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You’re in good company — the most influential AI academic of all time, the cooky grandfather of AI who picked up right where (when!) Turing left off, the man ha
by bbor 2y ago
You’re in good company — the most influential AI academic of all time, the cooky grandfather of AI who picked up right where (when!) Turing left off, the man hated by both camps yet somehow in charge of them, agrees with you. I’m talking about Marvin Minsky, of course. See: Logical vs. Analogical (Minsky, 1991) https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/download/894/812 https://ojs.aaai.org/aimagazine/index.php/aimagazine/article...
…the limitations of current machine intelligence largely stem from seeking unified theories or trying to repair the deficiencies of theoretically neat but conceptually impoverished ideological positions.
Our purely numeric connectionist networks are inherently deficient in abilities to reason well; our purely symbolic logical systems are inherently deficient in abilities to represent the all-important heuristic connections between things—the uncertain, approximate, and analogical links that we need for making new hypotheses. The versatility that we need can be found only in larger-scale architectures that can exploit and manage the advantages of several types of representations at the same time.
Then, each can be used to overcome the deficiencies of the others. To accomplish this task, each formally neat type of knowledge representation or inference must be complemented with some scruffier kind of machinery that can embody the heuristic connections between the knowledge itself and what we hope to do with it.
He phrases it backwards here in comparison to what you’re talking about (probably because no one in their right mind would have predicted the feasibility of LLMs), but I think the parallel argument should be clear. Talking about “human reasoning” like Simon & Newell or LeCun & Hinton do in terms of one single paradigm is like talking about “human neurons”. There’s tons of different neuronal architectures at play in our brains, and only through the ad-hoc minimally-centralized combination of all of them do we find success.
Personally, I’m a big booster of the term Unified Artificial Intelligence (UAI) for this paradigm; isn’t it fetch? ;)
- dleink 2y agoJust throwing this out there for someone, "Scruffier Kind of Machinery" is a good name for a book, company or band.
- bbor 2y agoHeh, well spotted. SSKOM will be overtaking KGATLW any day now…
- YeGoblynQueenne 2y agoMinsky is right. The "rift" between symbolic and sub-symbolic, or learning and reasoning has only impeded progress. The problem is it's very hard to be an expert on both at once, and it's getting harder and harder as more and more work is done on both. Since you mentioned Hinton, he has worked hard to entrench the idea that symbolic AI failed. I was listening to a lecture he gave [1] and he went on and On and ON about how symbolic AI was a stupid idea and it was conclusively proved wrong by neural nets. He went on for so long bashing symbolic AI to the ground that at some point I started wondering whether he's deep down worried that there might be a resurgence of it right on time to address the limitations of neural nets with respect to reasoning, before he and his mates have the chance to figure out how to overcome it. Which, well, good luck with that. _________________ [1] https://youtu.be/N1TEjTeQeg0?si=OIYY3wnDbi7rzJt3 https://youtu.be/N1TEjTeQeg0?si=OIYY3wnDbi7rzJt3
- infinite8s 2y agoCompletely agree. The next revolution in AI will be the person who can successfully combine both symbolic and connectionist architectures.
- larodi 2y agoYou’d be astonished how correct is this. I know several top PHD ppl well versed in ML who openly admin they know nothing about SQL which implies they also know very little PROLOG and very likely are ignorant about everything that is grammars and state automata. Top surprise was when my high school classmate who went on to win two gold medals in IoM and has been doing quant mathematics for finance more than 16years openly admitted he knew nothing about grammars and was like ‘is this useful at all…’. I was amazed how is this even possible. But it is - he went the probabilistic and symbolic way, I went the discret and graph way. On the other side I’m completely oblivious of what people use complex analysis for, even though I know a little DSP, some electronics, even some nano opto-electronics, and also can explain Fourier Transform to people. Even though I know what dérivâtes, nabla and vector field is, I can’t put them to work for me… Science is never done in isolation, and the whole LLM thing seems from another planet to many people cause it was devised in a ML silo and also enterprise silo.
- larodi 2y agoUAI seems reasonable as we need both discreet and non-discreet systems to start talking in a meaningful way. Was wondering recently - given that a lot can be done with predicate logic, and given that DNA is a sort of grammar, is there anything more powerful than these formalisms in math in genera, that is actually put to work somewhere, anywhere?