AI

If our future is to count as a utopia, we cannot allow a massive oppressed class of hyper-sentient, uncomfortable digital beings. We want it to be good for all kinds of minds.

Philosopher & Director of Future of Humanity Institute at Oxford

We're not really creating a specific artefact—we're setting a direction for a process. There's no upper limit to that process; we're simply saying we'll create something capable of becoming smarter and smarter.

AI Safety Researcher & Director of Cybersecurity Lab at University of Louisville

Much of our future work may end up being about convincing them that we are conscious, and worth keeping around.

AI Safety Researcher & Director of Cybersecurity Lab at University of Louisville

Arguably, it could be more comparable to the rise of Homo sapiens itself, or even to the origin of life on Earth.

Philosopher & Director of Future of Humanity Institute at Oxford

It's the ultimate invention—the last one we'll ever need to make—because once we have AI that is generally intelligent and then superintelligent, it will do the inventing far better than we can. In that sense, it's a handing over of the baton.

Philosopher & Director of Future of Humanity Institute at Oxford

We're not just over-reliant—we're wholly reliant—on American technology across the entire stack. Our data sits in American cloud infrastructure; our hardware is American designed; our software and operating systems are overwhelmingly American; most of the AI systems people interact with are American, and so on.

Former UK Deputy Prime Minister & Liberal Democrat Leader

You can have the best AI in the world and the best robots in the world, but if they aren't integrated well with the humans, then you will lose.

If you tried to achieve that purely through informal methods—where hallucination is a persistent risk—getting to the same level of consistent correctness would likely require a lot more research effort and resources.

The third and deepest reason this matters—why it's not just commercially meaningful but potentially world-changing—is the ability to bridge different levels of abstraction.

A model that's really strong at mathematical reasoning is likely to be strong at coding. And a model that's excellent at both math and code is often very good at analysing the nuts and bolts of legal reasoning as well.

What we envision is humans using informal reasoning and intuition as a powerful guide, with formal systems then verifying those ideas. In this way, the formal system grounds high-level intuition.

We see math as code and code as math. The real magic, and the key transition, comes from combining AI, programming languages, and mathematics—bringing all three pillars together.

Think about a rabbit sitting in a field. If that rabbit saw a hawk circling above and decided to wait for the back-propagation step before responding, it would be dead. The better you model the world, and the faster you can act on that model, the more likely your genes are to survive.

The things that are trivially easy for humans turn out to be extraordinarily difficult for machines, and vice versa. I cannot do the square root of a large number in my head, but my pocket calculator can do that instantly. But my pocket calculator still cannot make me a cup of coffee.

I think intelligence is best understood as an entity that has the ability to improve a metric through repeated exposure over time. By that definition, machine learning algorithms are learning systems — they get better with more data and more exposures.

The brain is definitely not doing computation in the purest sense. We are not crunching numbers in binary ones and zeros in our heads. A more important question is: what are your inputs, what output do you want, and how intelligently can the system get from one to the other?