In this article, we delve deeper into the key findings and implications—drawing on additional sources to enhance our understanding.

Thriving industry with significant growth

The study highlights the remarkable growth of the UK’s AI sector. With over 3,170 active AI companies, these firms have generated £10.6 billion in AI-related revenues and employed more than 50,000 people in AI-related roles. This significant contribution to GVA (Gross Value Added) underscores the sector’s transformative potential in driving the UK’s economic growth.

Mark Boost, CEO of Civo, said: “In a space that’s been dominated by US companies for too long, it’s promising to see the government now stepping up to help support the UK AI sector on the global stage.”

The study shows that AI activity is dispersed across various regions of the UK, with notable concentrations in London, the South East, and Scotland. This regional dispersion indicates a broad scope for the development of AI technology applications across different sectors and regions.

Investment and funding

Investment in the AI sector has been a key driver of growth. In 2022, £18.8 billion was secured in private investment since 2016, with investments made in 52 unique industry sectors compared to 35 sectors in 2016.

The government’s commitment to supporting AI is evident through significant investments. In 2022, the UK government unveiled a National AI Strategy and Action Plan—committing over £1.3 billion in support for the sector, complementing the £2.8 billion already invested.

However, as Boost cautions, “Major players like AWS are locking AI startups into their ecosystems with offerings like $500k cloud credits, ensuring that emerging companies start their journey reliant on their infrastructure. This not only hinders competition and promotes vendor lock-in but also risks stifling innovation across the broader UK AI ecosystem.”

Addressing bottlenecks

Despite the growth and investment, several bottlenecks must be addressed to fully harness the potential of AI:

• Infrastructure: The UK’s digital technology infrastructure is less advanced than many other countries. This bottleneck includes inadequate data centre infrastructure and a dependent supply of powerful GPU computer chips. Boost emphasises this concern, stating “It would be dangerous for the government to ignore the immense compute power that AI relies on. We need to consider where this power is coming from and the impact it’s having on both the already over-concentrated cloud market and the environment.”

• Commercial awareness: Many SMEs lack familiarity with digital technology. Almost a third (31%) of SMEs have yet to adopt the cloud, and nearly half (47%) do not currently use AI tools or applications.

• Skills shortage: Two-fifths of businesses struggle to find staff with good digital skills, including traditional digital roles like data analytics or IT. There is a rising need for workers with new AI-specific skills, such as prompt engineering, that will require retraining and upskilling opportunities.

To address these bottlenecks, the government has implemented several initiatives:

• Private sector investment: Microsoft has announced a £2.5 billion investment in AI skills, security, and data centre infrastructure, aiming to procure more than 20,000 of the most advanced GPUs by 2026.

• Government support: The government has invested £1.5 billion in computing capacity and committed to building three new supercomputers by 2025. This support aims to enhance the UK’s infrastructure to stay competitive in the AI market.

• Public sector integration: The UK Government Digital Service (GDS) is working to improve efficiency using predictive algorithms for future pension scheme behaviour. HMRC uses AI to help identify call centre priorities, demonstrating how AI solutions can address complex public sector challenges.

Future prospects and challenges

The future of the UK AI sector is both promising and challenging. While significant economic gains are predicted, including boosting GDP by £550 billion by 2035, delays in AI roll-out could cost the UK £150 billion over the same period. Ensuring a balanced approach between innovation and regulation will be crucial.

Boost emphasises the importance of data sovereignty and privacy: “Businesses have grown increasingly wary of how their data is collected, stored, and used by the likes of ChatGPT. The government has a real opportunity to enable the UK AI sector to offer viable alternatives.

“The forthcoming AI Action Plan will be another opportunity to identify how AI can drive economic growth and better support the UK tech sector.”

• AI Safety Summit: The AI Safety Summit at Bletchley Park highlighted the need for responsible AI development. The “Bletchley Declaration on AI Safety” emphasises the importance of ensuring AI tools are transparent, fair, and free from bias to maintain public trust and realise AI’s benefits in public services.

