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My current method for trying to break through this misconception is informing people that nobody knows how AI works. Literally. Nobody knows. (Note that knowing how to make something is not the same as knowing how it works. Take humans as an obvious example.)
[1]: https://www.reddit.com/r/slatestarcodex/comments/1o6n5ne/why...
Earlier quoted context omitted.
Yes, but we can't inspect, reproduce or explain the emergent property independently. We can't pick out the "math reasoning" part or the "programming" part, or inspect how it's working, or selectively change any of it. You can't turn any dials or twiddle any knobs. You can't replace one part with another, or pick out components. You can't peek inside and say: "hey it's got an irrational preference for naming its varia…
So just because all the parts are collaborating to get the desired outcome, and specific aspects of that outcome cannot be contributed to specific parts of the llm you think we don’t understand LLMs? With mixture of expert systems we’re introducing dedicated subsystems into the llm responsible for specific aspect of the llm, so we’re partially moving in that direction. But overall in my opinion if devs are able to re…
Common misconception, MoEs do have different "experts", but the model learns when to send input to different experts, and the model does not cleanly send coding tasks to the coding agent, physics tasks to the physics agent, etc. It's quite messy, and not nearly as intepretable as we'd want it to be.
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Being able to make it and knowing how it works are just not the same thing. I can make bread of consistent quality, and know how to improve certain aspects of it. To know how it works , I’d need to get a doctorate in biochemistry and then still have a fairly patchy understanding. I know plenty of people who can drive a car very successfully but would never claim they know how it works.
But with LLMs is there really more to understand? They’re just large functions that take numerical input and transform it into numerical output based on trained weights. There is nothing behind the scenes doing things we don’t understand. The magic is in the weights, and we know how to create these based on training data. Regarding the car, if you know how to build a car, you understand how a car works. A driver is m…
Yes! loads! (: I want to be able to say statements like "this model will never ask the user to kill themselves" and be confident, but I can't do that today, and we don't know how. Note that we do know how to prove similar statements for regular software.
My current method for trying to break through this misconception is informing people that nobody knows how AI works. Literally. Nobody knows. (Note that knowing how to make something is not the same as knowing how it works. Take humans as an obvious example.)
Well we are happy to work with other humans without knowing how they work.
My current method for trying to break through this misconception is informing people that nobody knows how AI works. Literally. Nobody knows. (Note that knowing how to make something is not the same as knowing how it works. Take humans as an obvious example.)
How is it possible that nobody knows how it works - it’s running on hardware we have complete control over and perfect observability into, is it not? At any frame we can pause, examine the state, then step forward, examine the state, and observe what changes have occurred - we have perfect knowledge of the source code, the compiler, whatever components you prefer to break software down into - What is it that we don’t…
This example from software doesn't meaningfully hold for neural networks. It's a bit like trying to watch an individual COVID virus duplicate and then attempting to predict the pandemic. It's incredibly complicated and we haven't yet built the tools to help us understand
My current method for trying to break through this misconception is informing people that nobody knows how AI works. Literally. Nobody knows. (Note that knowing how to make something is not the same as knowing how it works. Take humans as an obvious example.)
Nobody knows (full scope and on every level) how human brains work. Still bosses rely on their employees' brains all the time.
I'm a bit troubled with the phrasing > most AI companies will slightly change the way their AIs respond, so that they say slightly different things to the same prompt. This helps their AIs seem less robotic and more natural. To my understanding this is managed by the temperature of the next token prediction which is picked more or less randomly based on this value. This temperature plays a role in the variability of…
This is true, but sampling also plays a fairly large role. The model will produce probabilities for the next token, temperature will modify these probabilities somewhat, but different sampling techniques (top-K, top-P, beam search, others) will also change these probabilities.
> I wasn't under the impression that it was to give the user a feeling of "realism", but rather that it produced better results with a slightly random prediction.
My understanding is that it's a bit of both. If the AI responded exactly the same way to every "hi can you help me" prompt, I think users' would call it more robotic. I also think that slightly varying the token prediction helps prevent repetitive text
The most likely danger with AI is concentrated power, not that sentient AI will develop a dislike for us and use us as "batteries" like in the Matrix.
The most likely danger with AI is concentrated power, not that sentient AI will develop a dislike for us and use us as "batteries" like in the Matrix.
"AI will take over the world". I hear that. Then I try to use AI for simple code task, writing unit tests for a class, very similar to other unit tests. If fails miserably. Forgets to add an annotation and enters in a death loop of bullshit code generation. Generates test classes that tests failed test classes that test failed test classes and so on. Fascinating to watch. I wonder how much CO2 it generated while fryi…
It's well worth looking at https://progress.openai.com/, here's a snippet:
> human: Are you actually conscious under anesthesia?
> GPT-1 (2018): i did n't . " you 're awake .
> GPT-3 (2021): There is no single answer to this question since anesthesia can be administered [...]
The most likely danger with AI is concentrated power, not that sentient AI will develop a dislike for us and use us as "batteries" like in the Matrix.
For one thing, we'd make shit batteries.