Is this model address difference between two hemispheres?
Insofar as there are differences between the two hemispheres—and I don’t know much about that—I would treat it like any other difference between different parts of the cortex (Section 2), i.e. stemming from (1) the innate large-scale initial wiring diagram, and/or (2) differences in “hyperparameters”.
There’s a lot that can be said about how an adult neocortex represents and processes information—the dorsal and ventral streams, how do Wernicke’s area and Broca’s area interact in speech processing, etc. etc. ad infinitum. You could spend your life reading papers about this kind of stuff!! It’s one of the main activities of modern cognitive neuroscience. And you’ll notice that I said nothing whatsoever about that. Why not?
I guess there’s a spectrum of how to think about this whole field of inquiry:
On one end of the spectrum (the Gary Marcus / Steven Pinker end), this line of inquiry is directly attacking how the brain works, so obviously the way to understand the brain is to work out all these different representations and mechanisms and data flows etc.
On the opposite end of the spectrum (maybe the “cartoonish connectionist” end?), this whole field is just like the OpenAI Microscope project. There is a simple, generic learning algorithm, and all this rich structure—dorsal and ventral streams, phoneme processing in such-and-such area, etc.—just naturally pops out of the generic learning algorithm. So if your goal is just to make artificial intelligence, this whole field of inquiry is entirely unnecessary—in the same way that you don’t need to study the OpenAI Microscope project in order to train and use a ConvNet image classifier. (Of course maybe your goal is something else, like understanding adult human cognition, in which case this field is still worth studying.)
I’m not all the way at the “cartoonish connectionist” end of the spectrum, because I appreciate the importance of the initial large-scale wiring diagram and the hyperparameters. But I think I’m quite a bit farther in that direction than is the median cognitive neuroscientist. (I’m not alone out here … just in the minority.) So I get more excited than mainstream neuroscientists by low-level learning algorithm details, and less excited than mainstream neuroscientists about things like hemispherical specialization, phoneme processing chains, dorsal and ventral streams, and all that kind of stuff. And yeah, I didn’t talk about it at all in this blog post.
What about long term-memory? Is it part of neocortex?
There’s a lot about how the neocortex learning algorithm works that I didn’t talk about, and indeed a lot that is unknown, and certainly a lot that I don’t know! For example, the generative models need to come from somewhere!
My impression is that the hippocampus is optimized to rapidly memorize arbitrary high-level patterns, but it only holds on to those memories for like a couple years, during which time it recalls them when appropriate to help the neocortex deeply embed that new knowledge into its world model, with appropriate connections and relationships to other knowledge. So the final storage space for long-term memory is the neocortex.
How this model explain the phenomenon of night dreams?
I don’t know. I assume it somehow helps optimize the set of generative models and their connections.
I guess dreaming could also have a biological purpose but not a computational purpose (e.g., some homeostatic neuron-maintenance process, that makes the neurons fire as an unintended side-effect). I don’t think that’s particularly likely, but it’s possible. Beats me.
Thanks. I think that a plausible explanation of dreaming is generating of virtual training environments where an agent is training to behave in the edge cases, on which it is too costly to train in real life or in real world games. That is why the generic form of the dreams is nightmare: like, a lion attack me, or I am on stage and forget my speech.
From “technical” point view, dream generation seems rather simple: if the brain has world-model generation engine, it could generate predictions without any inputs, and it will look like an dream.
Where is “human values” in this model? If we give this model to an AI which wants to learn human values and have full access to human brain, where it should search for human values?
If cortical algorithm will be replaced with GPT-N in some human mind model, will the whole system work?
Well, all the models in the frontal lobe get, let’s call it, reward-prediction points (see my comment here), which feels like positive vibes or something.
If the generative model “I eat a cookie” has lots of reward-prediction points (including the model itself and the downstream models that get activated by it in turn), we describe that as “I want to eat a cookie”.
Likewise If the generative model “Michael Jackson” has lots of reward prediction points, we describe that as “I like Michael Jackson. He’s a great guy.”. (cf. “halo effect”)
If somebody says that justice is one of their values, I think it’s at least partly (and maybe primarily) up a level in meta-cognition. It’s not just that there’s a generative model “justice” and it has lots of reward-prediction points (“justice is good”), but there’s also a generative model of yourself valuing justice, and that has lots of reward-prediction points too. That feels like “When I think of myself as the kind of person who values justice, it’s a pleasing thought”, and “When I imagine other people saying that I’m a person who values justice, it’s a pleasing thought”.
This isn’t really answering your question of what human values are or should be—this is me saying a little bit about what happens behind the scenes when you ask someone “What are your values?”. Maybe they’re related, or maybe not. This is a philosophy question. I don’t know.
If cortical algorithm will be replaced with GPT-N in some human mind model, will the whole system work?
My belief (see post here) is that GPT-N is running a different kind of algorithm, but learning to imitate some steps of the brain algorithm (including neocortex and subcortex and the models that result from a lifetime of experience, and even hormones, body, etc.—after all, the next-token-prediction task is the whole input-output profile, not just the neocortex.) in a deep but limited way. I can’t think of a way to do what you suggest, but who knows.
I have several questions:
Where are qualia and consciousness in this model?
Is this model address difference between two hemispheres?
