error. The AI can match, and even outperform, predictions made by specialists in a fraction of a second. This is one of many ways Moorfields Eye Charity is researching the use of AI. 

Personalized Education 

Education is about the giving and receiving of knowledge. But individuals don’t all learn in the same way or at the same speed, and some of us need more support or nudging than others. At the higher levels, in particular, people can want to be more free to learn at their own pace. Educational institutions need to work differently to support and help them.

AI can be used to deliver educational content at different paces, and in different forms. Students can repeat learning exercises in different ways to help them fully understand concepts or complete learning modules. AI can help identify students who might need support, and provide useful nudges. 

Georgia State University

Georgia State University has been using an AI-enhanced messaging tool called Pounce since 2016. In 2021 the chatbot was integrated into course content, including so it could issue reminders to complete work by deadlines, and to take practice tests. Students getting this help turned in better grades. Using AI in this way helps teachers give their students the best support and assistance.

Mining and exploration

Mining involves complex processes and data analytics to identify the best localities in which to work. It also requires the use of precision equipment, which needs to be kept at peak performance and must maintain the highest possible safety and compliance standards.

Predictive maintenance can be used to help diagnose equipment and ensure parts are replaced before they break and call work to a halt. AI can also be used to analyze different data sets and suggest the most likely locations to mine. The technology might also be able to analyze the environmental conditions inside mines to support compliance – and play a role in maximizing yields by sorting the materials mined.

Streamlining mining logistics

A multi-national resources company, which manages freight of more than $1 billion annually for shipping across the world, wanted to enhance logistics with analytics capabilities. They tapped into Infosys Nia. This is an AI platform that helps companies manage their logistics – and combines data in numerous formats to help improve the success rate of discovery and exploration. It can be used to provide humans such as geologists and engineers with detailed information they can analyze further to help do their own jobs more efficiently.

City planning 

Cities and towns are complex, and people need to be able to move around. The right facilities – housing, work, leisure – need to be in the right places, too. We also are becoming increasingly aware of the need to reduce car travel and pollution and to increase access to open spaces and entertainment locally. Urban planners have their work cut out comparing variables and finding the solutions that work best.

AI-based tools can be used to model different scenarios for any particular area plan, taking into account environmental factors like heat, wind, or rainfall, and making the tweaks necessary to minimize negative environmental effects. In major cities, AI can analyze data on real places to support further development, such as transport planning, and provide analytics humans can use to better allocate resources. 

Transport for Wales

Transport for Wales (TfW) has worked with a third-party AI specialist, Optibus, to analyze bus use, demand for public transport, and movement patterns. Analyzing this data has allowed TfW to improve bus schedules, punctuality, and journey times.  

Using the Optibus software, TfW worked with all 22 local authorities to design a better public transport network with the aim of improving the quality of service and making buses more reliable, especially in rural areas.

Oracle is reportedly spending billions of dollars on Nvidia chips as well as AMD silicon and Ampere CPUs in a move that’s intended to make the company’s cloud service more attractive to a new wave of artificial intelligence (AI) companies.

The move appears to show the company taking its first serious steps towards pursuing a footing in the burgeoning generative AI market, one that’s really only taken flight over the past 12 months.

But it will doubtless be looking to avoid the same approach it took with cloud computing, where it arrived on the scene too late, only to find itself unable to claw back any meaningful market share from what we now consider the ‘big three’ hyperscalers: Microsoft Azure, AWS, and Google Cloud.

Oracle joined the cloud computing world only six years ago, considerably later than the ‘big three’. Recent studies put the company some way behind in terms of usage compared to its rivals.

“Oracle's Gen2 Cloud has quickly become the number-one choice for running generative AI workloads”, Ellison said in Oracle’s FY2023 Q4 earnings statement before highlighting the company’s GPU cluster technology. 

He also said “AI development companies have recently signed contracts to purchase more than $2 billion of capacity in Oracle's Gen2 Cloud”.

Is Oracle behind in generative AI?

In a recent interview, Jefferies analyst Brent Thill said that Oracle had some way to go in order to catch up with the leaders in the AI field. 

He referenced a $2 billion investment into AI at Oracle too, adding that competitors will be "seeing multiple times that [sum]". Though, Oracle has since clarified that the $2 billion figure Thill mentioned was 'incorrect' and that it has not announced a specific sum related to its AI investment generally.

“There’s no question that they’ve got their act together on a go-to-market as well as the technology side, but remember - they are multiple years late to the others that are ahead of them. Microsoft is like 40 laps ahead.

“When you think of the size of their business in the cloud versus AWS or Microsoft, it’s not even a fair comparison.”

Thill’s assessment is based on decades of monitoring Oracle, a company that has plenty of experience when it comes to the vast data loads of generative AI thanks to its enterprise database products.

“They’re engaged, but they are not where the others are at,” he added.

Referring to the AI wave, he said “if this tide lifts all boats, the other boats are way better boats”.

Oracle's "practical" AI approach

Chris Pang, VP analyst at Gartner, described Oracle as “radically practical”.

“They are very focused on the enterprise market, their customers and, as a result of that - in terms of how they innovate in different technologies - there always needs to be that business case for them to make that investment and also the use cases where it can actually add value.

Pang told ITPro that Oracle had been investing heavily in AI in recent years, but had been concentrating on its business applications and underlying technology platforms.

“It’s really been focused on a couple of areas,” he said.

One was the autonomous database and the other was adding capabilities such as predictive analysis and tools to do more with the data held in an enterprise’s databases.

Pang was at pains to differentiate types of AI - for example, machine learning, which has been deployed by many vendors over a number of years - and said “where we're heading towards now is this new frontier, which is around generative AI”. 

“And that's where I'd say it’s still kind of early days for Oracle, compared to perhaps some of its peers in the market."

Can Oracle catch up?

Ellison’s announcement indicating billions will be spent in order to attract AI workloads comes on top of recent feverish activity by the company in the AI sphere. 

It recently announced a collaboration with Cohere and plans to develop enterprise-level generative AI services.

Earlier this month, it announced the addition of generative AI-powered capabilities within its Oracle Fusion Cloud Human Capital Management service, to improve existing HR services.

However, as Thill observed, other companies are some distance ahead of Oracle. Microsoft, for example, invested $1 billion in OpenAI in 2019 and followed this with further investments culminating in a multi-billion-dollar deal earlier this year.

Microsoft has begun integrating the technology into its services - examples include its Copilot tools - and, as the provider of computing power for OpenAI, stands to benefit as companies seek to integrate their products with the platform.

As for Oracle, it appears to have been taken somewhat by surprise by the emergence of generative AI technology. For years, the company defined AI as “Adaptive Intelligence”, resulting in some confusion around its strategy. 

To challenge the leaders in the AI space, Oracle must therefore seek new ground and leverage its enterprise experience to address the privacy and regulatory issues currently being faced by its rivals. An opportunity to do so exists in the coming year or two, should the competition become mired in legal challenges.

A spokesperson told ITPro: “Oracle plans to develop powerful, generative AI services for organizations worldwide. We are uniquely positioned to deliver generative AI services spanning applications to infrastructure that help businesses solve today’s most complex challenges”.

“Oracle generative AI services will provide the highest levels of security, performance, and value in the industry."

“Customers can securely incorporate their own data to train specific models, deploy them on best-in-class AI infrastructure through OCI, and experience the business benefits immediately in their applications.”

Oracle has trailed behind the likes of AWS, Microsoft, and Google Cloud in the cloud computing stakes. 

Regarding the company’s delay, Pang said “Oracle likes to be completely vertically integrated”.

“It takes a lot of investment and a lot of time to get that going if you’re trying to do it completely by yourself versus piggybacking onto somebody else’s technology.”

He also cited Oracle’s enterprise customer base that “gave them a bit more time to investigate the cloud and to design a cloud infrastructure which was more aligned to customer requirements”.

That said, “I think it’s fair to say that their first-generation efforts to do cloud were perhaps… suboptimal. So that’s why they quickly redesigned it”.

During Oracle’s CloudWorld event in 2022, Ellison said that he decided to cancel the company’s first attempt at an infrastructure cloud, citing a desire to not simply copy its rivals. While the decision might have led to a different approach, it also cost the company time.

Ellison’s assertion that Oracle’s Gen2 cloud is now the number-one choice for AI has come at a time when the company’s competitors appear to have a substantial head start in the AI sector. 

Oracle was not the first mover when it came to databases, but later came to dominate the enterprise database market. However, it also lacked the first-mover advantage in the cloud marketplace but still languishes behind the leaders.

That the company is late to market with generative AI technology is without question. Its ability to catch up may lie in the potential missteps of its rivals and whether it can turn its enterprise experience into an attractive generative AI proposition.

Data security provider Rubrik has announced a new collaboration with Microsoft which aims to accelerate cyber recovery through generative AI and natural language processing (NLP). 

Building upon Rubrik’s existing channel offering, the move will see Rubrik Security Cloud integrated with Microsoft Sentinel and Azure OpenAI Service to reduce the time required to investigate and determine responses to cyber incidents. 

According to Rubrik Zero Labs’ State of Data Security report, just 56% of IT and security leaders reported developing or reviewing an incident response plan in 2022, while security operations center (SOC) and incident response teams have become inundated with alerts each day, making prioritization time-consuming.

Rubrik and Microsoft say the new integrations will help SOC teams investigate the most pressing cyber events, as well as offer up remediation guidance.

“In a time when bad actors are becoming increasingly inventive, organizations must lean on AI to turn the tables on attackers,” said Charlie Bell, executive vice president of security, compliance, identity, and management at Microsoft. 

“Working with Rubrik establishes a counterpoint to the modern threats that our customers are facing and allows organizations to react to incidents more quickly. 

“Through the speed of AI, we believe security defenders will be able to identify and stop attacks faster than ever before.”

The development expands Rubrik’s existing integration with Microsoft Sentinel, which was announced back in October 2022. 

Now, Rubrik says its platform’s ability to provide time series data insights directly into Microsoft Sentinel will enable organizations to tackle evolving cyber threats and safeguard sensitive data.

Thanks to the new integration, Rubrik can now leverage large language models and generative AI through OpenAI to automatically create recommended task workstreams in Microsoft Sentinel. Security and IT teams will be able to streamline incident creation, automate recommended task workstreams, as well as accelerate overall cyber recovery.

Bipul Sinha, CEO and co-founder at Rubrik, said the explosion of generative AI will kick start a new age of cyber attacks that “go far beyond human comprehension”.

“We must fight fire with fire and use generative AI to not only understand future cyber events but to also prevent and defend against them,” he said. “We’re thrilled to collaborate with Microsoft and continue to build upon our progressive and long-standing partnership. 

“This is an important step forward as we continue our mission of securing the world’s data and helping businesses achieve cyber resilience.”

Financial services giant JPMorgan Chase won't start using generative AI tools in its business until the technology's issues have been addressed.

The company's head of global tech strategy, innovation, and partnerships, Larry Feinsmith, confirmed its stance on generative AI this week, citing concerns around hallucinations and ethics as key blockades in adopting the technology.

“We at JPMorgan Chase will not roll out generative AI until we can mitigate all of the risks,” said Feintsmith during the first keynote address at Databricks Data + AI Summit 2023.

“You want to talk about responsible AI, you want to talk about hallucinations, you want to talk about misuse, you want to talk about having the right cyber capabilities so that the models aren’t poisoned or tampered.

“We’re excited – we’re working through those risks as we speak – but we won’t roll it out until we can do this in an entirely responsible manner, and it’s going to take time.”

The buzz around generative AI has surged in the last 12 months, with OpenAI’s large language models (LLMs) including ChatGPT and GPT-4, alongside tools like DALL·E 2, sparking a wave of enterprises hoping to adopt such systems within their internal workflows.

JPMorgan Chase, however, won’t begin integrating such models – most of which are trained on widely available online data – until developers can resolve some of the biggest challenges and limitations around it.

This isn't to say the financial services giant is staying away from AI completely; rather its approach is one of discipline in the interests of protecting its clients as well as its data. The firm has been using some form of AI across 300 or so use cases to date,  for instance, including for risk, fraud, and marketing.

Indeed, the financial services company’s cautious position differs from the approach many other companies have taken. 

Some businesses have already been keen to integrate tools to improve workflows, with the likes of text generation being one of the most widely touted use cases.

BuzzFeed announced in January it would begin to use generative AI to create online content, for example. 

Many other businesses are keen to explore the powerful capabilities of generative AI for fear of missing out on potential productivity gains.

JPMorgan Chase's approach is a cautious one, although it’s still very much keen to exploit AI to improve operations and benefit end-users.

It already deploys plenty of AI and machine learning capabilities internally, and recently established the chief data and analytics officer (CDAO) position.