• Cybersecurity challenges: As AI systems handle sensitive or personal information, ensuring their security is paramount. This involves protecting against cyber threats, securing algorithms from manipulation, safeguarding data centres and hardware, and ensuring supply chain security.

The AI sector study underscores a thriving industry with significant growth potential. However, it also highlights several bottlenecks that must be addressed – infrastructure gaps, lack of commercial awareness, and skills shortages – to fully harness the sector’s potential.

(Photo by John Noonan)

See also: EU AI Act: Early prep could give businesses competitive edge

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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While today’s AI excels in specific areas – think GPT-4 composing poetry or DeepMind’s AlphaFold predicting protein structures – it’s still miles away from genuine human-like intelligence. 

“While the novel neural-symbolic AI approaches developed by the SingularityNET AI team decrease the need for data, processing and energy somewhat relative to standard deep neural nets, we still need significant supercomputing facilities,” SingularityNET CEO Ben Goertzel explained to LiveScience in a recent written statement.

Enter SingularityNET’s ambitious plan: a “multi-level cognitive computing network” designed to host and train the incredibly complex AI architectures required for AGI. Imagine deep neural networks that mimic the human brain, vast language models (LLMs) trained on colossal datasets, and systems that seamlessly weave together human behaviours like speech and movement with multimedia outputs.

But this level of sophistication doesn’t come cheap. The first supercomputer, slated for completion by early 2025, will be a Frankensteinian beast of cutting-edge hardware: Nvidia GPUs, AMD processors, Tenstorrent server racks – you name it, it’s in there.

This, Goertzel believes, is more than just a technological leap, it’s a philosophical one: “Before our eyes, a paradigmatic shift is taking place towards continuous learning, seamless generalisation, and reflexive AI self-modification.”

To manage this distributed network and its precious data, SingularityNET has developed OpenCog Hyperon, an open-source software framework specifically designed for AI systems. Think of it as the conductor trying to make sense of a symphony played across multiple concert halls. 

But SingularityNET isn’t keeping all this brainpower to itself. Reminiscent of arcade tokens, users will purchase access to the supercomputer network with the AGIX token on blockchains like Ethereum and Cardano and contribute data to the collective pool—fuelling further AGI development.  

With experts like DeepMind’s Shane Legg predicting human-level AI by 2028, the race is on. Only time will tell if this global network of silicon brains will birth the next great leap in artificial intelligence.

(Photo by Anshita Nair)

See also: The merging of AI and blockchain was inevitable – but what will it mean?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Musk announced this collaboration on his social media platform, X, marking a key development in xAI’s goal to assemble what he has repeatedly called “the world’s biggest supercomputer.”

Server racks are an integral part of high-performance computing infrastructure, providing the skeleton needed to store and organise the various computing components necessary for supercomputer operations. These engineered rooms are designed to promote optimal efficiency and airflow—which is of vital importance in the world of supercomputing—by taking advantage of limited floor space.

Server racks, such as those used in xAI’s Grok for large-scale AI model training, are essential components of the server infrastructure that support the immense computational power required for these workloads. Hundreds of thousands of power-hungry AI chips are needed for these projects to achieve the desired scale, and there are insufficient production cycles available in semiconductor foundries.

xAI’s project is massive; thus, heat management was especially challenging on their scale. Current technology just isn’t fast enough, and supercomputers—which can perform calculations thousands of times faster—get so hot that the chips inside them degrade in performance over time. This issue is only exacerbated by the need for thousands of power-hungry AI chips required to train more advanced AI models like xAI’s Grok.

Partnership details: Dell and Super Micro’s roles

According to Musk, Dell Technologies will be responsible for assembling half of the racks for xAI’s supercomputer. Super Micro Computer, referred to as “SMC” by Musk, will provide the remaining half. Super Micro, known for its close ties with chip firms like Nvidia and its expertise in liquid-cooling technology, has confirmed this partnership to Reuters.

