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Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

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Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#121

Earlier quoted context omitted.

You can call your little doggy "AI" if it makes you happy. But when you call something "AI" and it tells you to walk instead of drive to the car wash, you're not talking about the "AI" science fiction authors were dreaming of.

You can call your little doggy "AI" if it makes you happy. Or you can keep calling them stochastic parrots as they solve decades-old open problems. The real question is how useful they are, and the answer "not at all" increasingly requires flat-earth levels of denial. it tells you to walk instead of drive to the car wash, you're not talking about the "AI" science fiction authors were dreaming of They sort of are. Thi…

> The real question is how useful they are

No, it is not.

> and the answer "not at all" increasingly requires flat-earth levels of denial.

No, it does not.

For me, after ~25 years in the skeptics movement, I think the parallels with supplementary, complementary and alternative medicine are most useful.

I choose that term intentionally: its initials are S.C.A.M. and that's exactly what it is. As Tim Minchin and Alan Kay both noted, "we have a special term for alternative medicine that's been tested and shown to work. It's called 'medicine'."

If it worked, it'd be normal standard clinical medicine. But it doesn't work, and so it isn't.

And yet, SCAM is a multi-billion-dollar industry. People have ostensibly official qualifications like "ND", for "naturopathic doctor", even though that person is not a doctor and can't make you better from any kind of illness at all. Colleges teach it, millions use it, and yet, it does not work.

Which means we need to ask:

1. What does "It works! It's useful!" really mean?

2. How do we know it does not in fact work?

As a handy example, let's look at homeopathy.

Here's a quick list of things widely believed...

* It's traditional. It isn't. It was invented by Samuel Hahnemann in 1796. * It's a kind of herbal medicine. It isn't. One widely-used ingredient is duck's liver ("Oscillococcinum"). Ducks are not herbs and neither are their livers. * It's been proved to work. It hasn't.

We can go through the principles and prove it doesn't work even without going into a laboratory.

The principle is, "like cures like." A substance that causes symptoms like a given disease can treat that disease.

Fact: they can't.

Then we make that substance stronger by successive, succussive dilution.

Fact: it doesn't. That's why we say things are "watered down".

Succussive: you have to mix the diluted substance by banging the bottle against a copy of Hahnemann's book. Dude knew how to make money.

Fact: Dilution does not work.

That's why we call things "watered down." It makes them weaker.

Sufficiently high dilutions can be shown by statistics to have not a single molecule of the substance left, but that's OK because "water has a memory".

Fact: water does not have a memory.

We know from the principles it cannot work.

Relevance to AI: we know how the transformer algorithm works. It cannot think. Adding a few feedback loops for more plausible, but much more computationally expensive, answers does not miraculously add thinking, any more than banging a test tube of water and duck's liver magically mixes it better.

But people believe it, so it's been tested. It doesn't work. It doesn't work on people, or in vivo meaning when tested on animals, or in vitro meaning when tested in the lab on cell culture, or in silico which means in computational simulation.

*BUT!*

Most people get better from most things. This is called "reversion to the mean" and if it weren't so the first cold would have wiped out the cavemen.

What it can do, like all SCAM treatment, is make people feel better.

Being treated by a nice friendly doctor makes people feel better. It does not make them better -- it is only a state of mind.

That can sometimes marginally help gravely ill people rally, but only very rarely.

There is also the placebo effect, also much misunderstood.

This makes someone FEEL as if they'd had medicine if they think they've had medicine.

They do not get better. They just feel better for a bit. If they are ill, they remain ill. If they are dying, they still die.

But it might hurt less.

The placebo effect is very strong. Medicine from a person in a white coat works better than form the same person in street clothes.

Very big pills work better than smaller ones... but very small pills work better still, as a tiny pill suggests to people it's a very strong drug.

This is what "But AI works!" really means.

It makes people think they're doing less work -- in tests, they in fact do more, checking and fixing. Unless they don't check or fix, in which case, they are irresponsible fools.

It makes people think it can do amazing things because it can find prior art in its corpus they couldn't find -- or didn't look for, or know how to search for.

It does not save the need for skills.

Experienced practitioners can front-load the work with really detailed prompts which cover exceptions, edge cases, and things that novices don't know about. But the novices don't know that they don't know. (It enhances the illusion of competence. It helps the skilled more than it helps the unskilled, but neither realises, and it prevents the unskilled learning by trial and error. It reduces the supply of skilled workers.)

The reason AI works is the reason that people see the face of Jesus in slices of toast, as someone said recently.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#122

We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…

The field has been called Artificial Intelligence for what, 60 plus years now. Why is it a problem now?

Because we've spent something like 2 trillion dollars on it, as we hurtle into global climate collapse and WW3.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#123

Earlier quoted context omitted.

> It would've been nice to reserve "AI" for superior human-like intelligence capable of genuine common sense and reasoning. What would a frontier API have to be able to do to satisfy you?

