181–190 of 205 posts
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Isn't it possible that intelligence is P(words|every sequence of words you've ever heard)?
Then where would sensible words come from, for the first time, in hominid history?
Can anyone explain why the GPT response to the same question 4 days ago is different from today's: [ 4 days ago] > Can I learn to sing like Pavarotti? It is possible to learn to sing like Pavarotti, but it will require a lot of practice, dedication, and training. Pavarotti was a renowned opera singer known for his incredible vocal range, powerful voice, and technical mastery. He had many years of training and experie…
This is by design (and implemented as explained in the sibling comments). The reason for the randomness is to avoid returning the same thing all the time. For example if you ask it "write me a poem" you don't want it to return the exact same one each time. You can also set the "temperature" parameter to 0 to remove the non determinism (though I'm not sure that's exposed in the web interface).
Imagine Wolfram's system with GPT integrated. I hope they're working on that.
Current GPT version is not very good at math, and it's not at all clear whether this can be fixed within a language model. Q: I have 2 cats and 1 horse. How many legs do they have in total? A: If you have two cats and one horse, then the total number of legs they have is 8. Cats typically have four legs, while horses have four legs as well. So two cats would have a total of 8 legs, and when you add the four legs of t…
Here's a "proof" of Gödel's Completeness Theorem:
Prompt: Write a proof of Gödel's Completeness Theorem
Answer: Gödel's Completeness Theorem states that for any consistent, effective axiomatic system for a first-order language, if a sentence is true in all models of the system, then it is provable in the system.
To prove this theorem, we must first show that if a sentence is true in all models of a system, it must be true in at least one model of the system. Suppose that a sentence is true in all models of a system. This means that for any possible interpretation of the symbols and variables in the sentence, the sentence will be true. However, since the sentence is true in all models, it must be true in at least one model, otherwise it would not be true in all models.
Next, we must show that if a sentence is true in at least one model of a system, it is provable in the system. Suppose that a sentence is true in at least one model of a system. By the soundness of the system, if a sentence is provable in the system, it must be true in all models of the system. Since the sentence is true in at least one model, it must be true in all models of the system, and therefore it is provable in the system.
Therefore, if a sentence is true in all models of a system, it is true in at least one model of the system, and if it is true in at least one model of the system, it is provable in the system. This completes the proof of Gödel's Completeness Theorem.
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Would you mind expanding on it a bit? I do sincerely appreciate its pithiness, but curious to read it explained a bit further.
Think of it as: reasoning=computation, knowledge=data. Data alone doesn’t say it must be computed. But computation, by definition, is attempting to create data (the result) that doesn’t exist. Thus: knowledge isn’t aware it must be reasoned about, but reasoning knows it’s trying to find (deduce, compute) knowledge it lacks.
By definition, an optimal compression algo is a dimensionality reduction algo. A dimensionality reduction algo lets you do a bunch of machine learning tasks.
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The problem with ChatGPT's "knowledge" is that it isn't trustworthy. It will happily output very confident sounding nonsense, or blatantly incorrect statements. We need a way to verify how accurate it's outputs are
Whose knowledge is trustworthy? We've somehow come to associate certain institutions or scientific authorities with truth when that is about the furthest from real science: "Have no respect whatsoever for authority; forget who said it and instead look what he starts with, where he ends up, and ask yourself, Is it reasonable?" -Richard P. Feynman "One of the great commandments of science is, "Mistrust arguments from a…
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Fascinating - makes me think of the distinction between dreaming and being awake. Only in the latter state one can tell the difference.
Not quite true, see lucid dreaming.
Amazing insight, particularly section 6. "- The two important but different abilities of GPT-3.5 are *knowledge* and *reasoning*. Generally, it would be ideal if we could *offload the knowledge part to the outside retrieval system and let the language model only focus on reasoning.* This is because: - The model’s internal knowledge is always cut off at a certain time. The model always needs up-to-date knowledge to an…
This suggests that there is some underlying structure related to our EQ and IQ that we learn through our bodies and the knowledge we gather from the world. The relationship between memory distillation, emotions, and reasoning could lead to some insights as to what this structure is. I would speculate that the refined structure is universal for all conscious beings, and that it can be formulated as a theory involving geometric invariance, similar to the standard model.
The LLM as simulators description is apt [0]. ChatGPT can be understood as an interface for navigating a knowledge space that offloads most reasoning to its users, much like a search engine. Generative models like GPT create a latent space but their ability to navigate it relies on flowing along the natural latent topology, meaning it uses probabilistic reasoning and needs carefully constructed prompts to find good starting points that don't descend into local extrema. Alternatively, the latent space could be given guard rails through RLHF or have base knowledge distilled and curated to smooth out the resulting topology.
[0] https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators
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The problem with ChatGPT's "knowledge" is that it isn't trustworthy. It will happily output very confident sounding nonsense, or blatantly incorrect statements. We need a way to verify how accurate it's outputs are
One way I tried to do this is by having it write an answer, and a footnote reference at each fact. [1] then list search terms that be used to verify each claim, then I would respond with the url and quotes from found pages for each one, then have it rewrite the answer based on that information and cite the sources. I think something this direction can be automated. I saw someone do this with math and other tasks, tha…
Earlier quoted context omitted.
The problem with ChatGPT's "knowledge" is that it isn't trustworthy. It will happily output very confident sounding nonsense, or blatantly incorrect statements. We need a way to verify how accurate it's outputs are
so, much like other knowledge sources?
Can anyone explain why the GPT response to the same question 4 days ago is different from today's: [ 4 days ago] > Can I learn to sing like Pavarotti? It is possible to learn to sing like Pavarotti, but it will require a lot of practice, dedication, and training. Pavarotti was a renowned opera singer known for his incredible vocal range, powerful voice, and technical mastery. He had many years of training and experie…
Large language models like GPT work by generating the probabilities for the next word in a sequence, given the previous words. You can make this purely deterministic (same sequence every time) by just selecting the word with the highest probability repeatedly. Most models will inject some randomness by sampling from the probabilities, to make the generated text more realistic. I don't know what parameters they use fo…
They definitely are. That's why the 'try again' button doesn't just return the same text again.