San Francisco-based Super Micro is renowned for its innovative approaches to server design, particularly its liquid-cooling technology. This technology is crucial for managing the extreme heat generated by high-performance computing systems, allowing for more efficient operation and potentially extending the lifespan of components.

In a related development, Dell CEO Michael Dell announced on X that the company is collaborating with Nvidia to build an “AI factory” that will power the next version of xAI’s chatbot, Grok. This collaboration underscores the extensive computational resources that advanced AI model training requires.

Musk has previously stated that training the Grok 2 model required approximately 20,000 Nvidia H100 graphic processing units (GPUs), and future versions might need up to 100,000 of these chips. According to The Information, the proposed supercomputer is expected to be operational by fall 2025.

Both Dell Technologies and Super Micro Computer bring extensive experience and expertise to this project. Dell has been a trusted supplier of servers and data centre infrastructure for decades, powering many of the world’s largest cloud computing platforms and supercomputing facilities, such as the Frontera supercomputer at the Texas Advanced Computing Center.

Super Micro has established itself as a leader in providing high-performance, energy-efficient server solutions. Their innovations in liquid cooling and blade server architectures are widely utilised by cloud providers, enterprises, and research institutions for demanding workloads like AI and high-performance computing.

Implications for AI and supercomputing technologies

The collaboration between xAI, Dell Technologies, and Super Micro Computer represents a significant milestone in the advancement of AI and supercomputing technologies. As the project progresses, it will likely push the boundaries of high-performance computing and contribute to the rapid evolution of artificial intelligence capabilities.

This partnership also highlights the growing importance of specialised hardware in the AI industry. As AI models become increasingly complex and data-intensive, the demand for high-performance computing solutions is expected to continue rising, potentially reshaping the landscape of the tech industry in the coming years.

See also: Dell, Intel and University of Cambridge deploy the UK’s fastest AI supercomputer

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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This cutting-edge AI supercomputer stands as the fastest of its kind in the UK today. It marks a groundbreaking fusion of AI and high-performance computing (HPC) technologies, showcasing the potential to tackle some of the world’s most pressing challenges.

Dawn Phase 1 is the cornerstone of the recently launched UK AI Research Resource (AIRR), demonstrating the nation’s commitment to exploring innovative systems and architectures.

This supercomputer brings the UK closer to achieving the exascale; a computing threshold of a quintillion (10^18) floating point operations per second. To put this into perspective, the processing power of an exascale system equals what every person on Earth would calculate in over four years if they were working non-stop, 24 hours a day.

Operational at the Cambridge Open Zettascale Lab, Dawn utilises Dell PowerEdge XE9640 servers, providing an unparalleled platform for the Intel Data Center GPU Max Series accelerator. This collaboration ensures a diverse ecosystem through oneAPI, fostering an environment of choice.

The system’s capabilities extend across various domains, including healthcare, engineering, green fusion energy, climate modelling, cosmology, and high-energy physics.

Adam Roe, EMEA HPC technical director at Intel, said:

“Dawn considerably strengthens the scientific and AI compute capability available in the UK and it’s on the ground and operational today at the Cambridge Open Zettascale Lab.

Dell PowerEdge XE9640 servers offer a no-compromises platform to host the Intel Data Center GPU Max Series accelerator, which opens up the ecosystem to choice through oneAPI.

I’m very excited to see the sorts of early science this machine can deliver and continue to strengthen the Open Zettascale Lab partnership between Dell Technologies, Intel, and the University of Cambridge, and further broaden that to the UK scientific and AI community.”

Glimpse into the future

Dawn Phase 1 is not just a standalone achievement; it’s part of a broader strategy.

The collaborative endeavour aims to deliver a Phase 2 supercomputer in 2024, promising tenfold performance levels. This progression would propel the UK’s AI capability, strengthening the successful industry partnership.

