Public evidence record
Published podcast speaker
Claim ledger
28 transcript-backed records
01 / evaluation
“Well, it’s because the Learning Subsystem is a powerful learning algorithm that does have generalization, that is capable of generalization.”
02 / evaluation
“The cortex inherently has the ability to generalize because it’s just predicting based on these very abstract variables and all these integrated information that it has.”
03 / uncertainty
“We don’t know how they’re all connected and exactly what they do or what the circuits are or what they mean, but you can just quantify how many different kinds of cells there are with sequencing the RNA.”
04 / belief
“First of all, I think the probabilistic AI people would be like, of course you need test-time compute, because this inference problem is really hard and the only ways we know how to do it involve lots of test-time compute.”
05 / belief
“I would lean somewhat toward the latter. I think a mouse has a lot of similarity in terms of cortex as a human.”
06 / commitment
“You can’t do interp on a hypothetical model-based reinforcement algorithm like the brain that we will eventually converge to when we do AGI.”
07 / belief
“I think there’s a lot of ways to do moonshot neuroscience companies that would never get you the connectome.”
08 / belief
“What I think we should do is we should describe the brain more in that language of things like architectures, learning rules, initializations, rather than trying to find the Golden Gate Bridge circuit and saying exactly how this neuron actually… That’s going to be some incredibly complicated learned pattern.”
09 / uncertainty
“I mean, I would take the entirety of your collective podcast with everyone as showing the distribution of these things. I don’t know.”
10 / belief
“I think you either have to have special cell types or you have to somehow otherwise get special wiring rules that evolution can say this neuron needs to wire to this neuron, without any learning. And the way that that is most likely to happen, I think, is that those cells express different receptors and proteins that say, “Okay, when this one comes in contact with this one, let’s form a synapse.”
11 / uncertainty
“I don’t know how transformer-like it is or if there’s anything analogous to that attention.”
12 / belief
“If we believe that all of this can come from evolution, the outer loop can be extremely not foresighted.”
13 / belief
“I think the reason that I think that we might be onto something is that the AIs we’re making based on these ideas are working surprisingly well.”
14 / belief
“There might be other ways of learning, energy-based models or other things like that, that you can imagine that is involved in being able to do this and that the brain has that. But I think there’s a version of it where what the brain does is crappy versions of backprop to learn to predict through a few layers and that it’s kind of like a multimodal foundation model.”
15 / belief
“Maybe there’s like group selection or whatever of these things is like more model-free. But now I think culture, well, it stores some of the model.”
16 / belief
“I think I would somewhat dispute it. We have some description of what the LLM is fundamentally doing.”
17 / uncertainty
“What are mine? I don’t know, I’m just watching your podcast. I’m trying to understand the distribution.”
18 / belief
“I think if AlphaZero, and model-based RL and all these other things that were being worked on 10 years ago, had been giving us the GPT-5 type capabilities, then I would be like, “Oh wow, we’re both in the right paradigm and seeing the results a priori.”
19 / evaluation
“, and each student pursuing a totally different direction or thesis that you see in universities is not also really key. But I think some amount of scalable infrastructure is missing in essentially every area of science, even math, which is crazy.”
20 / commitment
“If I can build that Hubble Space Telescope, then I will unblock all the other researchers in my field or some path of technological progress in the way that the Hubble Space Telescope lifted the boats and improved the life of every astronomer.”
21 / uncertainty
“I don’t know enough details of how hard these things are to search for, and I’m not sure anyone can fully predict that, just as we couldn’t exactly predict when Go would be solved or something like that.”
22 / evaluation
“If I channel Steve Byrnes more, I think he’s very concerned that the minimum viable things in the Steering Subsystem that you need to get something smart is way less than the minimum viable set of things you need for it to have human-like social instincts and ethics and stuff like that.”
23 / evaluation
“I don’t know, but I’m not a believer in the radical, “Oh, actually memory is not synapses mostly, or learning is mostly genetic changes” or something like that.”
24 / evaluation
“The problem is learning even the most basic things by a series of bespoke experiments takes an incredibly long time.”
25 / evaluation
“You had more false failures because you didn’t get something about the assembly code, rather than the essential thing of was your concept right.”
26 / evaluation
“I think evolution may have built a lot of complexity into the loss functions actually, many different loss functions for different areas turned on at different stages of development.”
27 / evaluation
“If we don’t know if it’s doing a backprop-like learning, and we don’t know if it’s doing energy-based models, and we don’t know how these areas are even connected in the first place, it’s very hard to really get to the ground truth of this.”
28 / preference
“Because if we’re going to be able to seek status in the tribe or learn from knowledgeable people, as you said, or things like that, exchange knowledge and skills with friends but not with enemies… We have to learn all this stuff.”