141–150 of 207 posts
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100%. This person is trying to find patterns in random noise and believes they are meaningful. The original post hurts my head with its bad logic.
What if, instead of your ridiculous strawman, they believe in waiting to get consistency?
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He said he is "Asking GPT-4 to draw a unicorn every day to track changes in the model." The variance he is seeing in the output is primarily the product of random chance, rather than changes in the model. Specifically this "unicorn" that he found today is likely just random chance and there was no changes in the model between yesterday and today that lead to it arising. If he wanted to track changes in the model for…
There's a linked blog post[0] that goes more into the methodology and reasoning. "As mentioned in the hacker news discussion, the model doesn't change daily. [...] As OpenAI releases incremental updates, we'll see the model change automatically and be able to judge outputs. A single sample per day leads to quite different results, but that's fine I think. What I expect to see a year from now is an evolution of output…
This is why, as a product manager, you should always test 20 hypotheses per month. At p-value of 0.05 this basically guarantees a successful product feature test every month!
My understanding is that you wanted to test whether a certain feature improves user satisfaction or not. Assuming that users have a 0.05=1/20 probability of liking each feature, by testing 20 features you can get at least one successful feature (because 0.05x20=1).
This is wrong in two ways.
First, the p-value is the probability of observing an effect at least as large as what you measured, assuming that there is actually no effect, i.e., the null hypothesis is true. However, and this is crucial, the p-value does not tell you the probability that the null hypothesis is false (or true)! Again, p-values are unrelated to the truth-ness of the null hypothesis (this is a very common misunderstanding). In fact, if you test 20 hypotheses at a confidence of 5%, you have a probability of (at most) 64% of incorrectly thinking that a feature is useful, while it is not.
Second, setting aside hypothesis testing and p-values, if each feature has a 5% probability of being liked and you test 20 features, you only have 64% probability of finding an useful feature. This is because, assuming that the success probabilities of those features are independent and identically distributed, the number of successful features has a binomial distribution [1]. If you wanted to be 95% confident of finding at least one useful feature, you would need to test at least 59 different features each month. What you computed (0.05x20=1) is the expected (average) number of useful features per month over the course of many months.
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Thats even worse for autonomous cars, there is so such data and noise there is no way to reproduce the issue, it's complete chaos. Whereas with a LLM if we control the seed we can 100% reproduce the same result
>> if we control the seed we can 100% reproduce the same result No, that's the problem. You can't. You should be able to, but you can't. If you could, they wouldn't be scary. But we have Temperature Zero, different results. Because no one gave enough of a shit when coding them, and no one gives enough of a shit to try to fix the issue . This is what in any other industry would be called gross negligence.
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I don't think anyone claims this is an iterative linear measure, rather than a step function. SVG can present arbitrarily complex graphics. The underlying display tech supports what ever fidelity GPT will eventually mature into. Has GPT plateaud? Will it be stuck forever at this hilariously naive level of competence at SVG art? Will it mature into Midjourney level competence? I have no frigging clue. Since the token…
I mean, how many humans can draw art by writing out svg? If that's not in the training set, I don't even see how GPT-4 gets much better at this over time.
Let's think about it:
1. It has to output SVG [1] 2. It is given a text based representation of what it must draw[2] 3. It must then somehow convert words -- the concept of a unicorn: equine with a horn, white, maybe rainbows? -- into SVG code, and attempt to convey both their location, shape, colour, appearance, with code.
And keep in mind, this is just a token predictor. I doubt there is much data in its training that is this specific.
So while it's quite far from science, for me, it's a bit of fun and I get emails every now and then remarking on things like the turd of May (2023-05-18) and it lightens the mood every now and then, which I think ultimately, is worth it.
[1] System: You are a helpful assistant that generates SVG drawings. You respond only with SVG. You do not respond with text.
[2] User: Draw a unicorn in SVG format. Dimensions: 500x500. Respond ONLY with a single SVG string. Do not respond with conversation or codeblocks.
See: https://github.com/adamkdean/gpt-unicorn/blob/master/src/lib...
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I'm not aware of anything concrete by OpenAI, but others have offered possible explanations. One idea is that the cause is batched inference in sparse MoE (mixture of experts) models. https://152334h.github.io/blog/non-determinism-in-gpt-4/ HN discussion: https://news.ycombinator.com/item?id=37006224
So in some sense the spectre attack for AI?
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Agree with this except for one data point. OpenAI does enhance/tweak/do something with the models at different levels. This can be determined by: 1. A change in the current model number (eg. gpt-3.5-turbo-0613) 2. On ChatGPT UI, the date at the bottom (eg. August 2023) So it isn’t correct to say “it is incredibly obvious that nothing has happened”. Not that obvious to me. A bit like how you can never tell for sure if…
The ChatGPT UI is indeed a wildcard. But it is irrelevant here because according to the github repo this page queries the API and OpenAI guarantees it doesn't change models with version number information (like gpt-4-0613, which is mentioned in the latest images). So this "experiment" would make a lot more sense if it was only run once every few months when the API actually offers new model versions and then generate…
Also it's fun to see each daily unicorn.
It has become common knowledge that GPT4 (and also 3.5) have problems with deterministic outputs (even at T=0). So what we're seeing here is just the effect of random sampling, not any actual change to the model itself. If you scroll down, you'll see other close attempts by the exact same model that could already be counted as a win depending on who you ask. Edit: This comment section is a super fascinating case stud…
Agreed. But have you seen the original talk? I believe he's trying to find an unicorn similar in style to the one generated by the original researcher. It's so sad that openai has a far more capable model internally that it can't give open access to because of safety (or any other argument).
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100%. This person is trying to find patterns in random noise and believes they are meaningful. The original post hurts my head with its bad logic.
I'm sorry it hurts your head. I'm happy to sponsor a packet of paracetamol or some water if that helps. Ultimately, this is fun, not science. I'm just happy that after all these attempts, it finally got to a unicorn.
It has become common knowledge that GPT4 (and also 3.5) have problems with deterministic outputs (even at T=0). So what we're seeing here is just the effect of random sampling, not any actual change to the model itself. If you scroll down, you'll see other close attempts by the exact same model that could already be counted as a win depending on who you ask. Edit: This comment section is a super fascinating case stud…
Yes, nothing about GPT4 changed today . But that's not the goal of the project (although I can't speak for the intentions of the submitter here). Currently there are two different GPT4 models represented in the samples, with quite significant quality difference between them. The quality (and variance in quality within a single model!) is interesting to see in such a comparison.
(submitter here) You're correct, it's not the goal of the project. It would be fair to say there is no goal other than to ask GPT to draw a unicorn every day, and through it, create a talking point and potential fun for people who follow along.