I don’t think that’s a good way to think about things. I think evolution is much more closely analogous to a search over neural architectures, hyperparameters, reward / loss functions, and other things like that, and not like “pretraining” for within-lifetime learning. See my post “Learning From Scratch” In The Brain.
I think the brain has parts that are non-pretrained learning algorithms (cortex, striatum, cerebellum), and I think that the brain has other parts that are not learning algorithms at all (hypothalamus, brainstem).
The hypothalamus & brainstem do lots of things, but I don’t think I would describe them as “knowing” anything at all.
Like, I think there’s a little innate part of the brainstem that triggers vomiting—when it gets certain combinations of input signals then it triggers certain muscle movements and hormones etc. Does that mean that the brainstem “knows when and how to vomit”? I vote for “not really”. I would rather say “the brainstem has an innate vomiting reflex”, or maybe “the brainstem contains a little simple machine that will set off vomiting under thus-and-such circumstance” or something like that.
I think the brainstem “knows when and how to vomit” in the same sense that a mechanical thermostat “knows when to turn on the heater”, and the same sense as a ribosome “knows how to build proteins”, which is to say, that would be a pretty weird use of the word “know”. I think I’d rather reserve the term “knowing” for the information stored in the cortex and related structures. For example, if we use the everyday sense of “know”, then it’s entirely possible for someone to not “know” that vomiting exists at all (i.e. if they’ve never done it or seen it or heard of it), even though that little reflex circuit is present and functional in their own brainstem.
I think that gyri are mostly hard coded by evolution and given how strongly they restrict the computation space that the cortical area can learn, one could consider the cortex to be heavily pre trained by evolution.
Studying geometrical gyri correlation with psychiatry is an ongoing hot topic
Neural network architecture is very different from neural network pretraining. Why do you think gyri are related to the latter not the former? (I think they’re related to the former.)
If all humans have about as many neurons in a the gyri that is hardwired to receive from the eyes, it seems safe to assume that the vast majority of humans will end up with this gyri extracting the same features.
Hence my view is that evolution, by imposing a few hardwired connections and gyri geometries, gives an enormous bias in the space of possible networks, which is similar to what pretraining is.
In essence evolution gives a foundational model that we fine tune with our own experiences.
by imposing a few hardwired connections and gyri geometries, gives an enormous bias in the space of possible networks, which is similar to what pretraining is.
A 12-layer ConvNet versus a 12-layer fully-connected MLP, given the same data, will wind up with very different trained models that do different things. In that sense, switching from MLP to ConvNet “gives an enormous bias in the space of possible networks”.
But “using a ConvNet” is NOT pretraining, right? You can pretrain a ConvNet (just like you can pretrain anything), but the ConvNet architecture itself is not an example of pretraining.
If all humans have about as many neurons in a the gyri that is hardwired to receive from the eyes, it seems safe to assume that the vast majority of humans will end up with this gyri extracting the same features.
I think it’s true to some extent that two randomly-initialized ML models (with two different random seeds), with similar neural architecture, similar hyperparameters, similar loss functions, similar learning rules, and similar data, may wind up building two similar trained models at the end of the day. And I think that this is an important dynamic to have in mind when we think about humans, especially things like human cross-cultural universals. But that fact is NOT related to pretraining either, right? I’m not talking about pretrained models at all, I’m talking about randomly-initialized models in this paragraph.
How do you define the word “pretraining”? I’m concerned that you’re using the word in a different way than me, and that one of us is misunderstanding standard terminology.
I agree that it’s probably terminology that is the culprit here. It’s entirely my fault: I was using the word pretraining loosely and meant more something like that hyper parameters (number of layers, inputs, outputs, activation fn, loss) are “learned” by evolution. Leaving to us poor creatures only the task to prune neurons and adjust the synaptic weights.
The reason I was thinking at it this way is that I’ve been reading about NEAT recently, an algorithm that uses a genetic algorithm to learn an architecture as well as train selected architecture. A bit like evolution?
To rephrase my initial point: evolution does its part of the heavy lifting for finding the right brain to live on earth. This shrinks tremendously the space of computation a human has to explore in his lifetime to have a brain fitted to the environnement. This “shrinking of the space” is kinda is like a strong bias towards certain computation. And model pretraining is having the weights of the network already initialized at a value that “already works”, kinda like a strong bias too. Hence the link in my mind.
But yeah, evolution does not give us synaptic weights that work so pretraining is not the right word. Unless you are thinking about learned architectures, in that case my point can somewhat work I think.
Don’t you also need to include the millions of brains over millions of years of pre-training during evolution?