The supercomputer’s technical foundation lies in Dell PowerEdge XE9640 servers, renowned for their versatile configurations and efficient liquid cooling technology. This innovation ensures optimal handling of AI and HPC workloads, offering a more effective solution than traditional air-cooled systems.

Tariq Hussain, Head of UK Public Sector at Dell, commented:

“Collaborations like the one between the University of Cambridge, Dell Technologies and Intel, alongside strong inward investment, are vital if we want the compute to unlock the high-growth AI potential of the UK. It is paramount that the government invests in the right technologies and infrastructure to ensure the UK leads in AI and exascale-class simulation capability.

It’s also important to embrace the full spectrum of the technology ecosystem, including GPU diversity, to ensure customers can tackle the growing demands of generative AI, industrial simulation modelling and ground-breaking scientific research.”

As the world awaits the full technical details and performance numbers of Dawn Phase 1 – slated for release in mid-November during the Supercomputing 23 (SC23) conference in Denver, Colorado – the UK stands at the precipice of a transformative era in scientific and AI research.

This collaboration between industry giants and academia not only accelerates research discovery but also propels the UK’s knowledge economy to new heights.

(Image Credit: Joe Bishop for Cambridge Open Zettascale Lab)

See also: UK paper highlights AI risks ahead of global Safety Summit

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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Dojo was unveiled at Tesla’s annual AI Day last year but the project was still in its infancy. At AI Day 2022, Tesla unveiled the progress it has made with Dojo over the course of the year.

The supercomputer has transitioned from just a chip and training tiles into a full cabinet. Tesla claims that it can replace six GPU boxes with a single Dojo tile, which it says is cheaper than one GPU box.

Per tray, there are six Dojo tiles. Tesla claims that each tray is equivalent to “three to four full-loaded supercomputer racks”. Two trays can fit in a single Dojo cabinet with a host assembly.

Such a supercomputer naturally has a large power draw. Dojo requires so much power that it managed to trip the grid in Palo Alto.

“Earlier this year, we started load testing our power and cooling infrastructure. We were able to push it over 2 MW before we tripped our substation and got a call from the city,” said Bill Chang, Tesla’s Principal System Engineer for Dojo.

In order to function, Tesla had to build custom infrastructure for Dojo with its own high-powered cooling and power system.

An ‘ExaPOD’ (consisting of a few Dojo cabinets) has the following specs:

• 1.1 EFLOP
• 1.3TB SRAM
• 13TB DRAM

Seven ExaPODs are currently planned to be housed in Palo Alto.

Dojo is purpose-built for AI and will greatly improve Tesla’s ability to train neural nets using video data from its vehicles. These neural nets will be critical for Tesla’s self-driving efforts and its humanoid robot ‘Optimus’, which also made an appearance during this year’s event.

Optimus

Optimus was also first unveiled last year and was even more in its infancy than Dojo. In fact, all it was at the time was a person in a spandex suit and some PowerPoint slides.

While it’s clear that Optimus still has a long way to go before it can do the shopping and carry out dangerous manual labour tasks, as Tesla envisions, we at least saw a working prototype of the robot at AI Day 2022.

“I do want to set some expectations with respect to our Optimus robot,” said Tesla CEO Elon Musk. “As you know, last year it was just a person in a robot suit. But, we’ve come a long way, and compared to that it’s going to be very impressive.”

Optimus can now walk around and, if attached to apparatus from the ceiling, do some basic tasks like watering plants:

The prototype of Optimus was reportedly developed in the past six months and Tesla is hoping to get a working design within the “next few months… or years”. The price tag is “probably less than $20,000”.

All the details of Optimus are still vague at the moment, but at least there’s more certainty around the Dojo supercomputer.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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The supercomputer is called the AI Research SuperCluster (RSC) and is yet to be fully complete. However, Meta’s researchers have already begun using it for training large natural language processing (NLP) and computer vision models.