Theresa Heitsenrether, the firm’s CDAO, has four key areas of responsibility including AI enablement, the ability to have a machine learning center of excellence, AI research, and managing a response AI team.

Databricks has launched a raft of new features for its flagship Lakehouse data lake platform, including artificial intelligence (AI) and natural language improvements as well as data governance features.

With Lakehouse AI, customers can develop generative AI applications, including large language models (LLMs), within the platform itself, with tools spanning the breadth of the AI lifecycle. This includes data collection, model development, and monitoring.

“This gives you control over your intellectual property,” said Databricks CEO Ali Ghodsi during his keynote address at Databricks Data + AI Summit 2023. He added every company in the next five years “will be an AI and data company” and would need the tools to create their own LLMs to be competitive, rather than rely on third-party services.

What is LakehouseIQ?

LakehouseIQ is the firm’s ‘knowledge engine’ that’s designed to make natural language interfaces, like chat bots, more accurate and relevant to the needs of a particular organization.

Sitting as a fabric between data and a front-end interface, the tool uses generative AI to understand a business’ unique culture, jargon, and data usage patterns, among other factors.

It ingests a vast quantity of internal material, including organizational charts, notebooks, and more, in order to understand how best to answer questions users ask via any chat interface.

It’s designed to be specific to each business, so natural language interactions are far more accurate and context-sensitive than classic chat bots. While not a chat bot or virtual assistant itself, it’s designed to improve the reliability and performance of these tools.

Databricks has made Lakehouse IQ available as an API, and the firm plans to integrate this into open source libraries.

New AI features in Lakehouse

Vector Search gives developers the capacity to improve accuracy of generative AI responses. The features creates vector embeddings based on files found in Unity Catalog — the central hub that gives customers access control, auditing, lineage and data discovery capabilities across their workspaces.

It creates vector embeddings from Unity Catalog files, and automatically updates them through integrations with Databricks Model Serving. Developers can also add query filters to provide better outcomes.

Improvements to AutoML also adds low-code functionality to finetuning LLMs. Any tweaks customers make to their LLMs will be owned by the enterprise, with data kept in-house and not shared with any third parties. This model can also be sahred within an organization with Model Serving, Unity Catalog and MLflow integrations.

Finally, Databricks has launched several open source models through its marketplace including MosaicML’s MPT-7B and Hugging Face’s Falcon-7B, as well as Stable Diffusion.

On the LLMOps front, MLflow AI Gateway lets customers centrally manage credentials for software as a service (SaaS) models or APIs, and provide access-controlled querying. This also allows for prediction caching to track repeat prompts and rate limiting to reduce costs.

MLflow Prompt Tools, meanwhile, is a no-code feature that allows users to compare different outputs from various models based on prompts that are automatically tracked within MLflow.

New governance features in Lakehouse

Also new in Lakehouse is LakeHouse Federation capabilities, which has broken down silos between data systems that previously existed on the platform. As a result, businesses are now able to discover, query and govern data across all their systems without the need to copy or move data. 

Query federation comprises catalog and querying functionality to consolidate and map out data asses from different platforms beyond the confines of Databricks, including MySQL, PostgreSQL, Redshift, Snowflake, Azure AQL Database, Azure Synapse, BigQuery, and others. Users can secure, audit and access data from one interface rather than having to move the data between different platforms.

Additional governance tools in Unity Catalog also lets customers apply access policies on any data asset that’s registered, including tables, rows, columns and tags. This comes ahead of future plans to define data access policies and push them out to other data warehouses.

The Lakehouse Federation's capabilities also include giving users a common approach to discovering and exploring both structured and unstructured data with a single query. Domain-owned data sources for analytics and AI use cases are also rapidly exposed, offering faster access to data. Finally, the changes make it possible for users to apply a single permission model for the data estate.

Databricks CEO Ali Ghodsi has denied paying over the odds to acquire large language model (LLM) startup MosaicML for $1.3 billion, rejecting any notion that such deals are fuelling an industry bubble.

Interest in generative AI has surged in 2023, and it’s underscored a flurry of venture capital activity alongside an explosion in new AI startups. But this has also sparked fears that generative AI is the latest in a long series of tech bubbles to have emerged and burst in recent memory, including crypto and metaverse technology.

Most investors responding to a TechCrunch survey, for example, cited AI when asked to predict the next major bubble. Stocks in both new AI companies and existing companies expanding into AI have increased dramatically in 2023.

Databricks has also made a string of recent AI acquisitions, with the MosaicML purchase following deals to acquire Okera in May and Rubicon in June.

Founded in 2020, MosaicML offers the tools for enterprises and startups to train their own LLMs and generative AI systems using data collected first-hand. Crucially, it lets customers own the IP for these systems.

Ghodsi cites MosaicML’s massive surge in revenue over the course of 2023 as among the main factors behind Databricks’ $1.3 billion valuation, which is six times greater than its latest investor-round valuation of $222 million.

The startup has grown from roughly $1 million in revenue to $20 million over six months, Ghodsi said, with the Databricks CEO predicting revenue to climb to $130 million within a year once the firm is granted access to Databricks’ platform and sellers.

Because many startups are being valued at roughly 20 times their revenue rates, Ghodsi said his firm’s valuation is relatively conservative.

“Just to absolutely push back on anyone who says this is a crazy number, just do the math,” he said.

“It’s a very sound, rational financial investment with a good ROI in the very short term, and I think it’s a very strategic long-term investment that’s going to pay off massively in the long run for the ecosystem and for Databricks as well.”

The Databricks CEO added the firm performed more complicated discounted cash flow (DCF) modeling and consulted with lots of customers too.

“Let’s make it clear, we’re not buying the company because we want to get 10x — that’s absolutely not the case,” he continued. “We are super excited about democratizing AI to all startups. We think this is going to be massive over the next five years.”

HPE has marketed itself as the ‘edge to cloud’ company for several years now, but that may soon change. The company has added artificial intelligence (AI) as a third pillar to its business model, and it’s set to take equal place alongside edge and hybrid cloud.

Beyond making a potentially awkward strapline, this new direction could make things awkward for partners, too.

During a breakout session at the company’s Partner Growth Summit, which took place the day before HPE Discover kicked off, AI was listed as a competency area for its Partner Ready and Partner Ready Vantage programs, alongside edge and hybrid cloud. 

However, while edge and hybrid each had five areas that are ready for partners to start working in now, all the elements listed under artificial intelligence – HPC for Enterprise, Supercomputing, Data Analytics Solutions, and AI Solutions – were listed as “future options”.

The earliest that future is likely to arrive is fiscal year 2025, according to Jesse Chavez, vice president of worldwide partner programs and operations, despite CEO Antonio Neri intimating at the Partner Growth Summit keynote that we can expect big news around AI, HPC, and supercomputing in his main Discover keynote.

So, where does this leave partners?

On the one hand, HPC and supercomputing are highly specialized areas that are traditionally sold directly as part of multi-year contracts featuring bespoke setups, so the channel isn’t being cut out of any immediate deals. AI, meanwhile, is an emerging field that few traditional resellers, systems integrators, or other channel partners would have a foot in, if any.

On the other hand, if this is going to be such an important part of HPE’s future, why not encourage partners to start making their first moves in this area sooner rather than later?

A change in mindset

HPE Partner Ready Vantage, the company’s second partner program, was also under the spotlight at the event. While Partner Ready has been in place since 2015, Partner Ready Vantage was only announced at the Partner Growth Summit last year and takes a different approach to how reselling is done.

As Chavez explained, Partner Ready Vantage puts what the partner does and what they want to achieve at the center, rather than the type of organization they are.

“In the past, we used to design programs that were focused on partner types, and in the future … our Partner Ready Vantage Program is actually recognizing different business models,” says Chavez.

Partner Ready Vantage has three tracks containing a total of five “centers of expertise”. The first of these is 'Build', which contains Solutions and Development, and also Technology Validation. There's also 'Sell', which contains as-a-Service. Finally, there's 'Service', which contains Customer Success, Professional Services, and Managed Services.

All of this, Chavez said, gives partners the chance to be part of a wider ecosystem which opens up the opportunity for collaboration between them, as well as reselling HPE products and services wrapped up in their own value-added offering.

Good morning from sunny Las Vegas. Following on from CEO Antonio Neri's keynote yesterday, today we're going to be hearing from CTO Fidelma Russo with a bit more of a deep dive into Tuesday's big announcements.

Delegates will soon be making their way to the keynote theatre to hear what CEO Antonio Neri has up his sleeve. As noted in our preview, we can expect to hear a lot about HPE GreenLake – described by Neri yesterday as the company's North Star.

It's already getting busy on the show floor, with some of the most prominent stands giving an inkling of the priority messages of the conference. There's also a lunar rover for reasons I'm hoping will become clear later.

With just 10 minutes to go before the keynote kicks off the auditorium is getting pretty full.

Lights down, roll VT, the Day One HPE Discover 2023 keynote is underway!

Neri has taken to the stage and is thanking all attendees both on site and those watching remotely. 

"Today, believe it or not, is my sixth keynote on this stage" says Neri. He reminisces on how much things have changed in that time for better and worse.

Since the acquisition of Aruba, the company has tripled the size of its networking business, Neri says.

Customer retention rate for GreenLake – the core of HPE's business and Neri's flagship project, is 98%

Neri is now talking about the company's Cray acquisition and the power of HPC, as well as AI. There's a "transformational" announcement coming up on this topic during the keynote – but first, a video break.

Time for some Intelligent Edge discussion. More than 50% of data is analysed and acted upon at the edge, says Neri. But 70% of data remains on premises and 'untransformed'.

Later this year you will see why this is the Year of AI at HPE says Neri. But still not yet... first he's talking about the importance of sustainability, particularly when it comes to data centers with initiatives like water cooling and renewable power sources. HPE is now one of only a handful of large companies with a certified net zero strategy for 2024.

We've got a hint of what's coming up. HPE has long called itself the "edge to cloud" company. This has rested on two pillars – edge and  hybrid cloud. But a third element had appeared, ghosted, next to these and that is AI. So a crucial part of the businesses forward strategy, or going to be, so it would seem.

Neri says that HPE was ahead of its time with its dedication to edge computing. It's been bolstered by the acquisition of Aruba Networks and Silverpeak. He's also talking up AIOps, which is deliverred through HPE Aruba Networks.

"We continue to expand our portfolio" says Neri, with Axis being a recent acquisition that adds security to networking and the edge. Another acquisition has brought in private 5G offerings.

All of this is, naturally, available to customers through GreenLake platform. 

Kerrell Hurt, global head of strategy, engagement and platforms at IHG has joined Neri. The company has been using edge services delivered through GreenLake for managing some of its services.

Hurt says the company wants guests to "fall in love" with IHG experiences and the IHG brand. They've been focusing on MLB – not major league baseball but Mobile, Loyalty and Booking.

Now they're going for NFL, which is about networking rather than the National Football Lague.

WiFi is now an essential in hotels. "Some people would rather have WiFi than running water" says Hurt, which raises a ripple of laughter from the audience. I think we all know it's truer than we would like to admit.

"Obviously we've been working with you around the edge with the Aruba portfolio" says Neri. Hurt says the two companies have a long term relationship and they "started dating" several years ago. They have HPE servers, "which are working great, by the way" and have been transitioning to GreenLake from traditional ownership.

All the networking is provided by Aruba. the partnership is "Inspired by IHG, but powered by HPE", says Hurt. It seems it's been a crucial part of IHG's succeess in generating revenue and returning business. And with that, Hurt is off the stage.

We're now onto the hybrid cloud segment. By Q2 2023, GreenLake platform had captured more than $10bn in total contract value since its launch.

Some stats to go with that.

Neri is now thanking partners. You can read more about yesterday's Partner Growth Summit here.

Neri is now talking about its most recent acquisition, OpsRamp. This acquisition only closed in May, but already it's being integrated into GreenLake, but will still remain available as a separate SaaS offering. 

"Ultimately, no hybrid cloud is complete without integration with the public cloud." Earlier this morning, HPE annoucned further partnering with Amazon Web Services (AWS).

Unsurprisingly, AWS believes that all (or almost all) workloads will end up in the public cloud, but there are some hurdles to this. Things like data sovereignty, data location and latency. So AWS has started stretching toward private cloud, such as virtual private cloud and EKS Anywhere, which lets customers run kubernetes clusters on premises.

This parnership inclused AWS EKS for public cloud, elements of EC2, and HPE services available on AWS marketplace.

Announcement: GreenLake for Private Cloud Enterprise (PCE) has been extended to Private Cloud Enterprise Business Edition, which is a more integrated solution, quicker to spin up, easier to deploy.

Also, new integration with Red Hat Open Shift, plus additional Azure features.

Announcement: VMware vCloud is also now available through GreenLake platform. 