What about long term-memory? Is it part of neocortex?
How this model explain the phenomenon of night dreams?
Good questions!!!
See my Book Review: Rethinking Consciousness.
Insofar as there are differences between the two hemispheres—and I don’t know much about that—I would treat it like any other difference between different parts of the cortex (Section 2), i.e. stemming from (1) the innate large-scale initial wiring diagram, and/or (2) differences in “hyperparameters”.
There’s a lot that can be said about how an adult neocortex represents and processes information—the dorsal and ventral streams, how do Wernicke’s area and Broca’s area interact in speech processing, etc. etc. ad infinitum. You could spend your life reading papers about this kind of stuff!! It’s one of the main activities of modern cognitive neuroscience. And you’ll notice that I said nothing whatsoever about that. Why not?
I guess there’s a spectrum of how to think about this whole field of inquiry:
On one end of the spectrum (the Gary Marcus / Steven Pinker end), this line of inquiry is directly attacking how the brain works, so obviously the way to understand the brain is to work out all these different representations and mechanisms and data flows etc.
On the opposite end of the spectrum (maybe the “cartoonish connectionist” end?), this whole field is just like the OpenAI Microscope project. There is a simple, generic learning algorithm, and all this rich structure—dorsal and ventral streams, phoneme processing in such-and-such area, etc.—just naturally pops out of the generic learning algorithm. So if your goal is just to make artificial intelligence, this whole field of inquiry is entirely unnecessary—in the same way that you don’t need to study the OpenAI Microscope project in order to train and use a ConvNet image classifier. (Of course maybe your goal is something else, like understanding adult human cognition, in which case this field is still worth studying.)
I’m not all the way at the “cartoonish connectionist” end of the spectrum, because I appreciate the importance of the initial large-scale wiring diagram and the hyperparameters. But I think I’m quite a bit farther in that direction than is the median cognitive neuroscientist. (I’m not alone out here … just in the minority.) So I get more excited than mainstream neuroscientists by low-level learning algorithm details, and less excited than mainstream neuroscientists about things like hemispherical specialization, phoneme processing chains, dorsal and ventral streams, and all that kind of stuff. And yeah, I didn’t talk about it at all in this blog post.
There’s a lot about how the neocortex learning algorithm works that I didn’t talk about, and indeed a lot that is unknown, and certainly a lot that I don’t know! For example, the generative models need to come from somewhere!
My impression is that the hippocampus is optimized to rapidly memorize arbitrary high-level patterns, but it only holds on to those memories for like a couple years, during which time it recalls them when appropriate to help the neocortex deeply embed that new knowledge into its world model, with appropriate connections and relationships to other knowledge. So the final storage space for long-term memory is the neocortex.
I’m not too sure about any of this.
This video about the hippocampus is pretty cool. Note that I count the hippocampus as part of the “neocortex subsystem”, following Jeff Hawkins.
I don’t know. I assume it somehow helps optimize the set of generative models and their connections.
I guess dreaming could also have a biological purpose but not a computational purpose (e.g., some homeostatic neuron-maintenance process, that makes the neurons fire as an unintended side-effect). I don’t think that’s particularly likely, but it’s possible. Beats me.
Thanks. I think that a plausible explanation of dreaming is generating of virtual training environments where an agent is training to behave in the edge cases, on which it is too costly to train in real life or in real world games. That is why the generic form of the dreams is nightmare: like, a lion attack me, or I am on stage and forget my speech.
From “technical” point view, dream generation seems rather simple: if the brain has world-model generation engine, it could generate predictions without any inputs, and it will look like an dream.
I reread the post and have some more questions:
Where is “human values” in this model? If we give this model to an AI which wants to learn human values and have full access to human brain, where it should search for human values?
If cortical algorithm will be replaced with GPT-N in some human mind model, will the whole system work?
Well, all the models in the frontal lobe get, let’s call it, reward-prediction points (see my comment here), which feels like positive vibes or something.
If the generative model “I eat a cookie” has lots of reward-prediction points (including the model itself and the downstream models that get activated by it in turn), we describe that as “I want to eat a cookie”.
Likewise If the generative model “Michael Jackson” has lots of reward prediction points, we describe that as “I like Michael Jackson. He’s a great guy.”. (cf. “halo effect”)
If somebody says that justice is one of their values, I think it’s at least partly (and maybe primarily) up a level in meta-cognition. It’s not just that there’s a generative model “justice” and it has lots of reward-prediction points (“justice is good”), but there’s also a generative model of yourself valuing justice, and that has lots of reward-prediction points too. That feels like “When I think of myself as the kind of person who values justice, it’s a pleasing thought”, and “When I imagine other people saying that I’m a person who values justice, it’s a pleasing thought”.
This isn’t really answering your question of what human values are or should be—this is me saying a little bit about what happens behind the scenes when you ask someone “What are your values?”. Maybe they’re related, or maybe not. This is a philosophy question. I don’t know.
My belief (see post here) is that GPT-N is running a different kind of algorithm, but learning to imitate some steps of the brain algorithm (including neocortex and subcortex and the models that result from a lifetime of experience, and even hormones, body, etc.—after all, the next-token-prediction task is the whole input-output profile, not just the neocortex.) in a deep but limited way. I can’t think of a way to do what you suggest, but who knows.