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Comment by hinkley | Hacker News Reader

I have a hard time putting SAT, SMT, and at least some linear algebra algorithms into the AI category. They are all logic, just of a sort that’s far more mind-numbing than our usual data processing workflows.

I know the AI people get grumpy about all of the problem spaces that have been harvested or “stolen” and called conventional code, but in this case I’m not so sure that’s accurate.

It feels more like a bunch of people who wanted to use advanced math and logic jumped on the AI gravy train while it was going past, and then hopped off again when they had their fill.

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SMT, SAT, etc are absolutely AI. In fact, Machine Learning is just another field inside AI (albeit a famous and successful one). As someone that got his PhD doing his research in "classical" AI, it's surprising that nowadays most people think that AI is just machine learning.

+1. I am always surprised that they used to do (and of course still must do) proper full-on formal verification of e.g. train track scheduling systems, so train accidents, and consequently 100s of deaths don't just happen because "sorry for the bug, ver 1.2 fixes it" -- but people are happy to sit into a Tesla going at 120km/h and allow a well-tested, but not formally verified deep learning system to make decisions. BTW, I know it's not formally verified, because formally verifying deep learning systems is a currently very much unsolved problem for anything but toy examples.

Is linear regression AI? How about logistic regression? Many people would consider the latter to be ML/AI while the former statistics.

Ultimately, I think the distinction is pretty meaningless.

I completely agree.

Hot take: If SMT, SAT are too simple to be given the coveted badge of AI, then Machine learning should be called as glorified curve-fitting?

I feel that everyone is amazed by the results provided by the good examples of Machine Learning, as only those get popular. The spectacular failures and completely absurd results which reveal that ML is not having any semantic understanding of the problem are completely glossed over.

I don't know if you know but 20 odd years ago what was called AI at universities was SMT/prolog etc. People where doing stuff like expressing common sense facts in logic and using programs to derive more common sense.

Academic computer scientists are like that.

Research priorities in the U.S. driven by what the Department of Defense will pay for. DoD had some bad experiences with FPGA and now they want to build ASIC supercompilers.

One thing I find funny is that the production rules engines that we have today (Drools, Clara) are dramatically better than the ones we had when production rules were popular (e.g. CLIPS, eMYCIN) Today we have improved RETE algorithms, hash indices, etc. Production rules only seem to survive in a few niches, one of which is inside banks, another is complex event processing.

AI is a wider field than you think. Hell, even the A* algorithm for path planning was considered AI.

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