VMware CEO Rangarajan Raghuram is on stage talking up this parntership, which makes VMware vCloud Foundation available on demand through GreenLake Platform. Neri says it will be a "winning combination" of VMware and HPE GreenLake's joint market leadership.

Announcement: HPE is partnering with colocation provider Equinix for GreenLake private cloud hosted in Equinix data centers.

Neri is joined by Jon Lin, EVP and GM of data center services at Equinix.

HPE and Equinix have been partners for a while, but the pre-provisioned GreenLake Private Cloud Enterprise and GreenLake Private Cloud Enterprise Business Edition in Equinix data centers is new.

"Pre-provioning makes it so much faster and easier" says Lin. He also talks up the ability of using a colocation provider, such as Equinix, can help customers solve sustainability questions.

This is in seven locations initially, including London and Frankfurt, but there's a hope it will expand beyond that in future. Neri jokes that hopefully they will go from seven to 71 soon.

Ok, finally we are at the AI pillar. It will be the most disruptive technology in human history, says Neri, and 2023 will be a historic year – "an inflection point". It creates "superpowers for your business", he says. It will fuel massive changes.

"We believe AI should be accessible to all" says Neri.

"This is the big announcement: HPE is entering the AI public cloud market".

HPE GreenLake for Large Language Models (LLMs) is the name of the product. The company has partnered with Aleph Alpha to provide some pretrained models in five languages. This is also a big cost saving – GreenLake for LLMs runs on HPC, which would normally cost millions to invest in both from the perspective of hardware and skills.

It uses ProLiant Gen 11, with Nvidia GPUs. And we have a new tagline for the company: "I call this edge to exascale", says Neri.

It will run on the most sustainable colocation facilities, says Neri. Sustainability is a key thread in this event, even if it's not the biggest news.

You can read more about the GreenLake for LLMs announcement here.

Jonas Andrulis, founder and CEO of Aleph Alpha has joined Neri on stage to talk about his company's involvement with GreenLake for LLMs.

"What a time to be alive" says Andrulis. He talks up the potential for generative AI, which is what aleph Alpha specialises in. "This isn't the end. We're in the middle of [a revolution]," he says.

AI is "the best co-worker you've ever had" says Andrulis, adding "AI is not the same as our (human) intelligence, but it's very similar and that's going to change the world."

Andrulis is talking now about the importance of thinking about what the AI revolution and speed of change means for humanity. Technology has advanced over all human history and we've learned to work alongside the machines. But the pace of change now means the question is very urgent and it is unsolved.

He points to the case of the lawyer who used Chat-GPT in a court case, which created a letter referencing fictitious past cases, as an example of how people misunderstand what AI is all about. 

The tech community, and the AI community in particular, is building the future, says Andrulis. We need to think about what we're building because for future generations AI will be ubiquitous. 

This is the fastest technological revolution that has ever happened, and it can allow us to build a world "free of the burden of stupid work" and solve demographic, environmental and other critical problems facing the world today, says Andrulis.

Neri says he hopes this has isparked ideas for delegates. "We believe HPE GreenLake for LLMs will enable enterprises to create unique advantages from AI", he says.

"It's an honor to have you here this week for what I believe is the best HPE Discover in the company's history" he says. "HPE has been at the forefront of every major technology advancement over the last 80 years and we are very proud that our leadership brings new groundbreaking innovations."

"HPE stands at the forefront of a new era where Edge, hybrid cloud and AI will come together to advance the way people live and work. Let's do it together," he says. And with that, the keynote is over. 

Thank you for reading. You can read my analysis of today's biggest announcement, HPE GreenLake for LLMs, here and please do stay with ITPro for further coverage from the event.

It is a beautiful morning here in Las Vegas at the Venetian Expo Center, formerly the Sands Expo. Particularly keen delegates (such as your intrepid reporter) are slowly starting to filter into the building.

This was, of course, yesterday's big news. You can read my analysis of GreenLake for Large Language Models for some additional insight on the announcement.

Delegates are starting to file into the auditorium ahead of the Day Two keynote with CTO Fidelma Russo set to take to the stage in about 10 minutes time. In the meantime we're being treated to some live rock music.

Lights, camera, action! Fidelma Russo is now on the stage to talk to us about "unlocking innovation through HPE GreenLake Platform"

She once again talks up the "inflection point" 2023 marks for HPE, with an emphasis on AI, becoming vendor neutral and HPC.

She's now talking about how the expected surge to public cloud never agtually happened, and hybrid became the go to model. HPE, though, considers that often people went "hybrid by accident" – some data was in a hyperscaler, some with a colo or maybe on premises, but there wasn't an underlying strategy.

HPE is promoting "hybrid by design" – a more planned strategy that is, naturally, underpinned and powered by HPE GreenLake.

She's now talking about edge, which has been a key proposition of HPE for over five years – even prior to Antonio Neri and the birth of GreenLake. "50% of data is now analysed at the edge. I don't think any of us would have predicted this a number of years ago," she says.

She's  talking now to a representative from Henkel – a company you may not have hears of but it's the parent of brands such as Persil and Schwarzkopf. They're rolling our Aruba and Aruba Central and are about halfway through that. About 6 months ago they started working with HPE looking at GreenLake.

He says that the organization has been moving out of managed services but "not out of as a service", which is why GreenLake appealed. The existing experience of Aruba Central also helped sell it to them, and the "single pane of glass" for management that it delivers for their hybrid cloud (they use both hyperscalers and private cloud).

Russo asks what Henkel wants to see from HPE in the future. He says carry on being responsive and that they're happy to be a test bed for new ideas as they have a highly distributed network. And with that, he's off

Now Russo is talking about what is meant by a :hybrid cloud experience.

"Hybrid cloud is not a destination," she says, "it's an experience." This is a segue for talking about HPE GreenLake for Private Cloud Enterprise, launched last year, and HPE GreenLake for Private Cloud Enterprise Business Edition, which was launched yesterday.

GreenLake for Public Cloud Enterprise Business Edition (PCEBE) "Is self managed by you or you can have a partner managers. It provides a unified child experience for virtual machines, either on prem or in the public cloud with a consistent experience and we start with Amazon Web Services, and soon to be expanded to other clients," says Russo.

"For ease of operation, we provide a single fake multi system deployment capability. So you don't have to go click click click 1000s of times. We provide a single click multi site upgrade capability, allowing for fully automated Lifecycle Management."

We now have Denis Vilfort, director of the office of the CTO at HPE, and Katelyn Stone giving a live demo of Aruba Centrral and HPE GreenLake PCEBE

Squeaky bum time as they start spinning up VMs live on both a private cloud and in AWS. Hoping that the network will hold for this, it's always a risk!

Congratulations to them both, it went without a hiccup. They even got a round of applause for their efforts!

Russo is back, touching on the Equinix partnership for Private Cloud Enterprise and Private Cloud Enterprise Business Edition announced yesterday.

We're now talking about GreenLake Platform and how it makes management of the full technology stack. Helping solve complexity, she says, is why the company acquired OpsRamp, which has already been integrated into GreenLake to make it available on a SaaS basis, as announced yesterday.

Russo says she'd like to reiterate how this is transforming the GreenLake offering. It's now "vendor neutral, neutral mode, multi tenant, and multi tier and AI driven", she says.

This is what OpsRamp looks like. You can see, as Vilfort points out, that it's truly vendor neutral as you can see instances from Dell and NetApp in there.

Russo is now talking up the idea of 2023 being "The year of AI" for HPE. She points out that HPE has been working on both HPC and AI for a while, such as through the acquisition of Cray and Pachyderm.

She's talking about HPE for Large Language Models, yesterday's big announcement, and how it opens up supercomputers to everyone on an as a service basis. This overcomes the problem of people wanting access to HPC services but just not having the budget for it.

There's also a sustainability play here. AI is energy intensive, particularly things like large language model training and then running the LLMs after that. Just training can use as much energy as 30 houses. HPE's facilities are being built sustainably, she says, with things like water cooling (in which they are experts) with the waste heat being used to heat greenhouses, for example. 

"GreenLake Cloud Platform is one of the most sustainable platforms on which to run your LLM," she says.

You'd better believe Vilfort is back for a demo of the energy use dashboard.

Russo is back. "You've seen how sustainability is foundational to our business model," she says. 

Russo is wrapping up by reiterating the key message of HPE's strategy: "HPE is the only company which has a single edge to cloud platform, that is vendor neutral, cloud neutral, delivering state of the art cloud services making AI accessible to all."

And that's it, the end of the CTO keynote, but not the end of our coverage. Keep an eye out for more news and analysis of the event in the coming days.

The EU has progressed a key bill seeking to regulate the use of artificial intelligence (AI) to its latter stages, in another move that outpaces the UK’s AI efforts.

On 14 June, MEPs held a pivotal vote to move the AI Act closer to being signed into law across the region, which was passed by an overwhelming majority.

The Artificial Intelligence Act (‘AI Act’) is a wide-ranging series of controls for the implementation of AI technology, centered around a risk-based approach.

UK government officials have become more vocal on the issue of AI in recent weeks, but have not pushed forward any legislation to curb the use of the technology.

Under the EU’s measures, companies seeking to use AI would be required to provide transparency around data used to train models and to clearly label AI-generated content.

The draft bill sets out a framework for risk assessment of systems, with high-risk systems defined as those that pose a “high risk to the health and safety or fundamental rights of natural persons”.

Companies that develop these systems will be required to register them in an EU database for public oversight.

Those that do not comply with the requirements of the act could be hit with fines of up to 4% of their annual worldwide turnover, or €20 million ($21.6 million).

The Financial Times reported a senior British official as having described the EU’s regulation as “draconian”, and OpenAI’s CEO Sam Altman warned his company could leave the EU over the law before walking this threat back.

In contrast to the EU’s strict risk-based approach, the UK whitepaper is based on a framework of five ‘principles’ for AI:

• Safety, security, and robustness
• Appropriate transparency and explainability
• Fairness
• Accountability and governance
• Contestability and redress

It lays out no corresponding measures that could be taken against non-conforming companies, nor plans for statutory controls over AI.

The UK whitepaper specifically states that the government will not seek AI regulation on a statutory basis initially due to concerns that this could hinder innovation in the field.

Some in the industry have hailed this as a positive move for the UK AI landscape, as firms may be more willing to invest in the region if they believe it provides a better environment for profitable development.

“The key is to strike the balance between ensuring fairness and making AI possible to use and experiment with – which this pro-innovation approach supports,” said Iván de Prado Alonso, head of AI at Freepik Company.

“I expect a warm welcoming from technology vendors across the UK as the government encourages creating an AI-enabled nation.”

AI regulation: Clashing approaches to risk

As with the GDPR, firms seeking to operate within the EU will still need to abide by the AI Act, which could limit the extent to which AI firms feel the freedom that the UK government is at pains to provide.

Industry consultation indicated that small businesses could face great time and financial burdens by compliance requirements if regulators are “not proportionate and aligned in their regulation of AI”.

In place of initial legislation, existing regulators, such as Ofcom or the Digital Markets Unit, will be asked to implement controls in a way seen as proportionate.

Setting out the exact powers each regulator will have is likely to push the legislation well into the next parliament; the two years it has taken for the Competition and Markets Authority’s (CMA) Digital Markets Unit to get off the ground casts a shadow over these proposals.

The decentralized approach, based on an evolving framework and tending towards pro-innovation rather than caution, could also allow the UK to adapt to the changing state of the AI market more easily.

First proposed in April 2021, the AI Act has had to be rewritten many times to keep up with the developments in areas such as generative AI since its inception.

AI regulation: Banned technologies

The UK whitepaper acknowledged specific risks posed by generative AI and deepfakes, such as the potential for a criminal to generate false compromising images of an individual to damage their reputation, or for large language models (LLM) like ChatGPT to generate disinformation.

It did not go as far as providing risk guidelines with pre-defined uses or technologies in mind, and this could prove vital for its continued relevance as new technologies emerge.

The draft document for the AI Act, in comparison, includes a list of technologies deemed to carry an unacceptable risk, such as systems that could subliminally influence individuals or the use of AI for real-time biometrics tracking.

In this regard, it presents more of a complete overview for AI regulation than the UK whitepaper, which businesses can use to inform their business decisions going forward. 

The UK’s approach could give AI developers more freedom to try systems out; it has also left firms guessing when it comes to what regulations they may have to follow down the line. 

While outright bans on some uses of AI may be seen as restrictive, this is an example of the EU having delivered a clear answer to the ethical and regulatory questions that are being asked today while the UK is still in the consultation stage.

The conservative EPP Group sought to quash the biometrics ban through a last-minute amendment to the bill but failed to muster the votes necessary to have this passed. 

It remains to be seen whether the ban will stick, and the bill does reference “certain limited exceptions” for the technology that may leave the door open for use by EU nations.

