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by photonthug 733 days ago
Someone somewhere is quietly working on teaching LLMs to generate something along the lines of AlloyLang code so that there’s an actual evolving/updating logical domain model that underpins and informs the statistical model.

This approach is not that far from what TFA is getting at with the stockfish comeback. Banking on pure stats or pure logic are both kind of obviously dead ends for having real progress instead of toys. Banking on poorly understood emergent properties of one system to compensate for the missing other system also seems silly.

Sadly though, whoever is working on serious hybrid systems will probably not be very popular in either of the rather extremist communities for pure logic or pure ML. I’m not exactly sure why folks are ideological about such things rather than focused on what new capabilities we might get. Maybe just historical reasons? But thus the fallout from last AI winter may lead us into the next one.

2 comments

The current hype phase is straight out of “Extraordinary Popular Delusions and the Madness of Crowds”

Science is out the window. Groupthink and salesmanship are running the show right now. There would be a real irony to it if we find out the whole AI industry drilled itself into a local minimum.

You mean, the high interest landscape made corpos and investors alike cry out in a loud panic while coincidentally people figured out they could scale up deep learning and thus we had a new Jesus Christ born for scammers to have a reason to scam stupid investors by the argument we only need 100000x more compute and then we can replace all expensive labour by one tiny box in the cloud?

Nah, surely Nvidia's market cap as the main shovel-seller in the 2022 - 2026(?) gold-rush being bigger than the whole French economy is well-reasoned and has a fundamentally solid basis.

It couldn’t have been a more well designed grift. At least when you mine bitcoin you get something you can sell. I’d be interested to see what profit, if any, any even large corporation has seen from burning compute on LLMs. Notice I’m explicitly leaving out use cases like ads ranking which almost certainly do not use LLMs even if they do run on GPUs.
>> Sadly though, whoever is working on serious hybrid systems will probably not be very popular in either of the rather extremist communities for pure logic or pure ML.

That is not true. I work in logic-based AI (a form of machine learning where everything, examples, learned models, and inductive bias, is represented as logic programs). I am not against hybrid systems and the conference of my field, the International Joint Conferences of Learning and Reasoning included NeSy the International Conference on Neural-Symbolic Learning and Reasoning (and will again, from next year, I believe). Statistical machine learning approaches and hybrid approaches are widespread in the literature of classical, symbolic AI, such as the literature on Automated Planning and Reasoning, and you need only take a look at the big symbolic conferences like AAAI, IJCAI, ICAPS (planning) and so on to see that there is a substantial fraction of papers on either purely statistical, or neuro-symbolic approaches.

But try going the other way and searching for symbolic approaches in the big statistical machine learning conferences: NeurIPS, ICML, ICLR. You may find the occasional paper from the Statistical Relational Learning community but that's basically it. So the fanaticism only goes one way: the symbolicists have learned the lessons of the past and have embraced what works, for the sake of making things, well, work. It's the statistical AI folks who are clinging on to doctrine, and my guess is they will continue to do so, while their compute budgets hold. After that, we'll see.

What's more, the majority of symbolicists have a background in statistical techniques- I for example, did my MSc in data science and let me tell you, there was hardly any symbolic AI in my course. But ask a Neural Net researcher to explain to you the difference between, oh, I don't know, DFS with backtracking and BFS with loop detection, without searching or asking an LLM. Or, I don't know, let them ask an LLM and watch what happens.

Now, that is a problem. The statistical machine learning field has taken it upon itself in recent years to solve reasoning, I guess, with Neural Nets. That's a fine ambition to have except that reasoning is already solved. At best, Neural Nets can do approximate reasoning, with caveats. In a fantasy world, which doesn't exist, one could re-discover sound and complete search algorithms and efficient heuristics with a big enough neural net trained on a large enough dataset of search problems. But, why? Neural Nets researchers could save themselves another 30 years of reinventing a wheel, or inventing a square wheel that only rolls on Tuesdays, if they picked up a textbook on basic Computer Science or AI (Say, Russel and Norvig, that it seems some substantial minority think as a failure because it didn't anticipate neural net breakthroughs 10 years later).

AI has a long history. Symbolicists know it, because they, or their PhD advisors, were there when it was being written and they have the facial injuries to prove it from falling down all the possible holes. But, what happens when one does not know the history of their own field of research?

In any case, don't blame symbolicists. We know what the statisticians do. It's them who don't know what we've done.

This is a really thoughtful comment. The part that stood out to me:

>> So the fanaticism only goes one way: the symbolicists have learned the lessons of the past and have embraced what works, for the sake of making things, well, work. It's the statistical AI folks who are clinging on to doctrine, and my guess is they will continue to do so, while their compute budgets hold. After that, we'll see.

I don’t think the compute budgets will hold for long enough to make their dream of intelligence emerging from a random bundles of edges and nodes to come to a reality. I’m hoping it comes to an end sooner rather than later