Still others of these “brain-like AGI ingredients” seem mostly or totally absent from today’s most popular ML algorithms (e.g. ability to form “thoughts” [e.g. “I’m going to the store”] that blend together immediate actions, short-term predictions, long-term predictions, and flexible hierarchical plans, inside a generative world-model that supports causal and counterfactual and metacognitive reasoning).
I think that chain-of-though planing in an agentic LLM-driven model, might qualify as this. Would you agree?
Thanks, I just deleted that whole part. I do believe there’s something-like-that which is true, but it would take some work to pin down, and it’s not very relevant to this post, so I figure, I should just delete it. :-)
In case anyone’s curious, here’s the edit I just made:
OLD VERSION:
Anyway, by assuming “brain-like AGI”, I get the right to make certain assumptions about the cognitive architecture, representations, learning algorithms, and so on.
Some of these “brain-like AGI ingredients” are universal parts of today’s popular ML algorithms (e.g. learning algorithms; distributed representations).
Others of these “brain-like AGI ingredients” are (individually) present in a subset of today’s popular ML algorithms but absent from others (e.g. reinforcement learning; predictive [a.k.a. self-supervised] learning; explicit planning).
Still others of these “brain-like AGI ingredients” seem mostly or totally absent from today’s most popular ML algorithms (e.g. ability to form “thoughts” [e.g. “I’m going to the store”] that blend together immediate actions, short-term predictions, long-term predictions, and flexible hierarchical plans, inside a generative world-model that supports causal and counterfactual and metacognitive reasoning).
So in this sense, “brain-like AGI” is a specific thing that might or might not happen, independently of “prosaic AGI”. Much more on “brain-like AGI”, or at least its safety-relevant aspects, in the subsequent posts.
NEW VERSION:
Anyway, by assuming “brain-like AGI”, I get the right to make certain assumptions about the cognitive architecture, representations, learning algorithms, and so on. Some of those assumptions would also apply to some existing AI algorithms. But if you take the whole package together—all the parts and how they interconnect—it constitutes a yet-to-be-invented AI architecture. So in this sense, “brain-like AGI” is a specific thing that might or might not happen, independently of “prosaic AGI”. Much more on “brain-like AGI”, or at least its safety-relevant aspects, in the subsequent posts.
I think that chain-of-though planing in an agentic LLM-driven model, might qualify as this. Would you agree?
Thanks, I just deleted that whole part. I do believe there’s something-like-that which is true, but it would take some work to pin down, and it’s not very relevant to this post, so I figure, I should just delete it. :-)
In case anyone’s curious, here’s the edit I just made:
OLD VERSION:
Anyway, by assuming “brain-like AGI”, I get the right to make certain assumptions about the cognitive architecture, representations, learning algorithms, and so on.
Some of these “brain-like AGI ingredients” are universal parts of today’s popular ML algorithms (e.g. learning algorithms; distributed representations).
Others of these “brain-like AGI ingredients” are (individually) present in a subset of today’s popular ML algorithms but absent from others (e.g. reinforcement learning; predictive [a.k.a. self-supervised] learning; explicit planning).
Still others of these “brain-like AGI ingredients” seem mostly or totally absent from today’s most popular ML algorithms (e.g. ability to form “thoughts” [e.g. “I’m going to the store”] that blend together immediate actions, short-term predictions, long-term predictions, and flexible hierarchical plans, inside a generative world-model that supports causal and counterfactual and metacognitive reasoning).
So in this sense, “brain-like AGI” is a specific thing that might or might not happen, independently of “prosaic AGI”. Much more on “brain-like AGI”, or at least its safety-relevant aspects, in the subsequent posts.
NEW VERSION:
Anyway, by assuming “brain-like AGI”, I get the right to make certain assumptions about the cognitive architecture, representations, learning algorithms, and so on. Some of those assumptions would also apply to some existing AI algorithms. But if you take the whole package together—all the parts and how they interconnect—it constitutes a yet-to-be-invented AI architecture. So in this sense, “brain-like AGI” is a specific thing that might or might not happen, independently of “prosaic AGI”. Much more on “brain-like AGI”, or at least its safety-relevant aspects, in the subsequent posts.