Demis Hassabis
👤 SpeakerAppearances Over Time
Podcast Appearances
Look, this is something Shane and I and many others here, we've had that forefront of our minds for
since before we started DeepMind.
Because we planned for success, crazy.
In 2010, no one was thinking about AI, let alone AGI.
But we already knew that if we could make progress with these systems and these ideas, the technology that would be created would be unbelievably transformative.
So we already were thinking 20 years ago about, well, what would the consequences of that be, both positive and negative?
Of course, the positive direction is amazing science, things like AlphaFold, incredible breakthroughs in health and science and maths and discovery, scientific discovery.
But then also we got to make sure these systems are sort of understandable and controllable.
And I think there's sort of several, this would be a whole sort of discussion in itself, but
There are many, many ideas that people have from much more stringent eval systems.
I think we don't have good enough evaluations and benchmarks for things like, can the system deceive you?
Can it exfiltrate its own code?
Sort of undesirable behaviors.
And then there's ideas of actually using AI, maybe narrow AIs, so not general learning ones, but systems that are specialized for a domain to help us as the
the human scientists, analyze and summarize what the more general system is doing.
Narrow AI tools.
I think that there's a lot of promise in creating hardened sandboxes or simulations that are hardened with cybersecurity arrangements around the simulation, both to keep the AI in, but also as cybersecurity to keep hackers out.
Then you could experiment a lot more
freely within that sandbox domain.
And I think a lot of these ideas are, and there's many, many others, including the analysis stuff we talked about earlier, where can we analyze and understand what the concepts are that this system is building, what the representations are, so maybe they're not so alien to us and we can actually keep track of the kind of knowledge that it's building.