Police use of real-time biometrics technology such as live facial recognition (LFR) has long been a point of controversy. 

In 2022 a University of Cambridge study called for the UK to ban the technology in public spaces, and cited instances of the Metropolitan Police Service (MPS) using LFR in which “minimum ethical and legal standards” were not met.

Human rights groups have strongly objected to the use of LFR in recent years, alleging that the technology will entrench racial biases and violate individual privacy to an unacceptable degree.

“With such a persistently inhospitable environment towards people fleeing wars and conflict or in search of a better life, it is vital that the European Parliament doesn’t dismiss the harms of racist AI systems,” said Mher Hakobyan, advocacy advisor on AI regulation at Amnesty International.

“Lawmakers must ban racist profiling and risk assessment systems, which label migrants and asylum seekers as ‘threats’; and forecasting technologies to predict border movements and deny people the right to asylum.”

A cross-party group of UK lawmakers called for CCTV devices from two Chinese companies to be banned in July 2022, due to alleged links with Uyghur internment camps and reported use of facial-recognition technology to detect Uyghur individuals.

During a trip to the US in June, UK Prime Minister Rishi Sunak attempted to distance the UK’s approach to AI from that of its closest neighbors.

Sunak has voiced ambitions for the UK to become a global leader in AI, and the newly announced Atlantic Declaration will see the UK and US work closely on AI safety.

The UK is also set to host the first international summit on AI safety later this year.

AI regulation: Common ground on sandboxes

Both the EU and UK approaches favor the adoption of trial environments, or ‘sandboxes’, for AI testing and deployment.

The UK whitepaper committed to providing businesses with a regulatory sandbox for AI, following a recommendation by Sir Patrick Vallance, a measure the EU has also promoted.

It is hoped that this will allow AI products and services to reach market faster, and highlight any hurdles to innovation, or emerging tools that deserve greater regulatory attention.

EU digital technology industry organization Digital Europe published a report based on results from nine European startups and SMEs. 

A key finding was that respondents supported sandboxes, and would favor a continuous sandboxing process in which to test products.

Despite bringing one of the most significant changes to the software industry in years, professionals say that code-writing generative AI tools are not substantially changing the software development lifecycle.

Nearly all developers (92%) have embraced AI code assistants in just a few years since their launch, but these tools haven’t overhauled operations within development teams, according to results from GitHub’s The developer wishlist survey.

The effect of products such as GitHub Copilot have led to greater efficiencies within existing workflows, such as continuous integration and continuous delivery (CI/CD), rather than changing them so drastically that entirely new workflows were required.

This has led to a quicker development process, spending less time writing code and freeing up developers’ time to work on devising solutions for more complex issues.

Developers stated that AI-based code generation tools helped improve the quality of their code, reduce incidents at a production level, and boost their overall code output.

The conclusions of the survey were informed by responses from 500 developers across the US in March 2023.

Approximately one-third of surveyed developers indicated that their managers attribute the volume of code they generated to their employee performance, with the same proportion indicating that they don’t expect this to change with the adoption of AI coding tools.

Despite this, the majority of respondents said that their code quality (40%) and time taken to complete tasks (34%) are the primary metrics used to measure their performance. Both metrics are ones with which AI-based coding tools can help.

Over a third of those surveyed suggested that collaboration and communication should be a metric that employers use.

A majority of respondents (81%) stated that AI coding tools would improve the degree to which they collaborate with their colleagues, and AI tools were identified as useful for collaborative tasks such as code reviews and pair programming.

The prevalence and encouragement of collaborative tests and safety checks enabled by AI could calm fears that tools such as GitHub Copilot can lead to increased code vulnerabilities.

Apple has reportedly banned its employees from using code-generating tools such as GitHub Copilot, ChatGPT, or Bard over data usage concerns. But data suggests that these tools continue to be used in workplaces for the majority of the time.

Only 6% of developers exclusively use AI tools exclusively outside of work, and researchers stated that the number of employees who do not use AI at some point in their working week will continue to dwindle as time goes on.

A less-visible way in which developers indicated that AI tools improved their time spent working was by reducing burnout. 41% of those surveyed said that AI coding tools can help prevent burnout from taking place.

This followed a previous survey run by GitHub in 2022, in which 87% of surveyed developers stated that the AI code generation tool GitHub Copilot helped them to conserve focus and avoid mental strain.

The UK and US have announced a new joint ‘Atlantic Declaration’ - a wide-ranging series of agreements and measures to further unite the two countries’ economies and tech sectors closer together.

Both governments have now reached an agreement in principle over a ‘data bridge’ between the UK and the US that would allow 55,000 UK businesses to transfer data to the US more freely.

At present, smaller UK firms often rely on an Alternative Transfer Mechanism to transfer data to the US, which comes with extra steps such as transfer impact assessments that can be costly and time-consuming. 

UK government figures estimate that the cost savings from the data bridge could top £92 million ($115 million) per year.

Questions remain over the precise details of the ‘data bridge’, such as what powers it would grant US security services over UK data.

The EU-US Privacy Shield, which had allowed US firms to receive EU data despite being outside the remit of the GDPR, was ruled invalid in 2020 over concerns that it could facilitate US surveillance of EU resident data.

The announcement comes after more than two years of negotiations, and once a final agreement is reached UK firms could transfer 

President Biden is expected to request the UK be designated a ‘domestic source’ under the US Defense Production Act (DPA), which could open the door to UK firms receiving US subsidies across a number of industries.

The DPA allows the President to direct the private sector to prioritize the production of materials, and supply of services, that serve the interests of US national security.

Outside of direct defense contracts, the act has been used to bolster the supply chains of printed circuit boards, domestic renewables, and hypersonic engine technology.

Working together on emerging technologies

Another key announcement from the White House press conference was that the UK and US will work more closely on the regulation of artificial intelligence (AI).

In the interest of advancing this partnership in research, development, and regulation the UK will host an international summit on AI safety later this year.

This will bring together strategic partners from across the world to evaluate the risks posed by AI, though the exact details on which countries will attend have not yet been set out.

Data processing firm Palantir has also announced it will open its new European HQ for AI development in the UK, in an expansion of its 800-strong UK workforce.

It is closely -involved with the UK Ministry of Defence (MoD), with the two having announced a £75 million ($94 million) digital transformation deal in December on top of a long-standing partnership with the Royal Navy.

“We are proud to extend our partnership with the United Kingdom, where we employ nearly a quarter of our global workforce,” said Alexander Karp, CEO at Palantir.

“London is a magnet for the best software engineering talent in the world, and it is the natural choice as the hub for our European efforts to develop the most effective and ethical artificial intelligence software solutions available.”

Palantir’s critics have argued against the company establishing a greater presence in the UK, citing events such as the link between one of its employees and Cambridge Analytica as reported in The Guardian.

Its decision to hire the former head of NHSX AI was called “scandalous” in 2022, amidst a push by the company to increase its UK presence.

Some have criticized the lack of detail in the government announcement, pointing to the prevalence of government spokespeople and industry spokespeople.

“This press release for the UK AI Safety Summit features DeepMind, Anthropic, Palantir, Microsoft, and Faculty, and not a single voice from civil society or academia, and no one with lived experience of algorithmic harms,” tweeted technology strategist Rachel Coldicutt.

Other technologies identified as important to the future of UK-US relations in the declaration included 5G and 6G, semiconductors, and quantum.

The UK semiconductor supply chain is still reliant on international imports, though the government has reportedly looked at providing chip subsidies through existing mechanisms such as the National Security Strategic Investment Fund.

Across the United States, semiconductor investment has seen rapid improvement following the signing of the CHIPS Act, through which some $280 billion in subsidies for semiconductor research and manufacturing was made available.

Closer ties to the US could provide the UK with a more stable supply chain amidst the US-China chip war, though a clear strategy has not yet been published.

In all, UK trade with the US is worth some £279 billion ($350 billion) per year.

Data security provider Cohesity has expanded its partnership with Google Cloud and unveiled Cohesity Turing, a new set of AI technologies that integrate into its multi-cloud data platform. 

Speaking at its Cohesity Catalyst virtual conference, the firm said the development will enable organizations to leverage their entire data estate through a single, secure workflow that covers on-premises, multi-cloud, and edge environments.

The expanded partnership adds integration between Cohesity Data Cloud, Cohesity’s AI-ready data security and management platform, and Vertex AI, Google Cloud’s fully managed machine learning (ML) platform that helps businesses accelerate the deployment of ML and AI models.

By combining its own capabilities with Vertex AI, customers can gain new insights into the same data they are already securing and managing on the Cohesity platform, the firm said. 

Customers could, for example, quickly search across large swathes of data to discover threats, answer specific questions, or quickly recover data using contextual searches.

“Vertex AI is one of the best platforms for building, deploying, managing, and scaling ML models - and we’re excited that Cohesity is joining our growing open ecosystem to help more customers get value from their data via AI,” said Thomas Kurian, CEO at Google Cloud. 

“Cohesity’s excellent data security and management capabilities, combined with Google Cloud’s powerful generative AI and analytics capabilities, will help customers get exceptional insights into their backup and archived data.”

What is Cohesity Turing?

Essentially, Cohesity Turing is a collection of AI/ML tools and technologies designed to power deeper insights for customers. 

Currently, users can use it for ransomware anomaly detection through advanced modelling, advanced threat intelligence, data classification, as well as predictive capacity planning with machine-driven recommendations.

Cohesity said it plans to continue expanding the offering’s capabilities as new AI needs emerge, while also promoting its responsible and safe implementation.

Coming soon, customers will also be able to gain further benefits from Retrieval Augmented Generation (RAG) AI model workflows, which are designed to derive deeper insights or quickly find content in petabytes of data.

In an announcement, Cohesity president and CEO Sanjay Poonen said that to gain the transformative benefits from generative AI, businesses require rapid insights from their data utilizing cutting-edge and leading AI/ML models. 

“We are excited to be leading the charge in generative AI in the data security and management industry, as we build out next-gen generative AI solutions for our category,” he said.

“We also agree with Google that AI must be handled securely and responsibly. With our unique platform, Cohesity not only provides phenomenal search via our built-in indexing capabilities, but robust security protocols to help customers maintain control and privacy of their data at every turn.”

Expanded integrations

Speaking at the Catalyst event, Cohesity also announced an expansion of the Data Security Alliance. 

Netskope, ServiceNow, and Zscaler are now part of the group, which was set up by Cohesity back in November 2022 to help businesses better defend against cyber attacks through vendor collaboration.

They join the existing pool of members, which includes BigID, Cisco, CyberArk, Mandiant, Okta, Palo Alto Networks, PwC UK, Qualys, Securonix, and Splunk.

“With the addition of new security partners Netskope, ServiceNow, and Zscaler and our growing portfolio of security integrations, we have one of the most robust data security ecosystems in the industry to help fight the threat of attacks,” Poonen said.

Dell Technologies and Nvidia have jointly announced the Project Helix initiative to help businesses create and deploy their own on-premise generative AI models.

Both companies will work together to provide “a series of solutions” aimed at enterprises to help use their proprietary data to enhance the value of AI models, all while protecting their data privacy.

Assistance provided through the new offering will span the complete AI journey, from modeling to deployment and fine-tuning results, with Dell providing optimized hardware and software to support this at every stage.

Nvidia’s tested AI Enterprise software will come included with Project Helix, including its more than 100 development tools, frameworks, and pre-trained models.

“This is a full-stack solution that enables enterprises to create and run custom AI models built with their knowledge of their business,” said Chuck Whitten, co-COO at Dell Technologies.

“Our powerful infrastructure and software, Nvidia’s accelerators, AI software, and expertise; this is unmatched, second to none in the industry.”

Whitten pointed to existing Dell hardware that can tackle heavy generative AI workloads such as the PowerEdge XE9680 server. 

This contains eight of Nvidia’s H100 GPUs which comprise the core infrastructure on top of which customers can make use of Dell’s server and storage software.

The pair will work to promote on-prem AI, in contrast to the current dominance of public cloud and hyperscaler dominance in AI.

In recent months, Microsoft and Google have established market leadership with their respective chatbots and AI productivity tools.

“Enterprises are working to deploy these models and workloads on-prem to make full use of their data,” said Jensen Huang, CEO at Nvidia. 

“If you think about how they drive better insight and better outcomes from their business, they're looking at ‘I need the context of my business with this data, with these models and tools to really drive faster insights to my business’. They have to do it on-prem because that's where their data is.

“And they have to do it close to the edge because that’s the speed of light, and you want it to respond instantaneously. But you also want it to be at the edge because, in the future, you want to have information from multiple modalities. 

“The more contextual information we get, the better grounded and better inference we can make.”

The announcement was made during the day-two keynote at Dell Technologies World 2023 in Las Vegas, after having been heavily teased by Dell CEO Michael Dell throughout the events of the first day.