RSC is set to be fully built in mid-2022. Meta says that it will be the fastest in the world once complete and the aim is for it to be capable of training models with trillions of parameters.

“We hope RSC will help us build entirely new AI systems that can, for example, power real-time voice translations to large groups of people, each speaking a different language, so they can seamlessly collaborate on a research project or play an AR game together,” wrote Meta in a blog post.

“Ultimately, the work done with RSC will pave the way toward building technologies for the next major computing platform — the metaverse, where AI-driven applications and products will play an important role.”

For production, Meta expects RSC will be 20x faster than Meta’s current V100-based clusters. RSC is also estimated to be 9x faster at running the NVIDIA Collective Communication Library (NCCL) and 3x faster at training large-scale NLP workflows.

A model with tens of billions of parameters can finish training in three weeks compared with nine weeks prior to RSC.

Meta says that its previous AI research infrastructure only leveraged open source and other publicly-available datasets. RSC was designed with the security and privacy controls in mind to allow Meta to use real-world examples from its production systems in production training.

What this means in practice is that Meta can use RSC to advance research for vital tasks such as identifying harmful content on its platforms—using real data from them.

“We believe this is the first time performance, reliability, security, and privacy have been tackled at such a scale,” says Meta.

(Image Credit: Meta)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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The UK’s most powerful supercomputer and among the world’s top fifty, Cambridge-1 was announced by the technology company in October last year and cost $100 million (£72m) to build.

Its first projects with AstraZeneca, GSK, Guy’s and St Thomas’ NHS Foundation Trust, King’s College London, and Oxford Nanopore Technologies include developing a deeper understanding of brain diseases like dementia, using AI to design new drugs, and improving the accuracy of finding disease-causing variations in human genomes.

“Cambridge-1 will empower world-leading researchers in business and academia with the ability to perform their life’s work on the U.K.’s most powerful supercomputer, unlocking clues to disease and treatments at a scale and speed previously impossible in the U.K.,” said Jensen Huang, founder and CEO of NVIDIA.

“The discoveries developed on Cambridge-1 will take shape in the U.K., but the impact will be global, driving groundbreaking research that has the potential to benefit millions around the world,” he added.

AI for healthcare is growing rapidly in the UK, with a range of start-ups and larger pharmaceutical companies turning to mining the vast quantities of data available to discover potential drugs, further understand certain diseases, and improve and personalise patient care.

“It’s great to see it in the UK ecosystem,” said Roel Bulthuis, head of the healthcare team at Inkef Capital. “Many European healthcare systems are not as advanced in their thinking about using data and integrating that into the healthcare system.”

According to Frontier Economics, an economics consulting firm, Cambridge-1 has the potential to create an estimated value of £600 million over the next 10 years.

Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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The A100 Tensor Core GPU set new MLPerf benchmark records last month—outperforming CPUs by up to 237x in data centre inference. In November, Amazon Web Services made eight A100 GPUs available in each of its P4d instances.

For those who prefer their hardware local, the DGX Station A100 is available in either four 80GB A100 GPUs or four 40GB configurations. The monstrous 80GB version of the A100 has twice the memory of when the GPU was originally unveiled just six months ago.

“We doubled everything in this system to make it more effective for customers,” said Paresh Kharya, senior director of product management for accelerated computing at NVIDIA.

NVIDIA says the two configurations provide options for data science and AI research teams to select a system according to their unique workloads and budgets.

Charlie Boyle, VP and GM of DGX systems at NVIDIA, commented:

“DGX Station A100 brings AI out of the data centre with a server-class system that can plug in anywhere.

Teams of data science and AI researchers can accelerate their work using the same software stack as NVIDIA DGX A100 systems, enabling them to easily scale from development to deployment.”

The memory capacity of the DGX Station A100 powered by the 80GB GPUs is now 640GB, enabling much larger datasets and models.

