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In new research, Google DeepMind has demonstrated that its AI can help accelerate the development of quantum computers taking one step further in combining two of the most disruptive technologies.

DeepMind worked together with UK-based Quantinuum to solve a key challenge in fault-tolerant quantum computers: reducing the number of T gates.

T gates are essential in implementing a quantum circuit a network of gates that manipulates qubits to generate algorithms. However, T gates are also the most expensive and most resource-intensive gates of the network.

To address this, the team developed AlphaTensor-Quantum, an extension of DeepMinds AlphaTensor, the first AI system that can discover efficient algorithms for tasks such as matrix multiplication.

AlphaTensor-Quantum is an AI model that leverages the relationship between optimising T-count and tensor decomposition, using deep reinforcement learning.

In contrast to existing approaches, the model can incorporate domain-specific knowledge about quantum computation as well as use gadgetisation techniques, which implement alternative gates by introducing additional qubits and operations. This way, the AI can significantly reduce the number of T gates.

According to the researchers, AlphaTensor-Quantum outperforms existing systems for T-count optimisation and is as efficient as the best human-designed solutions across numerous applications. It can also save hundreds of hours of research by optimising the process in a fully automated way, the team says in the paper.

On a representative standard benchmark set of circuits, AlphaTensor-Quantum improves the cost by 37% on average on the existing state of the art obtained by human-crafted heuristics, Konstantinos Meichanetzidis, head of product development at Quantinuum, told TNW.

DeepMind and Quantinuum envision applications in quantum chemistry and related fields, and suggest that possible future research could focus on improving the algorithms neural network architecture.

In general, the method can be readily applied to any given circuit independent of application, and the improvements over the baselines correspond directly to space and time cost of the quantum algorithm under consideration, Meichanetzidis said.

Update (11:30AM CET, February 27, 2024): The article has been updated to include the comments from Quantinuum.

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Google DeepMind taps the power of its AI to accelerate quantum computers - TNW

In 2010, Demis Hassabis co-founded what would become one of the most influential AI labs in the world: DeepMind, named after the term deep learning. The company, which Google acquired in 2014, had grand designs for building artificial general intelligence, or AGI.

How is that endeavor going?

Its looking like its going to be a more gradual process rather than a step function, he said during a keynote fireside chat at Mobile World Congress 2024 in Barcelona, Spain. Todays AI systems are becoming incrementally more powerful as compute, techniques and data used are scaled up.

It is possible that significant advances can come in the next few years with new innovations to improve AIs ability to plan, remember and use tools things current-generation AI systems are missing. In the meantime, AI advances are proving to be useful already in many other endeavors.

The CEO defines AGI as a system that can perform almost any cognitive task that humans can. He said there is a need for a human reference point is because the human brain is the only proof we have maybe in the universe that general intelligence is possible.

But how will we know AGI when we see it? It is a question hotly debated in the field of AI. For Hassabis, it either may be obvious when it appears or may require considerable tests to determine.

Related:Google DeepMind CEO: AGI is Coming in a Few Years

One way is to actually test the systems on thousands and thousands of tasks that humans do and see if it passes a certain threshold on all of those tasks. And the more tasks you put into that test set, the more sure you can be you have the general space covered.

From left: Wireds Steven Levy and Google DeepMind CEO Demis Hassabis

Amid its quest to develop AGI, it was another AI system that helped cement DeepMind as a key player in the AI space: AlphaFold.

The system predicts protein structures and in 2022, the model was used to map nearly all of the 200 million known proteins.

Commenting on the project at MWC, Hassabis used AlphaFold as an example of a non-general AI system that could be used to further human knowledge.

He said it would have taken a billion years of having a person with a doctorate to map every known protein something his team did in just one year.

Over a million researchers have used the model, according to the Google DeepMind CEO, but he wants the model to power drug discovery.

And that is a goal parent company Alphabet has in mind it formed Isomorphic Labs in 2021 to reimagine drug discovery with AI systems like AlphaFold 2.

Isomorphic penned deals with pharma giants Novartis and Eli Lilly in January to use AI to design new drugs. According to Hassabis, drugs designed by AI will hit clinics in the next couple of years.

Related:DeepMind AI System Predicts Structure of Nearly All Known Proteins

It's really having a material impact now on drug discovery, and I hope that drug discovery will shrink from 10 years to discover one drug down to maybe a matter of months to discover drugs to cure these terrible diseases.

Hassabis noted that most of the major AI innovations of the past decade came from Google Research, Brain and DeepMind. OpenAI actually took these ideas and techniques and applied Silicon Valley growth mentality, hacker mentality to it, and scaled it to sort of maximum speed, he said.

