Is the separate world model for each cortical column an “Ingenious workaround for the limitations of biology” or a “Good idea that AI should copy”?
In machine learning, one technique for training an ensemble is to give each model in your ensemble a different subset of the features you have available. (Well known as a component of one of the most successful ML algorithms.) So I think it already has been copied. BTW, results from different models in an ensemble are usually combined using a voting mechanism.
Re: sparseness—”sparse representation theory” is apparently a thing, and it looks really cool. At some point I want to take this course on EdX.
Near the end of the podcast, Jeff emphatically denounced the idea of AI existential risk, or more generally that there was any reason to second-guess his mission of getting beyond-human-level intelligence as soon as possible. However, he appears to be profoundly misinformed about both what the arguments are for existential risk and who is making them—almost as if he learned about the topic by reading Steven Pinker or something. Ditto for Lex, the podcast host.
Maybe we can get an AI safety person to appear on the podcast.
Jeff is proposing that our human brain’s world models are ridiculously profligate in the number of primitive entries included. Our world models don’t just have one entry for “shirt”, but rather separate entries for wet shirt, folded shirt, shirt-on-ironing-board, shirt-on-floor, shirt-on-our-body, shirt-on-someone-else’s-body, etc. etc. etc.
Shower thought: If the absent-minded professor stereotype is true, maybe it’s because the professor’s brain has repurposed so much storage space that these kind of “irrelevant” details are now being abstracted away. This could explain why learning more stuff doesn’t typically cause you to forget things you already know: You are forgetting stuff, it’s just irrelevant minutiae.
We can break that down into 10 simultaneous active concepts (implemented as sparse population codes of 200 neurons each)
Maybe thoughts which appear out of nowhere are the neurological equivalent of collisions in a bloom filter.
Thinking of the cortical columns as models in an ensemble… Have ML people tried ensemble models with tens of thousands of models? If so, are they substantially better than using only a few dozen? If they aren’t, then why does the brain need so many?
According to A Recipe for Training NNs, model ensembles stop being helpful at ~5 models. But that’s when they all have the same inputs and outputs. The more brain-like thing is to have lots of models whose inputs comprise various different subsets of both the inputs and the other models’ outputs.
...But then, you don’t really call it an “ensemble”, you call it a “bigger more complicated neural architecture”, right? I mean, I can take a deep NN and call it “six different models, where the output of model #1 is the input of model #2 etc.”, but no one in ML would say that, they would call it a single six-layer model...
In machine learning, one technique for training an ensemble is to give each model in your ensemble a different subset of the features you have available. (Well known as a component of one of the most successful ML algorithms.) So I think it already has been copied. BTW, results from different models in an ensemble are usually combined using a voting mechanism.
Re: sparseness—”sparse representation theory” is apparently a thing, and it looks really cool. At some point I want to take this course on EdX.
Maybe we can get an AI safety person to appear on the podcast.
Shower thought: If the absent-minded professor stereotype is true, maybe it’s because the professor’s brain has repurposed so much storage space that these kind of “irrelevant” details are now being abstracted away. This could explain why learning more stuff doesn’t typically cause you to forget things you already know: You are forgetting stuff, it’s just irrelevant minutiae.
Maybe thoughts which appear out of nowhere are the neurological equivalent of collisions in a bloom filter.
Interesting, thanks!
Thinking of the cortical columns as models in an ensemble… Have ML people tried ensemble models with tens of thousands of models? If so, are they substantially better than using only a few dozen? If they aren’t, then why does the brain need so many?
According to A Recipe for Training NNs, model ensembles stop being helpful at ~5 models. But that’s when they all have the same inputs and outputs. The more brain-like thing is to have lots of models whose inputs comprise various different subsets of both the inputs and the other models’ outputs.
...But then, you don’t really call it an “ensemble”, you call it a “bigger more complicated neural architecture”, right? I mean, I can take a deep NN and call it “six different models, where the output of model #1 is the input of model #2 etc.”, but no one in ML would say that, they would call it a single six-layer model...