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Why we stopped using the mathematics that works

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Re: Why we stopped using the mathematics that works

#31

Post by 10xDev »

NateEag wrote: post #10
andai wrote: post #9

Earlier quoted context omitted.

This means money beats math?

It means trying to figure out how to build an intelligence always loses to mindlessly brute-forcing problems with more compute: https://en.wikipedia.org/wiki/Bitter_lesson

They have researchers working for insane salaries just so they don't go to another frontier lab to share their ideas. If you think it is just "mindless bruteforce" you don't understand anything. The idea is that the most effective methods are ones that scale but those ideas are also then limited by the compute available.

Re: Why we stopped using the mathematics that works

#32

Post by naasking »

vessenes wrote: post #16

Heading down the links of this blog ends up at https://github.com/gfrmin/credence , which claims to be an agentic harness that keeps track of usefulness of tools separately and beats LangChain at a benchmark. LangChain… Now that’s a name I haven’t heard in a long, long time.. Anyway, that’s a cool idea. But also his blog posts include phrases like “That’s not intelligence, it’s just with vibes.” Urg. Slop of the wors…

> But also his blog posts include phrases like “That’s not intelligence, it’s just with vibes.” Urg. Slop of the worst sort.

You know people actually write like that. The LLMs learned it from somewhere.

Re: Why we stopped using the mathematics that works

#33

Post by naasking »

canjobear wrote: post #29
andai wrote: post #11

Earlier quoted context omitted.

What makes you say that? Which LLM does it sound like to you?

> Not because the methods it displaced had stopped working, but because the money, the talent, and the prestige had moved elsewhere. The researchers who understood decision theory, Bayesian inference, and operations research didn’t lose their arguments. They lost their audience.

So, what's the problem with it?

Re: Why we stopped using the mathematics that works

#34

Post by kjshsh123 »

pron wrote: post #30

> This is the VHS-versus-Betamax dynamic, or TCP/IP versus the OSI model, or QWERTY versus every ergonomic alternative proposed since 1936. The technically superior solution loses to the solution that’s easier to deploy, easier to hire for, and good enough for the use cases that pay the bills. Without commenting on the merit of the claims, the problem with this statement is that in many cases there is no universal "t…

Pretty much although it's less about majority/minority preference and more about utilitarianism and economics.

It's possible a majority of pofeople would have been marginally happier with betamax than vhs. Even in that case, vhs can still win because a minority of people had a strong, stubborn preference for it, even if a majority of people had a weak preference for betamax.

If 1,000,000 people are willing to pay $5 more for video quality but 800,000 people are willing to pay $8 more for longer recording, which wins out?

Not to mention savings on the producer side are relevant too, not just consumer side.

I'm not saying the above is necessarily the case. Just pointing out that markets aren't majoritarian, they're utilitarian.

Re: Why we stopped using the mathematics that works

#35

Post by jebarker »

ontouchstart wrote: post #5

We are at the age of alchemy, wait for the age of chemistry and physics. New mathematical foundations are yet to be found.

I’m not convinced that useful mathematical foundations will be found anytime soon. Neural nets exist because we want to make decisions in a world that is so noisy, complex and chaotic that we can’t satisfy the requirements of more rigorous analytical frameworks. It seems to me that the irreducible complexity is in the real world, not the neural networks.

Re: Why we stopped using the mathematics that works

#37

Post by PaulHoule »

zozbot234 wrote: post #23
PaulHoule wrote: post #15

Earlier quoted context omitted.

I think the worst thing about the golden age of symbolic AI was that there was never a systematic approach to reasoning about uncertainty. The MYCIN system was rather good at medical diagnostics and like other systems of the time had an ad-hoc procedure to deal with uncertainty which is essential in medical diagnosis. The problem is that is not enough to say "predicate A has a 80% of being true" but rather if you hav…

Symbolic AI ala Mycin and other expert systems didn't do anything that a modern database query engine can't do with far greater performance. The bottleneck is coming up with the set of rules that the system is to follow.

Early production rules engines really sucked, like a lot of the time they didn't have any kind of indexes and full scanned a lot. Good RETE engines with indexes didn't get mainstream by the 1980s but the industry was already losing interest. In a lot of ways

https://en.wikipedia.org/wiki/Drools

is pretty good as is the Jena rules engine but none of these have ways of dealing with uncertainty which are necessary if you're going to be working with language and having to decide which of 10,000 possible parses is right for a sentence. People used to talk as if 10,000 rules was a lot but handling 2 million well-organized rules with Drools is no problem at all today.

I think the problems of knowledge base construction are overstated and that a lack of tools are the problem. Or rather, the Cyc experience shows that rules are not enough, that is, after Lenat died it got out that Cyc didn't just have a big pile of facts and rules and a general reasoning procedure but it had a large database of algorithms to solve specific problems. That is, in principle you can solve anything with an SMT solver but if you actually try it you'll find you can code up a special-purpose algorithm to do common tasks before the SMT solver really gets warmed up.

Part of the production rules puzzle is that there never was a COBOL of business rules rather you got different systems which took different answers to various tricky problems like how to control the order of execution when it matters, how to represent negation, etc.

Re: Why we stopped using the mathematics that works

#38

Post by Tomte »

pron wrote: post #30

> This is the VHS-versus-Betamax dynamic, or TCP/IP versus the OSI model, or QWERTY versus every ergonomic alternative proposed since 1936. The technically superior solution loses to the solution that’s easier to deploy, easier to hire for, and good enough for the use cases that pay the bills. Without commenting on the merit of the claims, the problem with this statement is that in many cases there is no universal "t…

And there was a real cliff in recording time, not a marginal difference: a normal VHS tape could record a typical TV show, a normal Betamax tape could not. The utility function is a step function here.

(Both got more recording times through Long Play techniques a.k.a. quality degradation and through actually longer magnetic tape in the cassette, but at least in the beginning it was clear-cut).

Re: Why we stopped using the mathematics that works

#39

Post by steppi »

I spent some time in industry working on ML-based credit risk modeling. In my experience, successful shops that have a genuine interest in applying their models to practical decision making with real stakes care deeply about uncertainty quantification and decision theory. Things can get messy very fast though and the challenges faced are often too hyper-specific to one's situation to make sense as part of an academic research program. I think it's been for the best that academic research has tended to focus on the development of general algorithms intended to be broadly useful. Businesses are already well incentivized to take the best of what academia produces and try to get the decision theory right for their particular problems.

Re: Why we stopped using the mathematics that works

#40

Post by ontouchstart »

jebarker wrote: post #35
ontouchstart wrote: post #5

We are at the age of alchemy, wait for the age of chemistry and physics. New mathematical foundations are yet to be found.

I’m not convinced that useful mathematical foundations will be found anytime soon. Neural nets exist because we want to make decisions in a world that is so noisy, complex and chaotic that we can’t satisfy the requirements of more rigorous analytical frameworks. It seems to me that the irreducible complexity is in the real world, not the neural networks.

Never underestimate the power of mathematics. On the other hand, a lot of mathematical breakthroughs in the history are not found by people doing mathematics but in physics and engineering. Calculus, statistics, information theory, etc.

Even for computer science, take a look at Turing Award from 1966 [0], we will see how short sighted we are if we only follow the trend. Time will tell and smart people will find new path.

[0]: https://amturing.acm.org/byyear.cfm

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