All of your examples seem to involve applying some radically different approach to overturn a status quo, which is certainly possible. But I am unsure of where you suggest a radically different approach.
Many program induction researchers are cognitive scientists, and have suggested compositional / hierarchical representations for a decades [1] and actively in recent years [2] including even a paper I am on: https://arxiv.org/html/2504.20628v1 So, basically I don’t expect there to be alpha from this observation in itself.
I am, of course, an information theory enthusiast (particularly AIT), but this is an old field and it is not clear to me what specific (recent?) results you hope to leverage? Or why these would results would have been overlooked?
You point to progress on natural abstractions, but to me this only indicates that it should in principle perhaps be possible to come up with some kind of interpretable world model. Has NA research actually produced practical algorithms or methods, or is it in reach of doing so?
Agent foundations as a category is too broad for me to understand what mathematics you are hoping to leverage. And unfortunately, I do not think the AF community has made a large number of significant breakthoughs—reflective oracles, logical induction, and perhaps incomplete models/IB are the main examples left in my mind (you categorized NAH separately), but do not see how they are relevant here.
Basically, you have expressed various hopes, and perhaps some of them are promising (and I will read about them throughout this sequence) but from your comment alone, your alpha over the program induction community (or for instance even the Cyc project) is not legible (to me) - not even as a high-level summary of a technical program. So, I am left hopeful that you will make progress here, but your highly ambitious goal still seems like a distant point on a nearly blank map to me.
[1] Brenden M Lake and Steven T Piantadosi. People infer recursive visual concepts from just a few examples. Computational Brain & Behavior, 3(1):54–65, 2020 [2] Jerry A Fodor. The language of thought, volume 5. Harvard university press, 1975.
Nice, that’s the sort of poking-of-holes I was looking for.
your alpha over the program induction community (or for instance even the Cyc project) is not legible (to me)
That’s a good thinking prompt. What is the full set of reasons I’m optimistic about this, in legible terms?
… Hm, but perhaps outlining what you think your edge is in public is not a great idea. I’ll answer in PMs tomorrow. (To ensure future readers get some information about how convincing my reasons are, though, it’d be neat if you posted your impressions afterwards as a response to this comment.)
All of your examples seem to involve applying some radically different approach to overturn a status quo, which is certainly possible. But I am unsure of where you suggest a radically different approach.
The program induction community does take advantage of neural methods and has been doing so for a long time, see recent work from Kevin Ellis but I think as far back as his thesis: https://www.cs.cornell.edu/~ellisk/documents/kevin_ellis_thesis.pdf
Many program induction researchers are cognitive scientists, and have suggested compositional / hierarchical representations for a decades [1] and actively in recent years [2] including even a paper I am on: https://arxiv.org/html/2504.20628v1
So, basically I don’t expect there to be alpha from this observation in itself.
I am, of course, an information theory enthusiast (particularly AIT), but this is an old field and it is not clear to me what specific (recent?) results you hope to leverage? Or why these would results would have been overlooked?
You point to progress on natural abstractions, but to me this only indicates that it should in principle perhaps be possible to come up with some kind of interpretable world model. Has NA research actually produced practical algorithms or methods, or is it in reach of doing so?
Agent foundations as a category is too broad for me to understand what mathematics you are hoping to leverage. And unfortunately, I do not think the AF community has made a large number of significant breakthoughs—reflective oracles, logical induction, and perhaps incomplete models/IB are the main examples left in my mind (you categorized NAH separately), but do not see how they are relevant here.
Basically, you have expressed various hopes, and perhaps some of them are promising (and I will read about them throughout this sequence) but from your comment alone, your alpha over the program induction community (or for instance even the Cyc project) is not legible (to me) - not even as a high-level summary of a technical program. So, I am left hopeful that you will make progress here, but your highly ambitious goal still seems like a distant point on a nearly blank map to me.
[1] Brenden M Lake and Steven T Piantadosi. People infer recursive visual concepts from just a few
examples. Computational Brain & Behavior, 3(1):54–65, 2020
[2] Jerry A Fodor. The language of thought, volume 5. Harvard university press, 1975.
Nice, that’s the sort of poking-of-holes I was looking for.
That’s a good thinking prompt. What is the full set of reasons I’m optimistic about this, in legible terms?
… Hm, but perhaps outlining what you think your edge is in public is not a great idea. I’ll answer in PMs tomorrow. (To ensure future readers get some information about how convincing my reasons are, though, it’d be neat if you posted your impressions afterwards as a response to this comment.)