In a discussion with ITPro, John Roese, global chief technology officer at Dell Technologies, said that the company is in a prime position to advise customers on how to navigate AI uptake, and emphasized that Helix’s turnkey structure lets customers deploy tools with built-in safety.

“Many larger and midmarket companies will want to run generative AI in their own on-premises data centers, to ensure security for their proprietary data,” Laurie McCabe, cofounder and partner at SMB Group Inc., told ITPro.

“Project Helix gives them a system that’s optimized for this, so they don’t have to piece everything together – helping them to take advantage of generative AI more quickly and efficiently.”

Flexibility has been a core tenet throughout the conference, with cloud choice having been a key factor in Dell Technologies’ massive Apex expansion, and this approach applies to Project Helix too.

“There will be a lot of flexibility for customers that are looking to figure out what models to use,” said Varun Chhabra, SVP ISG and telecom marketing at Dell Technologies.

“Helix will come with foundational models that are provided and developed by Nvidia, if customers want to use that. Customers will also have the choice flexibility to be able to pick an open-source model of their own choice.

“Once these foundational models have been identified by the customer Helix will help them tune that model with their own proprietary, high-value data sets.”

Chhabra noted that the majority of customers will start with a foundational model, whether this is one included as part of Helix or an open-source model, but that the initiative will provide the flexibility for either to be easily trained on a business’ data.

Dell has stated that its Validated Designs based on Project Helix will become available in July 2023.

Red Hat CEO and president Matt Hicks has said enterprises operating in the current business environment shouldn’t have to adopt an either-or approach toward keeping the lights on and driving innovation. 

The message was delivered during the opening keynote of the open source leader's annual conference in Boston on Tuesday.

Hicks used his introductory session to re-energize conference delegates, acknowledging their key challenges and pain points, while talking about how Red Hat has responded and continues to work for its customers. 

“AI has moved from the obscurity of academia to the ubiquity of ChatGPT. But it’s also moved from the power of a few to the power of many,” he said. 

“We are watching the impact and potential of open source that played out all those years ago - with operating systems and programming languages - now playing out with AI. 

We’re watching creativity on a global scale make its way through AI to an open source revolution. It’s the time to dream about the potential for your business, your industry and to be the driver of change for the better.”

Many times in history, people have seen the potential of different technologies and acted on them, helping them become what they are today, according to Hicks. 

AI, in particular, he said has “moved from the obscurity of academia to the ubiquity of ChatGPT. It’s also moved from the power of a few to the power of many.”

While Hicks clearly sees much potential for enterprise in AI, he was also keen to communicate how the technology is still in its infancy and highlight the barriers businesses continue to face. 

“As excited as I am to be here on this stage in this moment, I also deeply appreciate the realities that can get in the way of realizing that journey,” he said. 

"The realities that jolt us awake and hold us back is what we are going to focus this year’s summit on - how to manage those realities. Because if we can manage the realities that we’re living in today, the potential of AI gives us the license to dream."

Hicks promised a slew of announcements from Red Hat over the next few days as the company continued to marry methodology and technology to help turn company visions into realities. 

Some of the big unveils from today include:

• The launch of the Red Hat Developer Hub, designed to turbocharge developer productivity.
• New and enhanced management capabilities for Red Hat Enterprise Linux (RHEL) offered as part of Insights.
• Ansible Lightspeed, which focuses on AI-driven automation to ensure organization-wide accessibility across myriad skill levels. 
• Event-driven Ansible to put automation front and center to derive the most from existing resources and investments.
• Catapulting Kubernetes-native security features into a Red Hat-managed offering in the form of Red Hat Advanced Cluster Security Cloud Service. 
• A focus on supply chain vulnerability detection and management in the form of the Red Hat Trusted Software Supply Chain solution.

“Today we’re going to focus on the reality of needing to do more with less. These challenges won’t be a surprise to you because you all are living them every day,” Hicks added. 

“We’ll help you manage the pressure of making the most of what you have while at the same time cultivating the skills you need, and the pressure of finding new ways to deliver more, faster. 

“If there’s one thread to remember from today it’s that doing more with less doesn’t have to be a constraint. It’s a reality to manage today to unlock your innovation in this unique moment. Today, you manage your realities, tomorrow you’ll thrive with them.”

Microsoft has unveiled a raft of new products “built for the era of AI” at its annual developer Build conference. 

Microsoft Fabric, which is now in preview, was among the key announcements from the conference. 

The tech giant said the new product aims to deliver an “integrated and simplified experience” for analytics workloads and users that will greatly support developer productivity.

“[Fabric] brings together Power BI, Data Factory, and the next generation of Synapse in a unified software as a service (SaaS) offering to give customers a price-effective and easy-to-manage modern analytics solution for the era of AI,” Microsoft said in a statement. 

“Fabric has experiences for all workloads and data professionals in one place – including data integration, data engineering, data warehousing, data science, real-time analytics, applied observability and business intelligence – to increase productivity like never before.”

Microsoft revealed it will integrate Copilot in Microsoft Fabric to further enable organizations to “accelerate value creation with their data”. 

Set to launch in preview soon, the integration will enable developers to harness natural language and a chat experience to generate code and queries. 

The platform will also allow for the creation of low/no-code AI plugins and enable custom Q&A. Microsoft added that this will be fully deployable on Microsoft Teams and Power BI. 

“With AI-driven insights, customers can focus on telling the right data story and let Copilot do the heavy lifting.”

“Persistent governance” capabilities

The launch of Microsoft Fabric will greatly enhance organizational visibility and governance of data, the company said. 

Organizational data is hosted on OneLake, Microsoft’s unified foundation, which it said will provide a “single source of truth” and will greatly reduce the need to extract, move, or replicate data. 

This aspect of the new solution will enable developers to eliminate “rogue data sprawl” and provide “persistent data governance” for organizations. 

“Fabric also enables persistent data governance and a single capacity pricing model that scales with growth, and it’s open at every layer with no proprietary lock-ins,” the firm said.

“Deep integrations with Microsoft 365, Teams and AI Copilot experiences accelerate and scale data value creation for everyone.”

Azure AI product rollouts

Azure was a key focus at Microsoft Build, with the company unveiling a series of AI-related updates and product roll-outs for the cloud service. 

Updates to the Azure OpenAI Service, which are now in preview, included enhancements that will enable organizations to streamline the integration of their own data sources into Azure, as well as simplify the integration of external data sources. 

Azure Cognitive Search will also include a new retrieval system for LLM apps, the company said. Exact launch dates are yet to be confirmed, however.

“Vector search allows developers to easily store, index, and search by concept in addition to using keywords, using organizational data including text, images, audio, video, and graphs,” Microsoft confirmed. 

“Developers will be able to build apps to generate personalized responses in natural language, deliver product recommendations, detect fraud, identify data patterns and more.”

Document and conversation summarization is also set to receive a boost through the Azure Cognitive Service function. A raft of new capabilities unveiled by Microsoft includes the ability for developers to customize summarization. 

“Coming soon to general availability is ready-to-use document and conversation Summarization,” Microsoft said. “This will allow customers to deploy use cases in production while backed by Azure’s enterprise-grade readiness, including Azure Service Level Agreements, data security, and more.”

These features will be powered by the Azure OpenAI Service and will allow users to interactively customize language skills across applications, leading to “faster time-to-value for organizations looking to use LLMs”.

Businesses using ExtraHop’s Reveal(x) 360 can now gain visibility into employees’ use of generative AI tools, thanks to the latest update to the network detection and response (NDR) platform.

The cyber security provider said the functionality will better protect organizations against accidental misuse of AI tools, such as OpenAI ChatGPT, helping them to better understand their risk exposure and whether AI tools are being used in compliance with AI policies.

The move comes at a time when generative AI and AI as a service (AIaaS) tools are being increasingly adopted across the enterprise in a bid to boost productivity. 

However, despite its benefits, ExtraHop CEO Patrick Dennis said customers have “expressed a real concern” about employees sending proprietary data and other sensitive information into AI services.

“Until today, there has been no good way to assess the scope of this problem,” he said. “Amid the proliferation of AIaaS, it’s extremely important that we give customers the tools they need to see what is happening across the network, what data is being shared, and what could be at risk. 

“With this new capability, our goal is to ensure that they can reap the wide-ranging benefits of generative AI while still maintaining data protections.”

AIaaS and generative AI tools have skyrocketed in recent times as users get to grips with the new technology. 

Since its release in November last year, OpenAI’s ChatGPT alone has gone on to amass more than 1 billion users. 

The key issues for organizations that implement these new AIaaS tools revolve around data leaks and intellectual property (IP) risk as employees input potentially sensitive and confidential information into them.

Once proprietary information has been shared, the service will continue to use that data to process future requests from other users, with the AI being unlikely to understand the effects of re-using the data to which it has access.

Reveal(x)’s new functionality works by providing organizations with visibility into the devices and users on their network that are connecting to external AIaaS domains, as well as information on how much data is being shared with these services. 

In some cases, the offering can also detail the type of data and individual files that are being shared.

The platform can do this through its use of network packets as the primary data source for monitoring and analysis, the firm said. 

By using a real-time processor, it turns unstructured packets into structured wire data and analyzes payloads and content from OSI Layer 2-7 for complete network visibility. 

“ExtraHop believes the productivity benefits of these tools outweigh the data exposure risks, provided organizations understand how these services will use their data (and how long they’ll retain it),” the company said in a blog post.

“And provided organizations not only implement policies governing use of these services but also have a control like Reveal(x) in place that allows them to assess policy compliance and spot risks in real time.”

Dell’s second in-person conference since the COVID-19 pandemic sees the industry mainstay ditch the riverboat faćades of Las Vegas’ Venetian for the tropical, South Seas-themed Mandalay Bay. 

Far from just being superficial, the move to a larger venue is fullling anticipation that Dell Technologies World 2023 will be its biggest yet, both in terms of size and the scale of the company’s strategic ambitions.

Multi-cloud as a service, the future of work, modern data infrastructure, edge computing, and cyber security are among the core themes, as the firm hopes to move on from recent layoffs and reaffirm its overall vision.

All eyes on Project Alpine

Updates to Dell Apex will without a doubt consume a lot of bandwidth, due to its broad portfolio spanning multi-cloud, storage, and security. But we also expect to hear more on Project Alpine, which the firm first teased last year.

Dell seeks to bring block, file, and object storage under one roof, regardless of the hyperscaler that customers use. Indeed, cloud has never been more important, and if Dell can offer customers more consistency, they could be onto a winner.

Its acquisition of cloud orchestration startup Cloudify in January could also fit into this vision for a seamless cloud experience.

Gartner has also identified edge architectures as a key trend influencing the future of cloud, with Dell vying to position itself to meet this demand.

Setting the pace on AI

Customers and partners will also be excited to see how Dell handles the rise of artificial intelligence (AI) for business, and how innovations in this space fit into its broader strategy.

It’s more or less impossible that generative AI won’t get a look-in. And indeed, CEO Michael Dell will touch on how Dell can “create a better future for all through the powerful combination of technology and human creativity”. 

The day-two keynote – ‘Embracing new potential’ – will explicitly focus on AI, and it’s likey Dell will lay out how companies can adopt these systems more effectively, in areas such as improving data insights. This is opposed to the glitzy appeal of chatbots.

Dell announced an AI initiative with Nvidia in January which fits this mold exactly, with a slew of its next-generation Dell PowerEdge servers optimized for AI and analytics work alongside Nvidia’s H100 AI chips. The firm has also promised ‘intelligent’ services across the breadth of its services, and this also seems like prime real estate for competitive, valuable AI.

Generative AI could change security among other fields, and if Dell can capitalize on this by introducing quality-of-life features such as generative summarisation of dense data at the edge, it could help to cement its place in the IT stacks of existing customers.

Dell has room to maneuver, with a customer base that would certainly benefit from a means to adopt AI seamlessly, without the need the train training large datasets.

Sustainability as a core tenet

The words “Dell Datastore” are likely to be on the lips of keynote speakers too. Championed at last year’s conference, Dell’s fastest-ever storage option is the kind of product ripe for new upgrades when it comes to energy efficiency.

Anxieties around business energy bills surged last year, and with businesses set to bet on sustainability as the key to long-term stability, it would be remiss of Dell to not properly address sustainability and wider ESG implementation in its announcements.

That shouldn’t just mean inviting prominent figures like film director James Cameron to deliver a “trailblazer” talk. Dell’s designated an entire ‘sustainability journey’ for attendees, but it remains to be seen whether the actual substance matches the theme’s prominence.

Like with most major tech conferences in 2023, the elephant in the room at Dell Technologies World will be layoffs. The firm outlined plans in February to cut 6,650 roles worldwide, equivalent to 5% of its total workforce, after it sustained particularly poor device sales. We’ll certainly be hoping to drill down into how Dell plans to address these anxieties.

