Demis was jointly awarded the Nobel Prize in Chemistry last year in recognition of the major advances made possible by AlphaFold2_._
And because these biological structures exist across much of life on Earth, he said new avenues of exploration had been opened up – in a wide range of fields – including climate, agriculture, disease, and drug discovery.
“The mission of DeepMind mind from the beginning was about building AI responsibly to benefit humanity, but the way we used to articulate it when we started out was a two-step process, step 1 – solve Artificial Intelligence, step 2 – use it to solve everything else.
“If I look at all the work we've done in the last 15 years, first of all our games work, and then now with the scientific work that we’ve been working on, it’s all about making this search ‘tractable’. You have this incredibly complex problem, and there’s many possible solutions to the problem, and you've got to find the optimal solution – kind of like a needle and a haystack. And you can't do it by brute force, so you have to learn this neural network model, so that you can efficiently guide the search and find the optimal solution.
“I think AI will be applicable to pretty much every field, and I think there are many, many advances to be made over the next 5-10 years by doing that.”
Discussing the path to Artificial General Intelligence (AGI) Demis said that Google DeepMind was making advances in all areas of AI’s understanding of the physics of the real world, and pointed to its new Veo 2 state-of-the-art video generation tool, which generates videos from a text description, and Genie 2, which can generate a computer game based on a single prompt.
And stressing the importance of AI safety, and the responsibility that came with building these kinds of transformative systems and technologies, he explained how Google DeepMind’s SynthID tool invisibly watermarked AI-generated content, which can then be detected as synthetically generated image, audio, text or video.
“AI has this incredible potential to help with our greatest challenges, from climate to health. But it is going to affect everyone, so I think it’s really important that we engage with a wide range of stakeholders from society. And I think that’s going to become increasingly important given the exponential improvement that we’re seeing with these technologies.”
Looking to the future, Demis said he was “very excited” about the next generation of virtual assistant technology, or ‘universal assistants’ as he described Google DeepMind’s work on a research prototype assistant that can understand the world around us.
“We call it ‘Project Astra’, where you have it on your phone or some other devices, maybe glasses. It’s an assistant you can take around with you in the real world and it helps you in everyday life.”
He said the next step in AI was building planning systems like we saw with AlphaGo, which can search and find good solutions to problems, ‘on top of’ world models like Google Gemini, which understand how the real world works. Combined, he said, they can plan and achieve things in the real world.
“That’s key to things like robotics working, which I think in the next two or three years is going to be a huge area that’s going to have massive advances.”
Bringing his talk to an end, Demis described himself as “Turing’s champion” – and posed the question “how far can these Turing machines and the idea of classical computing go?”
“There are a lot of things that are thought to require quantum computing to solve. My conjecture is that actually classical Turing machines that these types of AI systems are built on can do a lot more than we previously gave them credit for.
“If you think about AlphaFold and protein folding – proteins are quantum systems, they operate at the atomic scale and one might think you need quantum simulations to actually be able to find the structures of proteins. And yet we were able to approximate those solutions with our neural networks.
“And so one potential idea is that any pattern that can be generated or found in nature can be efficiently discovered and modelled by one of these classical learning algorithms. And if that turns out to be true, it has all sorts of implications for quantum mechanics and actually fundamental physics, which is something that I hope to explore. Maybe these classical systems will help us uncover what the true nature of reality might be.
“And that leads me back to the whole reason I started my path on AI many, many years ago. I always believed that AGI built in this way could be the ultimate general purpose tool to understand the universe around us and our place in it.”
**Words:** Stephen Bevan
**Published:** 24th March, 2025
The text in this work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)