“To power complex conversational AI models like BERT Large inference, DGX Station A100 is more than 4x faster than the previous generation DGX Station. It delivers nearly a 3x performance boost for BERT Large AI training,” NVIDIA wrote in a release.

DGX A100 640GB configurations can be integrated into the DGX SuperPOD Solution for Enterprise for unparalleled performance. Such “turnkey AI supercomputers” are available in units consisting of 20 DGX A100 systems.

Since acquiring ARM, NVIDIA continues to double-down on its investment in the UK and its local talent.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named,” Huang said in September. “We want to propel ARM – and the UK – to global AI leadership.”

NVIDIA’s latest supercomputer, the Cambridge-1, is being installed in the UK and will be one of the first SuperPODs with DGX A100 640GB systems. Cambridge-1 will initially be used by local pioneering companies to supercharge healthcare research.

Dr Kim Branson, SVP and Global Head of AI and ML at GSK, commented:

“Because of the massive size of the datasets we use for drug discovery, we need to push the boundaries of hardware and develop new machine learning software.

We’re building new algorithms and approaches in addition to bringing together the best minds at the intersection of medicine, genetics, and artificial intelligence in the UK’s rich ecosystem.

This new partnership with NVIDIA will also contribute additional computational power and state-of-the-art AI technology.”

The use of AI for healthcare research has received extra attention due to the coronavirus pandemic. A recent simulation of the coronavirus, the largest molecular simulation ever, simulated 305 million atoms and was powered by 27,000 NVIDIA GPUs.

Several promising COVID-19 vaccines in late-stage trials have emerged in recent days which have raised hopes that life could be mostly back to normal by summer, but we never know when the next pandemic may strike and there are still many challenges we all face both in and out of healthcare.

Systems like the DGX Station A100 help to ensure that – whatever challenges we face now and in the future – researchers have the power they need for their vital work.

Both configurations of the DGX Station A100 are expected to begin shipping this quarter.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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AI is helping with COVID-19 research around the world and much of it is being powered by NVIDIA GPUs. It’s a daunting task, new drugs often cost over $2.5 billion in research and development — doubling every nine years — and 90 percent of efforts fail.

Nvidia wants to help speed up discoveries of vital medicines while reducing costs

“COVID-19 hits home this urgency [for new tools],” Huang says.

Huang announced NVIDIA Clara Discovery—a suite of tools for assisting scientists in discovering lifesaving new drugs.

NVIDIA Clara combines imaging, radiology, and genomics to help develop healthcare AI applications. Pre-trained AI models and application-specific frameworks help researchers to find targets, build compounds, and develop responses.

Dr Hal Barron, Chief Scientific Officer and President of R&D at GSK, commented:

“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise.

NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients.

Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the UK’s outstanding scientists.”

Researchers can now use biomedical-specific language models for their work, thanks to a breakthrough in natural language processing. This means researchers can organise and activate large datasets, research literature, and sort through papers or patents on existing treatments and other vital real-world data.

“Where there are popular industry tools, our computer scientists accelerate them,” Huang said. “Where no tools exist, we develop them—like NVIDIA Parabricks, Clara Imaging, BioMegatron, BioBERT, NVIDIA RAPIDS.”

We’re all hoping COVID-19 research – using such powerful new tools available to scientists – can lead to a vaccine within a year or two, when they have often taken a decade or longer to create.

“The use of big data, supercomputing, and artificial intelligence has the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” commented James Weatherall, Ph.D., Head of Data Science and AI at AstraZeneca.

During his keynote, Huang provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer – which will be used to further healthcare research – the Cambridge-1.

NVIDIA has established partnerships with companies leading the fight against COVID-19 and other viruses including AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. These partners can harness Cambridge-1 for their vital research.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

And, for organisations wanting to set up their own AI supercomputers, NVIDIA has announced DGX SuperPODs as the world’s first turnkey AI infrastructure. The solution was developed from years of research for NVIDIA’s own work in healthcare, automotive, healthcare, conversational AI, recommender systems, data science and computer graphics.

