Guillaume Verdon
speaker
342 appearances
1 recordings
1 series
first heard Dec 2023
last heard Dec 2023
Guillaume Verdon’s voice in public audio — every appearance, attributed to the second.
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Like, let's say you have a company, you know, if you have a company, I don't know, of 10,000 people that all report to the CEO, even if that CEO is an AI, I think it would struggle to fuse all the information that is coming to it and then predict the whole system and then to enact its will. What has emerged in nature and in corporations and all sorts of systems
is a notion of sort of hierarchical cybernetic control, right? You have, you know, in a company it would be, you have like the individual contributors, they're self-interested and they're trying to achieve their tasks and they have a fine... in terms of time and space, if you will, control loop and field of perception, right? They have their code base.
Let's say you're in a software company, they have their code base, they iterate it on it intraday, right? And then the management may be checks in, it has a wider scope. It has, let's say, five reports, right? And then it samples each person's update once per week. And then you can go up the chain and you have larger timescale and greater scope.
And that seems to have emerged as sort of the optimal way to control systems. And really... That's what capitalism gives us, right? You have these hierarchies and you can even have like parent companies and so on. And so that is far more fault tolerant. In quantum computing, that's my field I came from, we have a concept of this fault tolerance and quantum error correction, right?
Quantum error correction is detecting a fault that came from noise, predicting how it's propagated through the system and then correcting it, right? So it's a cybernetic loop. And it turns out that decoders that are hierarchical, and at each level the hierarchy are local, perform the best by far and are far more fault tolerant. And the reason is if you have a non-local decoder,
then you have one fault at this control node and the whole system sort of crashes. Similarly to if you have, you know, one CEO that everybody reports to and that CEO goes on vacation, the whole company comes to their crawl.
And so to me, I think that yes, we're seeing a tendency towards centralization of AI, but I think there's going to be a correction over time where intelligence is going to go closer to the perception and we're going to break up AI into smaller subsystems that communicate with one another and form a sort of meta system.
centralized locus of control, yeah.
Yeah.
Yeah, just like, you know, in a company, you may have, like, two units working on similar technology and competing with one another, and you prune the one that performs not as well, right? And that's a sort of selection process for a tree, or a product gets killed, right? And then a whole org gets...
And that's this process of trying new things and shedding old things that didn't work is what gives us adaptability and helps us converge on the technologies and things to do that are most good.
I mean, that's been the case so far, right? We have OpenAI, we have Anthropic, now we have XAI. We had Meta even for open source, and now we have Mistral, which is highly competitive. And so that's the beauty of capitalism. You don't have to trust any one party too much because... we're kind of always hedging our bets at every level. There's always competition.
And that's the most beautiful thing to me, at least, is that the whole system is always shifting and always adapting. And maintaining that dynamism is how we avoid tyranny, right? Making sure that
Everyone has access to these tools, to these models and can contribute to the research, avoids a sort of neural tyranny where very few people have control over AI for the world and use it to oppress those around them.
I would say intelligence and computation aren't quite the same thing. I think that the universe is very much doing a quantum computation. If you had access to all the degrees of freedom, and a very, very, very large quantum computer with many, many, many qubits, let's say a few qubits per Planck volume,
which is more or less the pixels we have, then you'd be able to simulate the whole universe on a sufficiently large quantum computer, assuming you're looking at a finite volume, of course, of the universe. I think that, at least to me, intelligence is the... I go back to cybernetics, the ability to perceive, predict, and control our world. But really, it's
Nowadays, it seems like a lot of intelligence we use is more about compression. It's about operationalizing information theory. In information theory, you have the notion of entropy of a distribution or a system. And entropy tells you that you need this many bits to encode this distribution or this subsystem if you had the most optimal code.
And AI, at least the way we do it today for LLMs and for quantum, is very much trying to minimize relative entropy between are models of the world and the world, distributions from the world. And so, we're learning, we're searching over the space of computations to process the world to find that compressed representation that has distilled all the variance and noise and entropy, right? And
Originally, I came to quantum machine learning from the study of black holes because the entropy of black holes is very interesting. In a sense, they're physically the most dense objects in the universe. You can't pack more information spatially, any more densely than a black hole. And so I was wondering, how do black holes actually encode information? What is their compression code?
And so that got me into the space of algorithms to search over space of quantum codes. And it got me actually into also how do you acquire quantum information from the world, right? So something I've worked on, this is public now, is quantum analog digital conversion.
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