ITPro will be covering Dell Technologies World 2023 between 22 May and 25 May. Stay up-to-date with the latest news and updates by following our live blogs, and subscribe to our newsletter to catch the latest in-depth stories.

Appian has announced plans to increase natural language input across the platform, as well as new features for the automatic digitization of contracts.

With the implementation of pre-trained large language models (LLM) into its Appian Platform, the firm showed off new automated analytics solutions that will soon become available to customers.

Based on simple user inputs, and leveraging its integration with Appian’s Data Fabric, the system is capable of auto-generating prompts for data retrieval and generating complex data visualizations from plain-English sentences.

Michael Beckley, co-founder at Appian, demonstrated some of the new AI capabilities during a keynote at this week’s Appian World conference.

In one example, Beckley was able to produce a visual ESG breakdown with the phrase ‘show me the average waste emissions by utility bill’.

Appian also announced its new Contract Writing solution which is aimed at assisting public sector agencies by automation the process of drafting contracts.

Through a pre-recorded video, Beckley also demonstrated a warranty claim form being digitized using a few natural language prompts. 

Appian’s AI was capable of creating a digital copy with both optional and required fields, drop-down boxes and date-pickers, as well as company branding as described in the original prompt (in this case, green title text).

The time from upload to having a digitized contract was around 30 seconds in this example.

“Compared to the many hours this would probably take you, generally speaking, we think it’s a pretty great productivity booster,” said Beckley.

“These large language models provide a base level of natural language understanding, which is great,” Malcolm Ross, SVP of product strategy at Appian, told ITPro.

“But the value for us is to pair that with the context of the Appian software, and to have that natural language understanding translate into building an application or building something inside a product.”

A key example of this was shown in Appian Knowledge Base chatbot, which can return internal knowledge based on natural language inputs.

The prompt ‘What are employers subject to when fail to pay’, for example, returned the relevant legal information based on the territory of the company and cited the specific document from which it pulled the information.

In the near future, customers will also be able to generate requests for information (RFIs) - the documentation needed to obtain information from suppliers on goods and services - with natural language prompts.

A live demonstration showed a user asking for an RFI with a specific response time, and the categories respondents should fill in.

Using the Appian knowledge base, the system can produce accurate and company-specific RFIs based on previous documents.

Having made Appian Contract Writing with federal agencies in mind, the firm stressed that Appian Cloud is compliant with Department of Defense (DoD) impact levels IL4 and IL5.

These pertain to information on critical infrastructure, highly confidential financial information, and unclassified but sensitive military data.

The firm is also aiming for greater integration of generative AI throughout its SAIL code environment. 

SAIL is the declarative expression language beneath the hood of Appian’s visual low-code design UX, and Beckley quoted a reddit thread in which developers bemoaned the inadequacy of ChatGPT for working in the language.

With Appian’s new announcements, developers can quickly build workflows using AI and seamlessly open these in the Appian process modeler and edit them in SAIL or using low-code elements.

Developers seeking to use SAIL primarily can also use the inbuilt LLM functions to directly generate SAIL code, or translate SAIL code into plaintext explanations.

Microsoft has announced a range of new features for its AI-powered Microsoft Bing and Edge copilots, including chat history and multimodal support.

Users will be able to pick up where they left off with new persistent chats in both Bing chat and the Edge sidebar, while a new export option for chat aims to enable more collaboration on output.

If a link is opened from a Bing Chat answer, the link can open in Edge while shifting the original Bing Chat into the browser’s side pane, allowing the user to simultaneously continue chatting while browsing.

The idea behind the new feature is that by making Bing Chat accessible within the Microsoft Edge browser, users could use the pairing as an effective research tool.

One piece of information gleaned from a web page can become a question sent to Bing Chat, which can then direct the user to new information - a process to repeat until the research task is fully completed.

Workers in specific sectors may also benefit from future updates Microsoft teased that aim to personalize Bing Chats, bringing context from previous sessions to new conversations.

Research tasks that are repeated often, for example, could become quicker to complete with smarter suggestions.

Elsewhere, Microsoft also unveiled improved visual features for Bing chat, including images for search results, charts, and graphs.

Bing Image Creator, the firm's response to other generative AI art generators such as Midjourney and Dall-E, now supports up to 100 input languages.

In time, it has said it will incorporate multimodal capabilities such as visual input for chat.

“We know from research that the human brain processes visual information about 60,000 times faster than text, making visual tools a critical way people search, create, and gain understanding,” said Yusuf Mehdi, corporate vice president and consumer chief marketing officer at Microsoft.

“Bing has always been known for its visual experiences including features like Knowledge Cards and visual search. And now we’re delivering those same experiences in chat.”

With productivity gains in mind, Microsoft has improved the tool's ability to summarize longer documents and enabled Edge to complete actions such as opening media content or specific web pages in response to chat input.

Edge Mobile will gain a similar feature, with new capabilities to summarize mobile web page content due to be released in the coming weeks.

To broaden the reach of Microsoft’s Bing and Edge AI assistant, third-party plugins will soon allow developers to integrate their apps or websites within the Bing chat experience.

Microsoft gave the example of a user asking a math question in Bing chat and receiving a complex visualization through an integration with the answer engine Wolfram Alpha.

Bing exceeded 100 million daily users in March, in large part due to the interest in the search engine’s new GPT-4-powered, generative AI capabilities.

Users can try out the features in open preview now, and Microsoft has stated that updates will regularly follow.

A new draft of EU artificial intelligence (AI) legislation could better protect business IP from being secretly scraped by AI firms, with developers facing new transparency obligations on copyrighted content.

The long-awaited AI Act could force developers to disclose when they collect and use copyrighted material to train large language models (LLMs). 

The aim is to protect firms from having information such as source code used without their permission.

In addition to protection from unauthorized uses of data, the bill would offer companies legal grounds to establish the degree to which AI firms they work with are using ethically sourced, non-copyrighted data.

This could save businesses from costly legal battles over the use of tools that, unbeknown to them, contain stolen intellectual property (IP). 

The bill is also expected to categorize AI models by their risk factor, from ‘minimal’ to ‘unacceptable’. 

It aims to provide clear criteria that organizations can use to assess whether an AI tool’s risks outweigh its use cases.

High-risk AI systems have been defined as those that present “significant risks to the health and safety or fundamental rights of persons”, such as live facial recognition, and will be subject to additional transparency obligations.

EU’s AI Act and wider industry data scraping

At present, many developers of large language models (LLMs), the algorithms behind generative AI, use a great deal of data harvested from the internet for training purposes. 

Lawmakers from across the EU’s political divide have come to a provisional agreement for the bill, which will now be pushed to the EU trilogue for further debate.

Previous drafts of the bill, first proposed in 2021, stated that transparency obligations “will not disproportionately affect the right to protection of intellectual property”.

The current bill states that non-compliant firms could face fines totaling up to 4% of their annual worldwide turnover, or €20 million ($22 million), whichever is higher.

Alistair Dent, chief strategy officer at data company Profusion, said that the requirement “raises the question of why AI should be treated differently from other platforms, such as social media or search engines”.

“These platforms index or use a huge amount of copyrighted material often without citation - should they be forced to adhere to the same standards as AI?” he added.

Examples of this can be found in historical examples of wide-scale data scraping. 

Earlier this year, Meta sued a ‘data scraping for hire’ firm Voyager Labs for its practices, alleging the firm had collected data on 600,000 Facebook users in a hidden campaign utilizing fake accounts.

Meta itself also attracted a €265 million ($291 million) fine for “unacceptable” data scraping from the Irish Data Protection Commission (DPC) in November 2022.

At present, firms face a confusing regulatory landscape over the permittance of data scraping. 

A case in the US last year, between hiQ Labs and LinkedIn, concluded that web scraping was not in violation of federal law.

However, the court was not convened to directly rule on the practice of web scraping, nor did it offer judgment on how the legality of the practice could be impacted by claims of IP theft.

The EU, in contrast, has protections against the use of data that could infringe on the rights of citizens such as for biometric identification, and has issued fines on the subject as seen with the Irish DPC’s decision on Meta.

Like GDPR, the EU’s AI Act is expected to have widespread implications for the market. 

Those firms looking to sell or deploy their models in the EU will have to comply with the terms of the bill, which will require a market-wide homogenized approach to risk and ethics criteria for AI products.

Researchers from the University of Oxford have created a tool called capAI, which they describe as “an independent, comparable, quantifiable, and accountable assessment of AI systems that conforms with the proposed AIA regulation”.

Within the paper, a proposal for internal review protocols is given as well as a suggestion that firms could provide stakeholders and customers with a scorecard for their AI system.

This could alleviate the concerns of some experts such as Dent.

"A risk-based approach, which seeks to categorize different uses of AI and then add rules based on the perceived 'risk' of the solution causing harm, has the drawback of being unable to anticipate how AI will develop and the impact new tools will have on society,” he stated.

“Put simply, if you can't know what form a new AI solution will take and how it will be used, it's very hard to predetermine what category it should go in and apply the compliance burden accordingly.

Recent earnings calls from Microsoft and Google have shown there is still a strong appetite for cloud computing services, despite warnings of a pending ‘cloud slowdown’ in recent months. 

In an earnings call for the quarter ending March 31, Microsoft recorded 27% growth at its Azure cloud division. While slower than the 31% recorded in the previous quarter, this exceeded analyst expectations, Microsoft said. 

Microsoft attributed part of this growth to the ongoing hype surrounding the integration of generative AI products within its core service offerings.

In an investor call, CEO Satya Nadella said that the company’s cloud segment was reaping the benefits of AI integration within products. 

He noted that the firm now boasted more than 2,500 customers for its Azure OpenAI service. 

Elsewhere, Google Cloud recorded its first operating profit in the history of the division in the previous quarter. The hyperscaler recorded $7.4 billion in revenue, amounting to a 2.5% profit. 

Is a ‘cloud slowdown’ still possible? 

These earnings - Google Cloud’s in particular - call into question recent claims that the global cloud market was bracing for a ‘slowdown’. 

Deteriorating economic conditions, rising costs, and concerns around energy consumption prompted suggestions that the cloud market was entering a period of sluggish growth earlier this year. 

Research from the Uptime Institute showed that hyperscalers such as AWS and Microsoft had recorded slower growth toward the end of 2022 amid challenging macroeconomic conditions. 

“The global macroeconomic environment - specifically, high energy costs together with inflation - is making organizations more cautious about spending money,” the report warned. 

“Cloud development projects are no different from many others and are likely to be postponed or deprioritized due to rising costs, skill shortages, and global uncertainty.”

But a landmark profit for Google Cloud and a quarter that exceeded expectations at Azure does not necessarily constitute a reversal of any slowdown. 

Compared to pandemic-era highs, all three hyperscalers are still experiencing sluggish growth to some extent. 

Google Cloud may have recorded an operating profit in this quarter, but in Q1 2021 the firm reported a staggering 58% growth as organizations across the globe flocked to the cloud. 

Azure and AWS earnings of late also pale in comparison to that pandemic-era high in 2021 and early 2022. 

Mark Boost, CEO of Civo said that Google Cloud’s earnings are a point of interest given they come at a critical time in the cloud market amid rising concerns over costs. 

“It’s telling that Google Cloud’s first profitable quarter comes at a time of high economic pressure for businesses across the economy,” he told ITPro.

“Q1 has been a time of tightening budgets and minimizing costs,” Boost added. “Rising prices have played a role in driving these profits.”

Hyperscalers are bullish on artificial intelligence 

Satya Nadella’s comments on generative AI benefits appear indicative of how the hyperscalers view the emergence of this new trend. 

In recent weeks, AWS and Google have both outlined plans on how they plan to capitalize on generative AI to support core product offerings, and cloud services are most certainly in the crosshairs. 

 Earlier this month, AWS unveiled the launch of Amazon Bedrock, a managed service offering which it said offers customers the “easiest way to build and scale enterprise-ready generative AI applications”.

The launch of Amazon Bedrock was hailed as potentially a key differentiator for the hyperscaler due to its inclusion of access to third-party large language models (LLMs) to support cloud developers. 

Google also announced the launch of new generative AI tools for cloud developers this week, focusing specifically on bolstering security capabilities for customers within its own ecosystem. 

The Google Cloud Security AI Workbench will offer users an “industry-first extensible platform”, powered by a specialized security-specific large language model (LLM) known as ‘Sec-PaLM’.

ANALYSIS

The extent to which Microsoft has focused on AI in its report is indicative of the cloud giant’s wider company strategy going forward.

Services such as Bing have already benefited from the range of AI tools the Redmond giant has brought in across its product offerings, with the formerly-flagging search engine having exceeded 100 million daily users for the first time in March.

