Comment by ACCount37

Comment by ACCount37 3 days ago

ML doesn't work like programming because it's not programming. It just happens to run on the same computational substrate.

Modern ML is at this hellish intersection of underexplored math, twisted neurobiology and applied demon summoning. An engineer works with known laws of nature - but the laws of machine learning are still being written. You have to be at least a little bit of a scientist to navigate this landscape.

Unfortunately, the nature of intelligence doesn't seem to yield itself to simple, straightforward, human-understandable systems. But machine intelligence is desirable. So we're building AIs anyway.

ath3nd 3 days ago

> Modern ML is at this hellish intersection of underexplored math, twisted neurobiology and applied demon summoning

Nah, it's just a very very big and fancy autocomplete with probabilistic tokenization and some extra tricks thrown in to minimize the shortcomings of the approach.

> Unfortunately, the nature of intelligence doesn't seem to yield itself to simple, straightforward, human-understandable systems.

LLMs are maybe artificial but they are not intelligence unless you have overloaded the term intelligence to mean something much less and more trivial. A crow and even a cat is intelligent. An LLM is not.

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ACCount37 3 days ago

That's copium.
The proper name for it is "AI effect", but the word "copium" captures the essence perfectly.
Humans want to feel special, and a lot of them feel like intelligence is what makes them special. So whenever a new AI system shows a new capability that was thought to require intelligence? A capability that was once exclusive to humans? That doesn't mean it's "intelligent" in any way. Surely it just means that this capability was stupid and unimportant and didn't require any intelligence in the first place!
Writing a simple short story? Solving a college level math problem? Putting together a Bash script from a text description of what it should do? No intelligence required for any of that!
Copium is one hell of a drug.

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- ath3nd 3 days ago
  
  > Copium is one hell of a drug.
  What is the word for creating an account 12 days ago and exclusively defending the LLMs because they can't defend themselves?
  > Writing a simple short story
  Ah, allow me to introduce you to the Infinite Monkey theorem.
  https://en.wikipedia.org/wiki/Infinite_monkey_theorem
  In the case of LLMs it's just the monkey's hand is artificially guided by all the peanut-guided trainings it was trained on but it still didn't use a single ounce of thought or intelligence. Sorry that you get impressed by simple tricks and confuse them for magic.
  
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  ACCount37 3 days ago
  
  And this proves what exactly? That any task can be solved by pure chance at pass@k, with k blowing out to infinity as the solution space grows?
  We know that. The value of intelligence is being able to outperform that random chance.
  LLMs already outperform a typewriter monkey, a keyboard cat, and a non-insignificant amount of humans on a very diverse range of tasks.
  
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voidhorse 3 days ago

You should read some of the papers written in the 1940s and learn about the history of cybernetics. Your glowing perception of the "demon summoning" nature of ML might change a bit.

People want others to think this tech is mysterious. It's not. We've known the theory of these systems since the mid 1900s, we just didn't fully work out the resource arrangements to make them tractable until recently. Yes, there are some unknowns and the end product is a black box insofar as you cannot simply inspect source code, but this description of the situation is pure fantasy.

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ACCount37 3 days ago

Good luck trying to use theory from the 1940s to predict modern ML. And if theory has little predictive power, then it's of little use.
There's a reason why so many "laws" of ML are empirical - curves fitted to experimental observation data. If we had a solid mathematical backing for ML, we'd be able to derive those laws from math. If we had solid theoretical backing for ML, we'd be able to calculate whether a training run would fail without actually running it.
People say this tech is mysterious because it is mysterious. It's a field where practical applications are running far ahead of theory. We build systems that work, and we don't know how or why.

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- skydhash 3 days ago
  
  We have solid backing in maths for it. But the fact is what we are seeking is not what the math told us, but an hope that what it told us is sufficiently close to the TRUTH. Hence the pervasive presence of errors and loss functions.
  We know it’s not the correct answer, but better something close than nothing. (close can be awfully far, which is worse than nothing)
  
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  ACCount37 2 days ago
  
  The math covers the low level decently well, but you run out of it quick. A lot of it fails to scale, and almost all of it fails to capture the high level behavior of modern AIs.
  You can predict how some simple narrow edge case neural networks will converge, but this doesn't go all the way to frontier training runs, or even the kind of runs you can do at home on a single GPU. And that's one of the better covered areas.
  
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