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True, and this even happens in a brain the size of a flea's. Which makes one wonder, what is it that makes processing and reconciling millions of visual signals per second "easy", but reasoning through a simple sudoku near impossible?
Are you sure about fleas? I thought we know this does not happen with frogs as far as we can tell https://courses.csail.mit.edu/6.803/pdf/lettvin.pdf
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> In truth, the LLM is an idiot-savant which dreams up "fitting" additions to a given document. Tbh I'm not too sure that my brain works fundamentally differently. I'm an idiot-savant who responds to stimuli.
Do you know the number of atoms in my coffee cup right now? If you say "No, I don't" then you're already way ahead of any LLM, even the biggest ones. Remember, I asked about how many are in there right now . The only correct answer is "I don't know", because I haven't even told you whether my mug is full or empty. Here's copilot's answer: how many hydrogen atoms are there in my coffee cup right now Copilot said: That…
Even stupid models understand that if I ask them the population of Denmark they only need to give rough approximation.
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Are you sure about fleas? I thought we know this does not happen with frogs as far as we can tell https://courses.csail.mit.edu/6.803/pdf/lettvin.pdf
Oh. No, not sure.
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I think the problem is the way you are phrasing your argument implies the LLM is always wrong. Consider a simple prompt: "Write a hello world in Python." Every LLM i've tested gets this correct. In my mind, it can't be both bullshit and correct. I would argue that the amount of real bullshit returned from an LLM is correlated to the amount of bullshit you give it. Garbage in, garbage out. In the end, its irrelevant i…
It only gives you the statistically more likely way a conversation would evolve after one party says "Write a hello world in Python." It only happens to be the correct one. If I ask a 5yo "42 * 21 equals...?" and the kid replies with a random number, say, "882", and gets it right, it does not mean that the kid knows what multiplication is or how it works.
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It only gives you the statistically more likely way a conversation would evolve after one party says "Write a hello world in Python." It only happens to be the correct one. If I ask a 5yo "42 * 21 equals...?" and the kid replies with a random number, say, "882", and gets it right, it does not mean that the kid knows what multiplication is or how it works.
ChatGPT can use a stateful python environment to do math. It isn’t confabulating the answers, it’s using a calculator.
I'm not arguing for or against anything specifically, I just want to note that in practice I assume that to the LLM it's just a bunch of repeating prompts with the entire convo, and after outputting special 'signifier' tokens, the llm just suddenly gets a prompt that has the results of the program that was executed in an environment. for all we know various prompts were involved in setting up that environment too, but I suspect not.
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> It's just that since the masses found that they can talk to an AI like a human In a way it's worse: Even the "talking to" part is an illusion, and unfortunately a lot of technical people have trouble remembering it too. In truth, the LLM is an idiot-savant which dreams up "fitting" additions to a given document. Some humans have prepared a document which is in the form of a a theater-play or a turn-based chat trans…
Just wanted to point out that the notion of a "document" is also an illusion to the LLM. It's processing a sequence of low dimensional spaces into another sequence of low dimensional spaces. The input spaces preserve aspects of content similarity based on co-occurrence. The model learns to transform these spaces into higher order spaces based on the outcome of training. You couldn't say that the model has a singular…
Is the notion of an LLM operating on a document useful for building an intuition about how the LLM operates? That’s the more interesting question.
Maybe I just don’t understand the article but I really have 0 clue how they go about making their conclusions and really don’t understand what they are saying. I think the 5 issues they provide under “Cognitive Architectures” are severely underspecified to the point where they really don’t _mean_ anything. Because the issues are so underspeficifed I don’t know how their proposed solution solves their proposed problem…
This is publishing for the sake of publishing.
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Just wanted to point out that the notion of a "document" is also an illusion to the LLM. It's processing a sequence of low dimensional spaces into another sequence of low dimensional spaces. The input spaces preserve aspects of content similarity based on co-occurrence. The model learns to transform these spaces into higher order spaces based on the outcome of training. You couldn't say that the model has a singular…
For humans, the notion of “self” is also an illusion. We’re just atoms dancing to physics. But that’s not the right level of abstraction from which to do psychology. Is the notion of an LLM operating on a document useful for building an intuition about how the LLM operates? That’s the more interesting question.
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Do you know the number of atoms in my coffee cup right now? If you say "No, I don't" then you're already way ahead of any LLM, even the biggest ones. Remember, I asked about how many are in there right now . The only correct answer is "I don't know", because I haven't even told you whether my mug is full or empty. Here's copilot's answer: how many hydrogen atoms are there in my coffee cup right now Copilot said: That…
No, this is the correct answer. This is the answer I'd be looking for if I was asking that question. That you failed to specify the question is a failure on your part. Even stupid models understand that if I ask them the population of Denmark they only need to give rough approximation.
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No, this is the correct answer. This is the answer I'd be looking for if I was asking that question. That you failed to specify the question is a failure on your part. Even stupid models understand that if I ask them the population of Denmark they only need to give rough approximation.
no, i expect the system to answer the question I asked. Not the question it thinks I wanted to ask. The question is not underspecified, because the point of it is to demonstrate how the llm will never tell you it doesn't know. Nor will it ever tell you your prompt is underspecified.