Satya Nadella said that Microsoft is “the platform of choice to help customers get the most value out of their digital spend and innovate for this next generation of AI,” and implicit in this statement is the Microsoft executive’s perspective on the vast value of AI for the firm going forward.

In aligning AI innovation so closely with the wider value proposition of Microsoft’s cloud platform, Microsoft has distanced itself from the competition.

Google has publicly released AI products such as Bard and has now moved to put out ethical AI under the banner of Demis Hassabis’ Google DeepMind, and AWS Bedrock has put developers first with an emphasis on third-party development.

In doing so, both of these firms have set out AI projects from their other operations under specific divisions or frameworks. Microsoft has worked to achieve the opposite, blending generative AI throughout its operations under the ‘Copilot’ umbrella including for 365 Copilot, GitHub Copilot X, Security Copilot, and Copilot for Viva. 

This may cement Microsoft as more of an ‘AI-first’ firm in the eyes of its customers, a title that Google has long held. 

It is more than likely that Microsoft’s spending on AI, so far, outweighs its absolute take in that department - its $10 billion OpenAI investment hasn’t produced a clear profitable product offering as yet. 

But the benefits of such widespread embracement of AI models like GPT-4 can already be seen in bolstered investor confidence, improved customer interest in the 365 suite, and a shot in the arm for Azure’s value offering.

Alphabet has confirmed its subsidiary DeepMind will be merged with Google Brain, the company’s research wing, to form a single unit to be led by current DeepMind CEO Demis Hassabis.

The new joint group will be called Google DeepMind, and represents a new consolidation of AI research as the firm works harder to maintain its AI title against rivals OpenAI and Microsoft.

Hassabis, who co-founded DeepMind in 2010 and has served as its chief executive since, will now lead a larger team and shape Google AI policy and strategy more fully.

He has stressed the potential for AI to “transform industries” and improve lives, and through DeepMind he’s targeted a future in which businesses can utilize general AI to enrich activities.

“By creating Google DeepMind, I believe we can get to that future faster,” wrote Hassabis.

“Building ever more capable and general AI, safely and responsibly, demands that we solve some of the hardest scientific and engineering challenges of our time. For that, we need to work with greater speed, stronger collaboration and execution, and to simplify the way we make decisions to focus on achieving the biggest impact.”

That Google has kept the DeepMind name and retained Hassabis could indicate that he will run the new division in much the same way he has led his company since its inception.

To date, DeepMind has been able to operate with its own ethics reviews and reduced pressure from parent company Alphabet, and has long advocated for cautious development. 

The firm was an early pioneer of reinforcement learning, the context-based training paradigm now commonly used for training large language models (LLMs).

In January, Hassabis, who holds a Ph.D. in cognitive neuroscience along with a rich computer science background, warned against unchecked AI development in an interview with Time magazine.

“When it comes to very powerful technologies – and obviously AI is going to be one of the most powerful ever – we need to be careful,” he said.

In the same interview, former Google SVP Alan Eustace indicated that Hassabis had accepted Google’s acquisition of DeepMind on the understanding that the firm would take an ethical approach to AI development, and turned down a larger acquisition offer from Facebook.

With reference to ramping up speed, and now carrying the Google brand name, Hassabis could be forced to match a slow approach to ethical AI with competitive pressures more intensely in contradiction with prior concerns.

Hassabis has not been alone in voicing concerns about the speed at which AI has already progressed.

Executives from the search giant have warned against the flaws of generative AI, with Alphabet chairman John Hennessy having stated that Google was “hesitant” to release its chatbot Bard due to worries that the product was immature.

In March tech pioneers including Steve Wozniak and Elon Musk called for a six-month pause for “out of control” AI development, and OpenAI CEO Sam Altman has publicly accused Google of increasing risk to meet AI product deadlines.

But Hassabis taking on a more active role in Google’s up-to-now fragmented approach to AI products could be the steadying decision the firm has needed to begin fully executing its development plans for AI and ML features.

In his announcement of the news, Alphabet CEO Sundar Pichai said that Hassabis will “lead the development of our most capable and responsible general AI systems”, while Google Brain co-founder Jeff Dean will take on the role of Google’s chief scientist directly under Pichai.

“Combining all this talent into one focused team, backed by the computational resources of Google, will significantly accelerate our progress in AI,” wrote Pichai.

Whether this acceleration will result in faster AI release schedules, or more rigorous internal testing to ensure that what products it does churn out are more ready for public consumption remains to be seen.

A significant announcement within the notice was that Dean will oversee the production of Google’s first multimodal AI models.

This would put Google on a competitive foot against GPT-4, OpenAI’s multimodal LLM, and could level the playing field for Google after months behind competitors such as ChatGPT.

It is not clear how the newly announced models fit in with the two rumored offerings that fall under the umbrella of Magi’. 

The term refers to internal Google code for a project intended to inject generative AI capabilities into Google’s current search engine expected to be announced later this year.

Sony’s semiconductor arm has announced a “strategic investment” in Raspberry Pi - a move that will see users given access to its edge AI platform Aitrios.

The minority stake investment, details of which remain undisclosed, “cements the relationship” between the two companies and will provide a development platform for Sony Semiconductor Solutions’ (SSS) AI devices to Raspberry Pi’s global user base, the company said. 

This means Raspberry Pi users will be able to leverage Sony’s Aitrios edge computing AI platform to develop a range of AI-based visual sensing applications. 

 “Our goal is to provide new value to a variety of industries and support them in solving issues using our innovative edge AI sensing technology built around image sensors,” said Terushi Shimizu, president and CEO at SSS. 

 “We are very pleased to be partnering with Raspberry Pi to bring out Aitrios platform - which supports the development of unique and diverse solutions utilizing our edge AI devices - to the Raspberry Pi user and developer community, and provide a unique development experience.” 

Eben Upton, CEO at Raspberry Pi, said the partnership builds on longstanding ties between the two companies and will help Sony embed AI products within the firm’s developer ecosystem.  

“This transaction will allow us to expand our partnership, bringing Sony Semiconductor Solutions’ line of AI products to the Raspberry Pi ecosystem, and helping users to build exciting new machine learning (ML) applications at the edge,” he said in a statement. 

What is Sony Aitrios?

Aitrios is an edge AI sensing platform developed by Sony and launched in 2021. 

The platform runs directly on chips, which Sony says provides users with a range of benefits in developing machine learning and AI applications and creates “synergy between edge and cloud”. 

Since its launch, Sony has positioned Aitrios as a go-to platform for developers creating AI camera-driven and edge sensing solutions. 

Aitrios use-cases cited by Sony span a number of sectors, including surveillance, security, retail, logistics, and manufacturing. 

The platform could be used to develop solutions aimed at detecting vehicle license plates or monitoring footfall in retail venues, for example. 

Capitalizing on AI hype

This partnership with Raspberry Pi could signal a move from Sony to capitalize on recent excitement in the AI space. 

Developers have already been experimenting with generative AI systems such as ChatGPT on Raspberry Pi 4 devices, for example. 

Long-term, Upton told CNBC that he envisages the partnership helping young developers learn how computers work in their modern form, rather than how they did 30 years ago. 

He added that Raspberry Pi has been used for ML development for some time now, and that the Sony partnership will allow this to evolve further.

The Raspberry Pi 4 device is largely compatible with several AI and ML frameworks, such as TensorFlow or PyTorch, making it a go-to device for both hobbyists and students experimenting with ML applications. 

However, when speaking to CNBC Upton noted that while the company has traditionally appealed to hobbyists, its popularity among enterprises has been growing steadily. 

Around 70% of its sales now come from commercial clients looking to embed products within consumer devices or general operations. 

OpenAI has unveiled a new bug bounty program offering rewards for security researchers if they can uncover vulnerabilities in its products. 

In an announcement on Tuesday, the California-based AI firm said the bug bounty scheme is “essential to our commitment to develop safe and advanced AI” and deliver services that are secure, reliable, and trustworthy. 

As part of the initiative, OpenAI said it will offer a tiered reward system based on the severity of bugs uncovered by researchers. 

Rewards can range from as little as $200 for low-severity flaws with a maximum reward of $20,000 for “exceptional discoveries”. 

“The OpenAI Bug Bounty Program is a way for us to recognize and reward the valuable insights of security researchers who contribute to keeping our technology and company secure,” the firm said in a statement. 

“We invite you to report vulnerabilities, bugs, or security flaws you discover in our systems. By sharing your findings, you will play a crucial role in making our technology safer for everyone.”

Researchers participating in the new initiative will be able to disclose vulnerabilities or flaws through a partner organisation, Bugcrowd.

Bugcrowd will manage the submission and reward process, which OpenAI said is designed to “ensure a streamlined experience for all participants”. 

ChatGPT vulnerability concerns

The move from OpenAI follows a period of unrest over security-related issues at the generative AI firm, which has close ties with Microsoft. 

Last month, the company revealed that a bug in ChatGPT led to a leak of users' data.

This flaw meant that ChatGPT Plus users began seeing user email addresses, subscriber names, payment addresses, and limited credit card information. 

The issue prompted the company to temporarily take the chatbot offline to work on a fix. 

“The bug was discovered in the Redis client open-source library, redis-py,” OpenAI explained in a post at the time. 

“As soon as we identified the bug, we reached out to the Redis maintainers with a patch to resolve the issue.”

Stack Overflow has made the decision to temporarily stop answers being posted on its platform that are created using the ChatGPT AI-powered natural language processor (NLP).

The moderators of the software engineering knowledge exchange platform said that it’s a temporary policy aiming to slow down the rise in answers created with the AI tool.

The decision was made since the average rate of getting a ChatGPT answer that is correct is 'quite low', said the moderators. Due to this, accepting ChatGPT answers to the site is “substantially harmful” not only to Stack Overflow but also to users looking for solutions to their problems.

In the post, moderators said that the main issue is that the answers have a high rate of being incorrect but appear to look authentic, as well as being very easy to create.

“There are also many people trying out ChatGPT to create answers, without the expertise or willingness to verify that the answer is correct prior to posting,” said the moderators.

“Because such answers are so easy to produce, a large number of people are posting a lot of answers. The volume of these answers (thousands) and the fact that the answers often require a detailed read by someone with at least some subject matter expertise in order to determine that the answer is actually bad has effectively swamped our volunteer-based quality curation infrastructure.”

This has led the moderators to ban the use of ChatGPT to create posts on the platform. If a moderator believes that a user has broken this policy, sanctions are set to be imposed to stop users from continuing this behaviour, even if normally the post would be acceptable. The moderators didn’t clarify what these sanctions would be.

The platform is still developing a final policy on the use of ChatGPT and similar AI tools which it said will need to be discussed with staff as well as users on Meta Stack Overflow.

ChatGPT is an AI-driven NLP created by OpenAI. It acts as an interface with which users can send prompts and it will return answers. It can simply respond to questions using natural language, as well as suggest additions or completions in code, and in some cases, spot security vulnerabilities in a codebase.

It uses a dialogue format where the tool can answer follow-up questions or reject inappropriate requests. However, OpenAI stated on its website that ChatGPT has several limitations, including that it sometimes writes “plausible-sounding but incorrect or nonsensical answers.”

Its creators said this is hard to fix, as during reinforcement learning there’s no source of truth and training it to be more cautious makes it decline questions that it’s able to answer correctly.

When IT Pro asked the AI tool what the problems are with ChatGPT, it produced this result:

Since the launch of ChatGPT on 30 November, tech enthusiasts have experimented with the API. People have found wide uses for it from the fun, such as text-based adventure games, to the professional, such as potentially innovative ways to educate students on various topics.

the advent of the technology has impressed individuals across the sector and some have claimed it's one of the biggest technological advancements of the year.

However, this isn’t the first AI tool from OpenAI to have problems. In November 2021, OpenAI brought to market its GPT-3 natural language processing API.

It opened up the technology to help developers make applications faster. However, it had been described in the past as “too dangerous to release to the general public” since there were fears over its ability to produce fake news that appeared to be real.

OpenAI has made a new version of its GPT-3 AI language model available that promises to be better at following user intentions while also producing results that are more truthful and less toxic.

The Open AI API is powered by GPT-3 language models that can be used to perform natural language tasks using carefully engineered text prompts. However, the models can also produce outputs that are untruthful, toxic, or reflect harmful sentiments.

The organisation's AI models have been criticised in the past for a range of shortcomings, including racism against specific genders and religions. The organisation once called GPT-3 too dangerous to make public, due to the API being able to create fake news stories by taking cues from the eight million web pages it had scanned to learn about language.

The organisation said this is partly because GPT-3 is trained to predict the next word on a large dataset of Internet text instead of safely performing the language tasks the user wants.

