Symbolic AI could never become great because it was missing the connection to the intricacies of the real world that you can only get from data. I think a symbioses of symbolic techniques with LLMs or generally multimodal autoregressive foundation models will lead to our first legit "AGIish" agents. The LLM takes the role of a little gremlin inside the machine that provides the magic sauce, that tiny bit of general intelligence or common sense necessary to connect arbitrary interfaces.
I suspect Symbolic AI might become a part of the larger LLM based AI. IMO symbolic AI is really a representation of 'hard truths' where there is no room for fuzziness like physical laws. But real world is fuzzy so the AI needs to develop a discretion on where to apply symbolic AI and how much.
one example may be an AI can look at the car video feed and use it to determine the speed of a car but to recognize the mile/road markers it may have to use the traditional CNN type modules, kinda like what we do.
Overall I feel this has a potential of ballooning into something really interesting like theoretical physics research could be largely automated using this type of combo (in somewhat not-too-distant future).
Yes, I agree. Symbolic AI is for hardness, which we can use for the skeleton of the agent. LLMs are the fuzziness that surrounds and interfaces the skeleton. Everyone who writes some simple code that assembles a promot is already doing that.
This is I think quite insightful. Looking at the current AI hype cycle, I haven't actually been that impressed with GPT and the image generation models. They just get things too wrong, too often, and too convincingly to the point that they become dangerous/harmful. There's just no way to evaluate quality.
I strongly believe the first person that figures out how to interface these language models with some kind of knowledge model or source of truth will win a lot of the pot for AI technology. Human feedback clearly helped GPT get to where it is now, but It's clearly optimized for answers that appear good rather than are good and based on known facts.
The tldr; is “yes, but…” and the “but” is that, no, generating/invoking python functions from an LLM is no where even remotely near the capabilities exposed by their online systems.
Yeah yeah, “but there’s a python library for that…”, no really. Have a play with it. There isn’t, for lots of the stuff it can do.
You can ground models better with retrieval augmented generation, and toolformer can actually call functions and whatnot, it is also entirely possible to orchestrate things with "agents".
If you connect a bunch of LLMs with a hard coded structure (Like sensory module -> planning module -> action agent or something like that) then for me that is like assembling a symbolic AI system out of non-symbolic components.
I think the magic of LLMs, if any, is the fact that they can make these kind of "symbolic AI" systems to work.
AutoGPT / AgentGPT / TeenageGPT will inevitably start borrowing ideas from Marvin Minsky & other symbolic AI / cognitive science researchers.
The space of different configurations is much larger than simple connections. It's in fact possibly larger than our imagination. This is because it's hard to comprehend all inputs to a single GPT-4 instance, and the space of LLM-multiagents is essentially the space of *graph theoretical graphs* of GPT-4 instances, practically in four dimensions (if one needs to start thinking about the signals strictly).
Comments
Symbolic AI could never become great because it was missing the connection to the intricacies of the real world that you can only get from data. I think a symbioses of symbolic techniques with LLMs or generally multimodal autoregressive foundation models will lead to our first legit "AGIish" agents. The LLM takes the role of a little gremlin inside the machine that provides the magic sauce, that tiny bit of general intelligence or common sense necessary to connect arbitrary interfaces.
I suspect Symbolic AI might become a part of the larger LLM based AI. IMO symbolic AI is really a representation of 'hard truths' where there is no room for fuzziness like physical laws. But real world is fuzzy so the AI needs to develop a discretion on where to apply symbolic AI and how much.
one example may be an AI can look at the car video feed and use it to determine the speed of a car but to recognize the mile/road markers it may have to use the traditional CNN type modules, kinda like what we do.
Overall I feel this has a potential of ballooning into something really interesting like theoretical physics research could be largely automated using this type of combo (in somewhat not-too-distant future).
Yes, I agree. Symbolic AI is for hardness, which we can use for the skeleton of the agent. LLMs are the fuzziness that surrounds and interfaces the skeleton. Everyone who writes some simple code that assembles a promot is already doing that.
This is I think quite insightful. Looking at the current AI hype cycle, I haven't actually been that impressed with GPT and the image generation models. They just get things too wrong, too often, and too convincingly to the point that they become dangerous/harmful. There's just no way to evaluate quality.
I strongly believe the first person that figures out how to interface these language models with some kind of knowledge model or source of truth will win a lot of the pot for AI technology. Human feedback clearly helped GPT get to where it is now, but It's clearly optimized for answers that appear good rather than are good and based on known facts.
It's been already done with LLangchain / Plugins / Function calling by LLMs.
I don’t think you’ve understood the difference between what you can do with wolfram vs. python.
Have a read of https://www.wolfram.com/language/elementary-introduction/3rd... and play with some examples maybe?
The tldr; is “yes, but…” and the “but” is that, no, generating/invoking python functions from an LLM is no where even remotely near the capabilities exposed by their online systems.
Yeah yeah, “but there’s a python library for that…”, no really. Have a play with it. There isn’t, for lots of the stuff it can do.
You can ground models better with retrieval augmented generation, and toolformer can actually call functions and whatnot, it is also entirely possible to orchestrate things with "agents".
By far, symbiosis with other LLMs, not traditional programs, will lead to more power AGIs.
If you connect a bunch of LLMs with a hard coded structure (Like sensory module -> planning module -> action agent or something like that) then for me that is like assembling a symbolic AI system out of non-symbolic components.
I think the magic of LLMs, if any, is the fact that they can make these kind of "symbolic AI" systems to work.
AutoGPT / AgentGPT / TeenageGPT will inevitably start borrowing ideas from Marvin Minsky & other symbolic AI / cognitive science researchers.
The space of different configurations is much larger than simple connections. It's in fact possibly larger than our imagination. This is because it's hard to comprehend all inputs to a single GPT-4 instance, and the space of LLM-multiagents is essentially the space of *graph theoretical graphs* of GPT-4 instances, practically in four dimensions (if one needs to start thinking about the signals strictly).