I don’t think that’s a good way to think about things. I think evolution is much more closely analogous to a search over neural architectures, hyperparameters, reward / loss functions, and other things like that, and not like “pretraining” for within-lifetime learning. See my post “Learning From Scratch” In The Brain.
I’ve only skimmed your post, but is part of the claim that the things that brains instinctually know are too minor to count?
I think the brain has parts that are non-pretrained learning algorithms (cortex, striatum, cerebellum), and I think that the brain has other parts that are not learning algorithms at all (hypothalamus, brainstem).
The hypothalamus & brainstem do lots of things, but I don’t think I would describe them as “knowing” anything at all.
Like, I think there’s a little innate part of the brainstem that triggers vomiting—when it gets certain combinations of input signals then it triggers certain muscle movements and hormones etc. Does that mean that the brainstem “knows when and how to vomit”? I vote for “not really”. I would rather say “the brainstem has an innate vomiting reflex”, or maybe “the brainstem contains a little simple machine that will set off vomiting under thus-and-such circumstance” or something like that.
I think the brainstem “knows when and how to vomit” in the same sense that a mechanical thermostat “knows when to turn on the heater”, and the same sense as a ribosome “knows how to build proteins”, which is to say, that would be a pretty weird use of the word “know”. I think I’d rather reserve the term “knowing” for the information stored in the cortex and related structures. For example, if we use the everyday sense of “know”, then it’s entirely possible for someone to not “know” that vomiting exists at all (i.e. if they’ve never done it or seen it or heard of it), even though that little reflex circuit is present and functional in their own brainstem.
I think that gyri are mostly hard coded by evolution and given how strongly they restrict the computation space that the cortical area can learn, one could consider the cortex to be heavily pre trained by evolution.
Studying geometrical gyri correlation with psychiatry is an ongoing hot topic
Neural network architecture is very different from neural network pretraining. Why do you think gyri are related to the latter not the former? (I think they’re related to the former.)
If all humans have about as many neurons in a the gyri that is hardwired to receive from the eyes, it seems safe to assume that the vast majority of humans will end up with this gyri extracting the same features.
Hence my view is that evolution, by imposing a few hardwired connections and gyri geometries, gives an enormous bias in the space of possible networks, which is similar to what pretraining is.
In essence evolution gives a foundational model that we fine tune with our own experiences.
What do you think? Does that make sense?
No, it doesn’t make sense…
A 12-layer ConvNet versus a 12-layer fully-connected MLP, given the same data, will wind up with very different trained models that do different things. In that sense, switching from MLP to ConvNet “gives an enormous bias in the space of possible networks”.
But “using a ConvNet” is NOT pretraining, right? You can pretrain a ConvNet (just like you can pretrain anything), but the ConvNet architecture itself is not an example of pretraining.
I think it’s true to some extent that two randomly-initialized ML models (with two different random seeds), with similar neural architecture, similar hyperparameters, similar loss functions, similar learning rules, and similar data, may wind up building two similar trained models at the end of the day. And I think that this is an important dynamic to have in mind when we think about humans, especially things like human cross-cultural universals. But that fact is NOT related to pretraining either, right? I’m not talking about pretrained models at all, I’m talking about randomly-initialized models in this paragraph.
How do you define the word “pretraining”? I’m concerned that you’re using the word in a different way than me, and that one of us is misunderstanding standard terminology.
edit: rereading your above comments. I see that I should have made clear that I was thinking more about learned architectures. In which case we apparently agree is I meant what you said in https://www.lesswrong.com/posts/ftEvHLAXia8Cm9W5a/data-and-tokens-a-30-year-old-human-trains-on?commentId=4QtpAo3XXsbeWt4NC
Thank your for taking the time.
I agree that it’s probably terminology that is the culprit here. It’s entirely my fault: I was using the word pretraining loosely and meant more something like that hyper parameters (number of layers, inputs, outputs, activation fn, loss) are “learned” by evolution. Leaving to us poor creatures only the task to prune neurons and adjust the synaptic weights.
The reason I was thinking at it this way is that I’ve been reading about NEAT recently, an algorithm that uses a genetic algorithm to learn an architecture as well as train selected architecture. A bit like evolution?
To rephrase my initial point: evolution does its part of the heavy lifting for finding the right brain to live on earth. This shrinks tremendously the space of computation a human has to explore in his lifetime to have a brain fitted to the environnement. This “shrinking of the space” is kinda is like a strong bias towards certain computation. And model pretraining is having the weights of the network already initialized at a value that “already works”, kinda like a strong bias too. Hence the link in my mind.
But yeah, evolution does not give us synaptic weights that work so pretraining is not the right word. Unless you are thinking about learned architectures, in that case my point can somewhat work I think.