281–290 of 306 posts
How hard is it to do fine tuning? I thought it would be cool to tune one to be good at debating and changing peoples minds about pickleball.
If you're creating an AI to dissuade people from pickleball then this is truly AI alignment promised land. If, however, your AI is arguing in favor of it, then we have truly entered the age of renegade AI.
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Right, but there's no contamination studies there. I suspect that RLHF data leaked HumanEval into GPT-4. It just seems unlikely to me that GPT-4's coding abilities have improved since March (when 67% was officially reported by OpenAI) given all of the examples and anecdotes about degradation. This is why we use the official numbers.
(Chat)GPT-4s practical coding abilities are now 100x because it can code, run the code, and reason about its performance mid-response. They must be using fine tunes for this so the overall model could well be better too
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Sounds like schadenfreude.
Perhaps it is because it is true and both of you are showing signs of great denial. Engineers will be replaced by those that use AI and it will only accelerate and there will be less of them hired.
So 10x engineers will replace normal engineers? Although this seems plausible, the advent of the 10 or 100x engineer isn't something new (and it didn't put millions of engineers out of work, although it created the biggest corporations to ever exist).
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Let's not call it open source. Even Llama2 doesn't think that Llama2 is open source: https://imgur.com/AZFOzWk
Is this really the line we want to draw in the sand? It’s not open source because it can’t be trivially used by AWS and Google? It feels like the popularism is the point, not the complete lack of restrictions.
If it were really "open source", nobody would be complaining about its moralizing attitude. They'd just make a new one, without the attitude.
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It is offensive if you take the output personally. You are interacting with the model, but the model isn't interacting with you. The model doesn't know who you are. It could be the bad actors currently confined to the spam folder of your email making these requests, and the model wouldn't know the difference.
These responses are hard-coded by developers, we know this because it's the same stock response every time. It is personal because it's not the model, it's a wrapper around the model enforcing US-centric cultural censorship norms onto the rest of the world. I understand the optics around why FB/OpenAI/etc do this, (as a sibling user posted), but make no mistake, it is no accident that it talks to you in a condescendi…
The response quoted above makes no mention of offensiveness, and I explained the need to decline certain requests above.
I understand your sentiment, and I would agree if it was the stock response we are used to from ChatGPT. Unlike the condescending stock response from ChatGPT, however, the response in question is to the point, honest, and provides useful feedback.
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> So despite its flaws and mistakes, I still find it to be a tremendously useful tool, even if only to point me in the right direction. Much of this resonates. That said, I get tremendous value simply by writing things down (or dictating them) and replying to my own question. I would expect that a sizable fraction of people have forgotten about these strategies and/or don't use them when they are most useful. For man…
> I'm not amazed in the way you are. I expect a variation in quality across topics and domains and question styles. Yes, I can see that. But over time, you also learn and adapt the prompts to ChatGPT's peculiarities so that it provides more useful output. Still, I'm sure there are many topics/domains for which it's not useful. As another anecdote, I'm not a mathematician but at one point I was playing around with pro…
A quick thought about your success: ChatGPT's imprecision and stochasticity can work in its favor for many creative efforts. Unexpected token connections can have a lot of value in a space where vast numbers of novel directions are worthwhile.
For me, having spent thousands of hours thinking about statistics, ML, logic, and reasoning, ChatGPT is not paradoxical. To me, the human aspect is more interesting; namely, the ways in which people are surprised reveals a tremendous diversity in people's expectations about intelligence, algorithms, and pattern-matching.
For many people, most of the time, basic reasoning is a basic requirement for intelligence. By themselves, sequence to sequence models are not computationally capable of deductive reasoning with an arbitrary number of steps, since that would require recursion (or iteration).
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> I'm not amazed in the way you are. I expect a variation in quality across topics and domains and question styles. Yes, I can see that. But over time, you also learn and adapt the prompts to ChatGPT's peculiarities so that it provides more useful output. Still, I'm sure there are many topics/domains for which it's not useful. As another anecdote, I'm not a mathematician but at one point I was playing around with pro…
Anecdotes sometimes take a beating, but I happen to like the personal ones. Thanks for sharing. A quick thought about your success: ChatGPT's imprecision and stochasticity can work in its favor for many creative efforts. Unexpected token connections can have a lot of value in a space where vast numbers of novel directions are worthwhile. For me, having spent thousands of hours thinking about statistics, ML, logic, an…
> By themselves, sequence to sequence models are not computationally capable of deductive reasoning with an arbitrary number of steps, since that would require recursion (or iteration).
Isn't the fact that LLMs perform their inference step by step, where in each step they output only one token, an instance of deductive reasoning with a (potentially) arbitrary number of steps?
I say this because on each inference step, the tokens that were previously generated do become part of the input.
At a higher level of abstraction, I'm also thinking about chain-of-thought prompting, in which the LLMs first output the easier-to-deduct steps, then build on these steps to perform more deductive steps up until they finally produce the desired answer [1].
Of course, they have a limited context, but the context can be (and has been) increased. And humans have a limited context as well (except if we consider long-term memory or taking notes, perhaps).
The main difference I see is that in LLM chain-of-thought reasoning, they are currently outputting their intermediate "thoughts" before actually giving the final answer, whereas we humans are capable of silencing ourselves before actually having figured out the answer, which we then "output" as speech [2].
So I think there is still a form of recursion or iteration happening in LLMs, it's just that it's in a somewhat limited form in that we are observing it as it happens, i.e. as they output tokens one-by-one.
That said, something that I think could really make LLMs take a big step forward would be to have something akin to long-term memory. And the other big step would probably be being able to learn continuously, rather than only during their training. These two potential steps might even be the same thing.
So I don't know. I'm obviously not an expert but these are my thoughts with regards to what you've just said.
[1] https://ai.googleblog.com/2022/05/language-models-perform-re...
[2] Interestingly, there have been studies that show that humans produce micro-speech patterns when we are thinking, i.e. as if we are really speaking, although imperceptibly. That said, I have no idea how trustworthy these studies are.
Edit: added a clarification at the beginning.
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Anecdotes sometimes take a beating, but I happen to like the personal ones. Thanks for sharing. A quick thought about your success: ChatGPT's imprecision and stochasticity can work in its favor for many creative efforts. Unexpected token connections can have a lot of value in a space where vast numbers of novel directions are worthwhile. For me, having spent thousands of hours thinking about statistics, ML, logic, an…
I don't think I've spent nearly as much time as you thinking about these things and I'm not entirely sure I understood your perspective, but I have a couple of reflections for you which perhaps you can comment on: > By themselves, sequence to sequence models are not computationally capable of deductive reasoning with an arbitrary number of steps, since that would require recursion (or iteration). Isn't the fact that…
Second, your comment above covers the ground I was referring to regarding deduction. It seems like we're on the same page. The main difference may be where one draws the lines. When I said "by themselves sequence to sequence models..." I was excluding algorithms that chain language models together in various ways.
Not too long ago, when people said "AI" that tended to refer to algorithms like forward chaining over a set of facts.