While Huang has a nice kitchen, I’m sure he’d like to be back on the big stage for his GTC 2021 keynote. We’d certainly all love COVID-19 to be well and truly in the rear-view mirror.

(Photo by Elwin de Witte on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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NVIDIA is investing heavily in the UK’s accelerating AI sector. The company announced its acquisition of legendary semiconductor giant Arm for $40 billion back in September along with the promise to open a new AI centre in Cambridge.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named,” Huang said at the time. “We want to propel Arm – and the UK – to global AI leadership.”

NVIDIA promises to advance Arm’s platform in three major ways:

• NVIDIA will complement Arm partners with GPU, networking, storage and security technologies to create complete accelerated platforms.
• NVIDIA will work with Arm partners to create platforms for HPC, cloud, edge and PC — this requires chips, systems, and system software.
• NVIDIA will port the NVIDIA AI and NVIDIA RTX engines to Arm.

“Today, these capabilities are available only on x86,” Huang said, “With this initiative, Arm platforms will also be leading-edge at accelerated and AI computing.”

Huang also provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer, the Cambridge-1.

Cambridge-1 will boast 400 petaflops of AI performance and will be used by NVIDIA for its vast AI and healthcare collaborations in the UK across academia, industry, and startups.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

The company’s first partners are AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. A partnership with GSK will also see the world’s first AI drug discovery lab built in London.

“Because of the massive size of the datasets we use for drug discovery, we need to push the boundaries of hardware and develop new machine learning software,” commented Dr Kim Branson, senior vice president and global head of AI and ML at GSK.

“We’re building new algorithms and approaches in addition to bringing together the best minds at the intersection of medicine, genetics and artificial intelligence in the UK’s rich ecosystem. This new partnership with NVIDIA will also contribute additional computational power and state-of-the-art AI technology.”

While there were some natural concerns that Arm’s acquisition would see operations move from the UK to the US, NVIDIA clearly wants to build up its operations in what’s quickly becoming Europe’s AI epicentre.

(Photo by A Perry on Unsplash)

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The supercomputer will deliver eye-watering amounts of power from its 285,000 CPU cores and 10,000 GPUs (yes, it can probably even run Crysis.)

OpenAI is a non-profit that was founded by one Elon Musk to promote the ethical development of artificial intelligence technologies. Musk, however, departed OpenAI following disagreements over the company’s direction.

Back in February, Musk responded to an MIT Technology Review profile of OpenAI saying that it “should be more open,” and that all organisations “developing advanced AI should be regulated, including Tesla.”

Microsoft invested $1 billion in OpenAI last year and it seems we’re just beginning to see the fruits of that relationship. While most AIs today focus on doing single tasks well, the next wave of research is focusing on performing multiple at once.

“The exciting thing about these models is the breadth of things they’re going to enable,” said Microsoft Chief Technical Officer Kevin Scott.

“This is about being able to do a hundred exciting things in natural language processing at once and a hundred exciting things in computer vision, and when you start to see combinations of these perceptual domains, you’re going to have new applications that are hard to even imagine right now.”

So-called Artificial General Intelligence (AGI) is the ultimate goal for AI research; the point when a machine can understand or learn any task just like the human brain.

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity,” said Sam Altman, CEO, OpenAI. “Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.”

“We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

AGI will, of course, require tremendous amounts of processing power.

Microsoft and OpenAI claim their new supercomputer would rank in the top five but do not give any specific power measurements. To rank in the top five, a supercomputer would currently require more than 23,000 teraflops of performance. The current leader, the IBM Summit, reaches over 148,000 teraflops.

“As we’ve learned more and more about what we need and the different limits of all the components that make up a supercomputer, we were really able to say, ‘If we could design our dream system, what would it look like?’” said Altman. “And then Microsoft was able to build it.”

Unfortunately, for now at least, the supercomputer is built exclusively for OpenAI.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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