Also, OpenAIs unusual path to success with its models was not in coming up with a new innovation but rather by scaling current innovation.

I dont think anyone predicted it, maybe even including them, that these new capabilities would just emerge just through scale, not for inventing some new innovation, but actually just sort of scaling, Hassabis said.

And its quite unusual in the history of most scientific technology fields where you get step-changing capability by doing the same thing, just bigger that doesnt happen very often. Usually, you just get incremental capabilities, and normally you have to have some new insight or some new flash of inspiration, or some new breakthrough in order to get a step change. And that wasnt the case here.

The other surprising thing was that with ChatGPT, the general public seems to be ready to use these systems even though they clearly have flaws hallucinations, theyre not factual, Hassabis said.

Googles thinking was these systems needed to be 100 times more accurate before releasing them but OpenAI just released it and it turns out millions of people found value out of that, he added. It didnt have to be 100% accurate for there to be some valuable use cases there, so I think that was surprising for the whole industry.

Hassabis said they also thought these systems would have narrower use cases for scientists and other specific professions. But actually, the general public was willing to use slightly messier systems and find value and use cases for them. So that then precipitated a change in (Googles) outlook.

This led to Googles merging of Google Brain, a team within Google Research, with DeepMind in April 2023. The goal was to combine all of our compute together and engineering talent together to build the biggest possible things we can, he said. Gemini, our most advanced, most capable AI model, is one of the fruits of that combination.

What does Hassabis believe the future of AI will look like? He said last May that DeepMinds dream of AGI may be coming in a few years, but for now, his team is exploring new areas to apply AI.

One of those areas is in material sciences using AI to help discover new types of materials.

I dream of one day discovering room temperature superconductor it may exist in chemical space, but we just haven't found it as human chemists and material scientists.

Google DeepMind is also looking at applying AI to weather prediction and climate change, as well as mathematics.

He also said that the next generation of smart assistants will be useful in peoples daily lives rather than sort of gimmicky as they were in the previous generation.

Users are already seeing smarter and more adaptable phones, sporting Googles Gemini features and a new capability to search just by encircling an image.

But in five or more years, is the phone even really going to be the perfect phone factor? he asked. Maybe we need glasses or some other things so that the AI system can actually see a bit of the context that you're in to be even more helpful in your daily life.

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Google DeepMind CEO on AGI, OpenAI and Beyond MWC 2024 - AI Business

Tuesday 27 February 2024 6:00 am

The chief executive of Google DeepMind has said artificial intelligence (AI) could be designing drugs in clinics within the next couple of years.

Demis Hassabis, who founded DeepMind in the UK in 2010, said AI will have a material impact on drug discovery.

I think in the next couple of years were going to start seeing AI design drugs in the clinic, he said, speaking to an audience of global telecoms industry players gathered at the Mobile World Congress in Barcelona on Monday afternoon.

Deepmind, bought by Google in 2014, is an AI research lab and the creator of a system called Alphafold that can predict protein structures, potentially accelerating drug discovery.

If you know the structure of a protein it also means that you could target a drug compound to bind to the surface of the relevant bit of the surface of the proteins structure, he explained.

In 2021, Hassabis also founded the London-based drug discovery company Isomorphic Labs, owned by Google parent company Alphabet.

It uses AI to generate new chemical compounds that bind specifically to the exact part of the protein but no other protein, minimising side effects on the body.

I hope that drug discovery will shrink from an average of 10 years to design one drug to maybe a matter of months to discover drugs to cure these terrible diseases, Hassabis added.

At the start of this year, Isomorphic signed large deals with two of the biggest pharma companies in the world, Eli Lilly and Novartis, worth up to $3 billion, to work on several real-world design programmes.

It comes as an increasing number of health tech companies are attempting to use AI to solve critical medical delays. One example is Cambridge-based Nuclera, which helps accelerate drug discovery by rapidly finding the correct proteins needed to create new medicines and vaccines.

Nuclera says it can reduce the lengthy process, which can take months or even years in some cases, down to days.

There are nearly 4,500 health tech companies in the UK which have a combined turnover of 30bn, according to the Association of British Healthtech Industries.

A recent report said: Responsible AI use has immense potential and its value in the health sector was widely discussed during the recent UK AI Safety Summit. Industry overwhelmingly felt that AI has the greatest potential in disease and diagnosis detection.

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AI will design drugs in the next couple of years, says Google Deepmind boss - City A.M.