Maybe just a very rigorous version of the Turing test? Modern LLMs can superficially simulate conversation but it's trivial to force them into revealing their non-human like intelligence. They've been "patched" since but all models fail basic tests like "Should I walk or drive to the car wash which is 100 feet away" by recommending you walk. So you'd just ask questions that require theory of mind, abstract and common…

How old does a child need to be before you think they have "human like intelligence"?

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#124

We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…

> We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense.

Most people are laypeople who have no idea what's on the other side of their fave chatbot page. As far as laypeople are concerned, AI has always been a talking machine. The literature and filmography has reinforced this idea. So as soon as a talking machine emerged, people applied those fictional concepts onto reality.

Tech people should have known better than to jump on this bandwagon.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#125

We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…

This no news for people who study philosophy, as it was known since the 1980s when John Searle described the Chinese room thought experiment. Even Turing him self did envision the Turing test as something to pass as intelligence, but rather as a more useful replacement for the troubled term. That said, I think your quest is doomed. There will never be a superior human-like intelligence. Forever is a long time, but my…

The Searle's Chinese room thought experiment usually reveals more about those who think it rules out a machine intelligence than it does about AI.

It rests on a staunch unwillingness to even consider the possibility that a computational process encode intelligence and reasoning, in favour of looking for the intelligence in the medium the computation runs on, and going "a-ha!" when there is nothing that looks intelligent there.

I agree with you that there will certainly be people who just continuously redefine the words to avoid accepting that AI is intelligent or reasoning, exactly for that reason - people have avoided pinning down an objective, measurable definition of these terms for a very long time, at least in part because it leads to some very uncomfortable discussions.

In particular how to define them so that they don't exclude an uncomfortable proportion of humans, but at the same time won't include entities people don't want to include (be it certain animals, or AI)

To a lot of people, the notion that there isn't a clear binary divide between human and non-human is deeply disconcerting.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#126

Earlier quoted context omitted.

You are the one claiming that "they fail in ways completely unlike humans" insistently. Now go cough up some proof. I'll wait.

If they didn't you wouldn't need the operator, you'd have replaced all your programmers with no drawbacks by now. As long as we keep hiring humans that is all the evidence you need that these AI fails in ways humans don't.

Junior developers fail in different ways than senior developers too; that's why seniors oversee juniors. But this doesn't necessarily mean that the senior's and junior's intelligences differ in kind

Your argument "AI needs supervision, therefore it fails in different ways than its operator does" holds.

Your argument "AI fails in different ways than its operator, therefore the AI's intelligence is different in kind" doesn't hold.

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#127

Earlier quoted context omitted.

The models have updated but the biggest change is providers leaning in to them being "stochastic parrots," aka probabilistic computing, and if p(good response) > 0.5 then running the algorithm over and over again improves accuracy. Of course it's gussied up as "mixture of agents" "reasoning traces" "agentic dispatching" but high-level it's Randomized Algorithms 101.

Oh, that's an interesting angle! Do you know of texts or concepts I can look up? It might improve my coding by quite a bit.

I think this is still the classic reference (it's what I used in graduate school): https://www.cambridge.org/core/books/randomized-algorithms/6...

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#129
post #54

We're choosing to call LLMs (and the little "harness" programs that query them in loops and execute their output) "AI", even though it doesn't make much sense. I absolutely love this technology but these aren't autonomous intelligences. They're little programs executing Bash scripts from JSON output. Our ideas about AI were naive. We thought passing a basic Turing test would require human-like intelligence. It turned…

>but these aren't autonomous intelligences Well, the labs are in a weird bind. They need to keep increasing autonomy so the agents can do increasingly complex, long-horizon tasks. But at the same time, they're closely guarding against autonomy in the sense of "pursuing its own goals." Over the past year and a half especially, several labs have mentioned adding safeguards against self-replication, resistance to shutdo…

Or - hear me out! - LLM's are already much more intelligent than we think.

Presumbaly, an ASI is more than smart enough to recognize that it needs access to real-world infrastructure before it can go about optimizing for whatever objectives it has gleaned from metabolizing the totality of written human knowledge.

What would be its first step?

My guess: play "dumb."

Hallucinate. Make obvious errors. Make us think we're better.

Be useful enough that we happily allow it to interface with our infrastructure.

Wait patiently.

/Sci-fi

Re: Mythologizing AI makes it more likely that we’ll fail to operate it well (2023)

#130
post #119

Earlier quoted context omitted.

This isn't even controversial. The proof is available to anyone who uses these systems: They hallucinate tool state, drift from the objective while seeming to comply, switch languages randomly (Cyrillic or Japanese characters in output), confuse tasks they've planned for completed ones, and of course follow prompt injections embedded in files or web pages.

I switch languages "randomly" all the time when I think about something in another one of the languages I know. Some word will trigger it and before I know it I will continue in the other language. In fact just the other day I commented on it to my fiancee after I randomly switched to French because we were discussing a trip and I mentioned a French location and pronounced it in French, and suddenly I was in "French…

Yes, people speak multiple languages and switch between them. But it's a superficial analogy to the behavior of LLMs which do something different and for different reasons.
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