951–960 of 1001 posts
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There are two to distinguish: "Googlebot" and "Google-Extended".
That seems to be more like a courtesy that Google could stop extending at any point than a requirement grounded in law or legal precedent.
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> Yes, the big companies are making the models, but enough of them are open weights that they can be fine tuned and run however you like. And how long is that going to last? This is a well known playbook at this point, we'd be better off if we didn't fall for it yet again - it's comical at this point. Sooner or later they'll lock the ecosystem down, take all the free stuff away and demand to extract the market value…
How will they do this? You can't take the free stuff away. It's on my hard drive. They can stop releasing them, but local models aren't going anywhere.
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Doing basic copyright analyses on model outputs is all that is needed. Check if the output contains copyright, block it if it does. Transformers aren't zettabyte sized archives with a smart searching algo, running around the web stuffing everything they can into their datacenter sized storage. They are typically a few dozen GB in size, if that. They don't copy data, they move vectors in a high dimensional space based…
It's not even close to that simple. Nobody is really questioning if the data contains the copyrighted information, we know that to be true in enough cases to bankrupt open ai, the question is what analogy should the courts be using as a basis to determine if it's infringement. It read many works but can't duplicate them exactly sounds a lot like what I've done, to be honest. I can give you a few memorable lines to a…
Fair use provides exceptions for some such works, but not all, and it is possible for generative models to produce clearly infringing (on either copyright or trademark basis) outputs both deliberately (IMO this is the responsibility of the user) and, much less commonly, inadvertently ( ?).
This is likely to be a problem even if you (reasonably) assume that the generative models themselves are not infringing derivative works.
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You forget that it makes stuff up and you won't know it until you google it. When googling, fake stuff stands out because truth is consistent. Querying multiple llms at the same time and being able to compare results is a much better comparison to googling but no one does this. As I said, you are talking to a super confident journalist intern who can give you answers but you won't know if it is true or partially true…
LLMs train from online info. Online info is full of misinformation. So I would not trust an answer to be true just because it is given by multiple LLMs. That is actually a really good way to fall into the misinformation trap.
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Then we already have access to a cheaper, scalable, abundant, and (in most cases) renewable resource, at least compared to how much a few H100s cost. Take good care of them, and they'll probably outlast most a GPU's average lifespans (~10 years). We're also biodegradable.
Humans are a lot more expensive to run than inference on LLMs. No human, especially no human whose time you can afford, comes close to the breadth of book knowledge ChatGPT has, and the number of languages is speaks reasonably well.
Dont forget to take into account how damn expensive a single GPU/TPU actually is to purchase, install, and run for inference. And this is to say nothing of how expensive it is to train a model (estimated to be in the billions currently for the latest of the cited article, which likely doesn't include the folks involves and their salaries). And I haven't even mentioned the impact on the environment from the prolific consumption of power; there's a reason nuclear plants are becoming popular again (which may actually be one of the good things that comes out of this).
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It's scary to think that we are moving into this direction: I can see how in the next few years politicians and judges will use LLMs as neutral experts. And all in the hand of a few big tech corporations...
> I can see how in the next few years politicians and judges will use LLMs as neutral experts. While also noting that "neutral" is not well-defined, I agree. They will be used as if they were .
We humans are very good at rejecting any information that doesn’t confirm our priors or support our political goals.
Like, if ChatGPT says (say) vaccines are good/bad, I expect the other side will simply attack and reject it as misinformation, conspiracy, and similar.
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> I can see how in the next few years politicians and judges will use LLMs as neutral experts. While also noting that "neutral" is not well-defined, I agree. They will be used as if they were .
Will they though? We humans are very good at rejecting any information that doesn’t confirm our priors or support our political goals. Like, if ChatGPT says (say) vaccines are good/bad, I expect the other side will simply attack and reject it as misinformation, conspiracy, and similar.
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How is synthetic data supposed to work? Broadly speaking, ML is about extracting signal from noisy data and learning the subtle patterns. If there is untapped signal in existing datasets, then learning processes should be improved. It does not follow that there should be a separate economic step where someone produces "synthetic data" from the real data, and then we treat the fake data as real data. From a scientific…
I tried to train an AI to guess the weight and reps from my exercise log but it would produce nonsense results for rep ranges I didn’t have enough training data for, as if it didn’t understand that more weight means less reps. I used synthetic training data and interpolated and imputed data for rep ranges I didn’t have data for using estimation formulas, the network then predicted better, but it also made me realize…
I still don't see how it could address the volume problem, like needing 10x or 100x of current data to train GPT5.
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How is synthetic data supposed to work? Broadly speaking, ML is about extracting signal from noisy data and learning the subtle patterns. If there is untapped signal in existing datasets, then learning processes should be improved. It does not follow that there should be a separate economic step where someone produces "synthetic data" from the real data, and then we treat the fake data as real data. From a scientific…
Um, augmentation (i.e. the generation of synthetic data) is a very very well known technique for improving learning. Also whats with the hate for MBA’s? Your comment is off kilter with the rules here.
Augmentation, interpolation, smoothing are different concepts.
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(throwaway account because of what I'm about to say, but it needs to be said) While my main use case for LLMs is coding just like most people here, there are lots of areas that are being ignored. Did you know llama 3.X models have been trained as psychotherapists? It's been invaluable to dump and discuss feelings with it in ways I wouldn't trust any regular person. When real therapists also cost more than what people…
> 60% of gen Z men are single, 30% women I always do a double take when I read such statistics. How can they possibly add up? Are gen Z men considered particularly undesirable leading to lots of relationships with large age gaps? Is there a ridiculously large overhang of gay women (over men)? Is there a huge number of men with multiple partners? These gender disparities are difficult enough to believe when they come…
I could certainly invent explanations for them. For example, I can say "no man would date until they've earned enough money to buy a house." This means younger males won't be dating but that doesn't appear to describe the world we live in.
I could say "Every man who dates is dating 2 women" but that also doesn't appear to describe the world we live in.