To make its models safer, and more aligned with users, OpenAI used a technique known as reinforcement learning from human feedback (RLHF), using human helpers called labelers to assist the AI in its learning.

“On prompts submitted by our customers to the API, our labelers provide demonstrations of the desired model behavior, and rank several outputs from our models. We then use this data to fine-tune GPT-3,” said the company.

It found the resulting models are much better at following instructions than the GPT-3. They also make up facts less often and show small decreases in toxicity. The organisation’s labelers prefer outputs from its new 1.3B InstructGPT model over outputs from its 175B GPT-3 model, despite having over 100x fewer parameters.

These InstructGPT models have been in beta on the API for over a year and are now the default language models accessible on OpenAI’s API.

“We believe that fine-tuning language models with humans in the loop is a powerful tool for improving their safety and reliability, and we will continue to push in this direction,” the organisation explained.

However, OpenAI outlined that there are some limitations to this model too. The InstructGPT models, for example, are far from fully aligned or fully safe, meaning they still generate toxic outputs, make up facts, or generate sexual and violent content without explicit prompting.

It said that to support the safety of its API, it will continue to review potential applications before they go live, provide content filters for detecting unsafe completions, and monitor for misuse.

OpenAI also highlighted that in many cases, aligning to the average labeler preference may not be desirable. The example it gave is that when generating text that disproportionately affects a minority group, the preferences of that group should be weighted more heavily.

“Right now, InstructGPT is trained to follow instructions in English; thus, it is biased towards the cultural values of English-speaking people,” it said. “We are conducting research into understanding the differences and disagreements between labelers’ preferences so we can condition our models on the values of more specific populations.”

In collaboration with NVIDIA, DataDirect Networks (DDN) has launched an artificial intelligence (AI) Innovation Lab in Singapore to foster innovation and accelerate enterprise AI deployment.

The lab is equipped with DDN's A3I AI400X systems that provide superior performance, optimal efficiency, and flexible expansion when combined with NVIDIA DGX servers and workstations.

Additionally, DDN A3I’s shared parallel architecture offers high redundancy and failover capabilities to keep data accessible and reliable in the event of server or network failure.

AI400X systems from DDN provide robust and high-performance storage for NVIDIA Selene, the world's sixth-most powerful supercomputer. DDN systems also support NVIDIA DGX POD and NVIDIA DGX SuperPOD configurations with an optimized storage infrastructure to accommodate complex AI workloads.

With NVIDIA DGX SuperPOD, businesses have access to comprehensive services ranging from installation to managed production of AI.

According to DDN, the AI Innovation Lab will equip customers and partners with the tools and resources they need to develop AI-driven solutions. The lab will also enable enterprises to build, test, and optimize AI models at scale.

"The AI Innovation Lab will focus on enabling customers to innovate and develop industry-specific AI solutions to help customers and partners to co-create proof of concepts, innovate and test production-ready solutions in the lab," said Dennis Ang, director of enterprise business for SEA and ANZ regions at NVIDIA.

Ang added, "With DDN and NVIDIA's powerful technologies, organizations can access world-class computing and storage infrastructure to fast track a broad range of AI and data science initiatives."

Commenting on their partnership, DDN general manager for India & S.E. Asia, Atul Vidwansa, said, "As a trusted data storage solutions provider, we are excited to collaborate with NVIDIA to deliver Intelligent Infrastructure to enterprises locally to develop, test and deploy rich AI solutions at scale.”

"This further demonstrates our commitment to empowering customers and partners to drive AI-powered innovations as quickly as possible."

IBM has unveiled its new Environmental Intelligence Suite, a collection of artificial intelligence (AI)-based software tools aimed at helping organizations prepare for and respond to weather and climate risks.

The suite, offered as a software as a service (SaaS) product, brings together AI, weather data, climate risk analytics, and carbon accounting capabilities. IBM said this would allow firms to spend fewer resources collecting such complex data and more on analyzing it for insights and taking action to improve their operations.

IBM said the suite delivers environmental insights via application programming interfaces (APIs), dashboards, maps, and alerts that can help companies address immediate operational challenges and longer-term planning and strategies. It also uses technologies from IBM Research, which apply natural language processing and automation designed to help companies estimate carbon emissions and identify opportunities to reduce them across their operations or with suppliers.

Among the examples IBM gave of the suite’s usefulness was the ability for companies to monitor disruptive environmental conditions, such as severe weather, wildfires, flooding, and air quality and send alerts when detected. The suite can also predict potential impacts of climate change and weather across the business using climate risk analytics.

Companies can also gain insights into potential operational disruptions and prioritize mitigation and response efforts. Other features measure and report on environmental initiatives while reducing this burden on procurement and operations teams.

For example, IBM environmental data and geospatial analytics are being used by Brazilian ethanol, bioelectricity, and sugar company BP Bunge Bioenergia. This data helps it better understand its agricultural sugarcane production and improve its market intelligence estimates regarding global sugar production. Agribusiness leader Cajamar also uses it to help Spanish farmers improve yields and reduce environmental impact via its digital Plataforma Tierra tool.

Kareem Yusuf, general manager of IBM AI Applications, said the future of business and the environment are deeply intertwined. “Not only are companies coping with the effects of extreme weather disruptions on their operations, they're also being held increasingly accountable by shareholders and regulators for how their operations impact the planet,” he said.

The suite integrates with IBM's broader software portfolio for additional efficiencies across business operations, including IBM Maximo Application Suite to help companies protect and extend critical assets’ life cycle and IBM Supply Chain Intelligence Suite to help build more sustainable supply chains.

Vecow has announced its new artificial intelligence (AI)-powered edge computing solution: ECX-2400 AI.

Developed in partnership with Blaize, Vecow ECX-2400 AI features programmable GSP architecture, ensuring low latency and high performance.

Powered by 10-core 10th Gen Intel Xeon/Core i9/i7/i5/i3 processor and built on Blaize Xplorer X1600P PCIe series AI accelerator card, Vecow ECX-2400 AI also delivers increased system reliability, reduced power consumption, and faster time-to-market at a lower total cost of ownership.

The system is geared toward Industry 4.0 applications, including smart AOI, traffic vision, robotic control, and more.

The compact and rugged Vecow ECX-2400 AI also pairs Blaize Picasso SDK with VHub AI developer software. This allows users to build, deploy and manage edge AI inference systems with single-core or quad-core Blaize Xplorer AI Inference Accelerators.

Vecow global sales director Joseph Huang said, “It’s our honor to carry a new powerful and lower power consumption AI accelerator solution to the market. Industrial users have stringent requirements for trusted and reliable AI computing engines with longevity support and less total cost of ownership. This collaboration with Blaize contributes our global partners and customers closer to their success with faster time-to-market consideration. We will keep engaging advanced AI technologies to benefit to our global partners and customers.”

Vecow’s AI computing system also addresses industrial demands with its smart system protection functions, including 12V to 50V wide range DC input, 80V surge protection, and software ignition control.

Optional VHub Artificial Intelligence of Things (AIoT) solution offers support for OpenVINO based AI accelerator and advanced edge AI applications.

“We look forward to jointly engaging with end customers and channel partners to deliver the new levels of efficient and cost-effective AI inference systems enabled by the Vecow ECX-2400 AI,” said Barrie Mullins, senior director of product marketing at Blaize.

Mullins added, “Industrial customers using vision analytics in use cases such as industrial inspection, manufacturing production line anomaly, human-robot interaction, employee safety and smart camera systems can utilize the ECX-2400 AI to advance operational efficiencies and processes.”

IBM has announced several enhancements to Watson Assistant, enabling businesses to provide better customer service across touchpoints.

New features, including artificial intelligence (AI) and automation capabilities, will reportedly help brands resolve customer queries promptly and at first contact.

Commenting on the addition of voice support, IBM said IntelePeer’s Atmosphere Communications Platform is now available as an add-on in Watson Assistant. Plus and Enterprise plan subscribers have the option to set up telephone numbers and voice capabilities for their virtual agents.

If a business already has a contact center phone system, IntelePeer will integrate Watson Assistant with existing call center systems via industry-standard protocols. In partnership with IntelePeer, IBM also developed a smart agent app to enable seamless handoffs between bots and live agents.

IBM's speech recognition models provide near-real-time transcriptions of customer conversations. Customer service representatives can readily view and access the latest transcripts during handoffs with the new agent app, resulting in reduced response times.

"One critical challenge facing nearly every business today is the need to better communicate with their customers and employees across all channels," said Daniel Hernandez, general manager of data and AI at IBM.

Hernandez added, "We're continuously delivering new innovations in natural language processing, automation and advancing responsible AI, and building a strong, open ecosystem with partners like IntelePeer to help businesses transform customer care with Watson Assistant."

Furthermore, Watson Assistant's Search Skill now includes a short-answer retrieval function. Backed by IBM Research’s robust question-answering (QA) system, the new feature enables virtual agents to articulate answers in a few words from longer sentences or paragraphs, while also citing source and context.

Lastly, FAQ extraction is generally available on Search Skill. Using this feature, administrators can sync virtual agents with broader sources of information, reducing the need for constant manual updates.

The US is unprepared to defend itself against artificial intelligence (AI) threats, according to a report from the National Security Commission on Artificial Intelligence (NSCAI), which has the government to increase its AI investment as a matter of urgent national security.

The document singled out China as a primary threat to America due to its advanced use of AI. "China possesses the might, talent, and ambition to surpass the United States as the world’s leader in AI in the next decade if current trends do not change," it said.

The report also warned of AI-based attacks extending beyond the technology's use in weapons systems. "AI is deepening the threat posed by cyber attacks and disinformation campaigns that Russia, China, and others are using to infiltrate our society, steal our data, and interfere in our democracy," it said. "The limited uses of AI-enabled attacks to date represent the tip of the iceberg."

The Commission recommended $40 billion in federal government AI investment, calling it "a modest down payment on future breakthroughs".

However, the report refused to call for a ban on AI-based weapons. "It is neither feasible nor currently in the interests of the United States to pursue a global prohibition of AI-enabled and autonomous weapon systems," it said, instead recommending international standards to manage their development and use. It also called for a ban on AI-triggered nuclear weapon launches.

The document also called for a democratic model of AI use for national security and a task force that would assess the effect of AI on privacy and civil liberties, and encouraged the oversight and auditing of AI tools using AI itself.

Other recommendations included scaling up digital talent in the military. The report called for a Digital Service Academy based on the five current military service academies. This would encourage the growth of technology talent, it advised.

The government formed the NSCAI in August 2018 as part of the National Defense Authorization Act. It is an independent body tasked with developing strategies for advancing the development of AI for national security and defense purposes in the US. It is chaired by former Alphabet executive chair Eric Schmidt and includes commissioners from Google, Oracle, Microsoft, and Amazon Web Services. Also on the committee was a representative from CIA-affiliated venture group In-Q-Tel. There were no privacy or civil rights advocacy groups on the list of commissioners.

The report arrived just days after Senator Chuck Schumer promised a legislative package to strengthen domestic competitiveness in a range of technology areas, including AI. That legislation will be based on a bill Schumer proposed last year that would have earmarked $100 billion for investment.

In the final days of the Trump presidency, the White House also launched an AI initiative office to coordinate federal AI activities.

Q-CTRL has announced a new AI-based toolset to facilitate the unassisted performance optimization of quantum computers.

By and large, quantum algorithms are susceptible to errors, creating a substantial barrier to progress and advancement in quantum computing. Q-CTRL’s new automated closed-loop hardware optimization tool uses custom AI agents to run quantum algorithms, resulting in fewer errors and better overall performance for end-users.

Integrated with Q-CTRL’s flagship BOULDER OPAL software for developers and R&D teams, automated closed-loop hardware optimization is also trained to obtain new experimental data/results from quantum computers while simultaneously running optimizations for algorithms. It can be used as a standalone tool or in tandem with a machine-learner online optimization package (M-LOOP) that manages quantum experiments autonomously.

Users can also use the Q-CTRL Python package to manually send batch requests to experimental apparatus for running multiple tests in quick succession.

Q-CTRL had previously demonstrated its novel quantum technology on an IBM quantum computer, resulting in quantum logic gates for individual qubits (or quantum bits) with up to 10 times better performance than standard logic gates. Q-CTRL’s custom AI agent could also detect new multi-qubit gates autonomously with up to two times lower error-rate.

"Just like software abstraction on conventional computers enables programmers to write algorithms without a need to understand how a transistor works, this tool makes it much easier for researchers to explore the potential of quantum computers," said Michael J. Biercuk, founder and CEO of Q-CTRL.

"Watching a quantum computer tune itself up with this tool and deliver quantum logic with lower errors than that achieved by the best hardware development teams in the world is quite amazing. We believe it will accelerate the development of quantum computer hardware and applications, pushing the industry closer toward delivering real-world business value,” Biercuk added.