Demis Hassabis

But you're talking about two, you know, world class organizations, long storied histories of inventing many, many important things, you know, from deep reinforcement learning to transformers.

And so it's very exciting, actually, pulling all of that together and, and collaborating much more closely, we always used to be collaborating, but more on a on a

on a sort of project by project basis versus a much deeper, broader collaboration like we have now.

And Gemini is the first fruit of that collaboration, including the name Gemini actually, implying twins.

And of course, a lot of other things are made more efficient like

pooling compute resources together and ideas and engineering, which I think at the stage we're at now where there's huge amounts of world class engineering that has to go on to build the frontier systems, I think it makes sense to to coordinate that more closely.

Yeah, no, I think overall, and this is why, you know, I think one of the reasons we joined forces with Google back in 2014 was I think the entirety of Google and Alphabet, not just brain and deep mind, take these questions very seriously of responsibility.

And, you know, our kind of mantra is to try and be bold and responsible with these systems.

So, you know, I would I would class it as I'm obviously a huge techno optimist, but I want us to be cautious with that, given the transformative power of what we're bringing, bringing into the world, you know, collectively.

And I think it's important, you know, I think it's going to be one of the most important technologies humanity will ever invent.

So we've got to put, you know, all our efforts into getting this right and be thoughtful and sort of.

also humble about what we know and don't know about the systems that are coming and the uncertainties around that.

And in my view, the only sensible approach when you have huge uncertainty is to be sort of cautiously optimistic and use the scientific method to try and have as much foresight and understanding about what's coming down the line and the consequences of that before it happens.

You know, you don't want to be live AB testing out in the world with these very consequential systems because unintended consequences may be quite severe.

So, you know, I want us to move away as a field from a sort of move fast and break things attitude, which is, you know, maybe served the value very well in the past and obviously created...

uh important innovations um but but i think in this case you know we want to be uh uh bold with the with the positive things that it can do and make sure we realize things like medicine and science and advancing all of those things whilst being um you know responsible and thoughtful with with uh as far as possible with with mitigating the risks

Well, first, the secure model part, I think we've covered with the cybersecurity and make sure that's well-classed and you're monitoring all those things.

I think if a capability was discovered like that through red teaming or external testing by government institutes or academia or whatever, independent testers, then we would have to fix that

that loophole depending on what it was.

If that required more a different kind of perhaps constitution or different guardrails or more RLHF to avoid that or removing some training data.