261–270 of 387 posts
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Reddit is a magnificent source of useful knowledge. r/AskHistorians r/bikewrench To name just two. There is nothing even remotely comparable. But you need to be able to detect sarcasm and irony.
I have seen a tremendous amount of bad advice on bikewrench.
I became a half decent home bike mechanic through reading it, and of course Park Tool videos.
This approach to remove bad search suggestions manually reminded of a different approach Google once took, where they weren’t satisfied with manually tweaking search results but rather wanted to tweak the algorithm that produces these results when there were bad results. 'Around 2002, a team was testing a subset of search limited to products, called Froogle. But one problem was so glaring that the team wasn't comfort…
Imagine typing a search and getting a response: "Give us 30 mins to respond - here's a token, come back at 17:35 with your token" ... and then you get an answer from an expert, which also gets indexed.
The clever bit decides when to defer to an expert instead of returning answers from the index.
I'll leave the finer details out.
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If you train it then it's no longer the same model. If I have f(x) = x + 1 and change it to f(x) = x + 1 + 1/1e9, it would not mean that `f` is not deterministic. The issue would be in whatever interface I was exposing the f's at.
But current models must be retrained to incorporate new information. Or to attempt to fix undesirable behavior. So just freezing it forever does not seem feasible. And because there is no way to predict what has changed - one has to verify everything all over again.
I don't see how that's different than the LLM case, a lot of algorithms change as a function of the data they're processing.
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Encyclopedia Britannica is also wrong in a reproducible and fixable way. And the input queries a finite set. It's output does not change due to random or arbitrary things. It is actually possible to verify. LLMs so far seem to be entirely unverifiable.
They don’t just seem it. They are by design. We talk about models “hallucinating” but that’s us bringing an external value judgement after the fact. The actual process of token generation works precisely the same. It’d be more accurate to say that models always hallucinate.
All the stress people are feeling with GenAI comes from the over anthropomorphisation of ... stats. Impressive syntatic ability is not equivalent to semantic capability.
This approach to remove bad search suggestions manually reminded of a different approach Google once took, where they weren’t satisfied with manually tweaking search results but rather wanted to tweak the algorithm that produces these results when there were bad results. 'Around 2002, a team was testing a subset of search limited to products, called Froogle. But one problem was so glaring that the team wasn't comfort…
Spend say £500M (USD/GBP/EUR) on experts, per annum. Imagine typing a search and getting a response: "Give us 30 mins to respond - here's a token, come back at 17:35 with your token" ... and then you get an answer from an expert, which also gets indexed. The clever bit decides when to defer to an expert instead of returning answers from the index. I'll leave the finer details out.
This approach to remove bad search suggestions manually reminded of a different approach Google once took, where they weren’t satisfied with manually tweaking search results but rather wanted to tweak the algorithm that produces these results when there were bad results. 'Around 2002, a team was testing a subset of search limited to products, called Froogle. But one problem was so glaring that the team wasn't comfort…
Sounds rather like how Google photos does not identify anything as a Gorilla.
"Achieving the initial 80 percent is relatively straightforward since it involves approximating a large amount of human data, Marcus said, but the final 20 percent is extremely challenging. In fact, Marcus thinks that last 20 percent might be the hardest thing of all." 100% completely accurate is super-AI-complete. No human can meet that goal either. No, not even you, dear person reading this. You are wrong about som…
But even when I’m wrong I’m not 100% off. Not “to help with depression jump of a bridge” or “use glue to keep the cheese on the pizza” kind of wrong.
This approach to remove bad search suggestions manually reminded of a different approach Google once took, where they weren’t satisfied with manually tweaking search results but rather wanted to tweak the algorithm that produces these results when there were bad results. 'Around 2002, a team was testing a subset of search limited to products, called Froogle. But one problem was so glaring that the team wasn't comfort…
Spend say £500M (USD/GBP/EUR) on experts, per annum. Imagine typing a search and getting a response: "Give us 30 mins to respond - here's a token, come back at 17:35 with your token" ... and then you get an answer from an expert, which also gets indexed. The clever bit decides when to defer to an expert instead of returning answers from the index. I'll leave the finer details out.
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Do the activations tell you anything more than what the LLM delivers in plain text? Other than for trivial bugs in the LLM code, I don't think so.
Yes, "making up an answer" will look different from "quoting pretrained knowledge" because eg the model might've decided you were asking a creative writing question.
My understanding was the opposite -- that the activity of a confabulating LLM is indistinguishable from one giving factually accurate responses.
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Or to put it another way, I think Google should have a way of saying "yes, we know this result is wrong, but we're leaving it in because it's funny." There is a demand for funny results. Someone asking “how many rocks should I eat” is looking for entertainment, so you might as well give it to them.
The right answer is no rocks. Some mentally ill person could type that in and get "eat 1000 rocks" and then die from eating rocks, and that would be Google's fault. It's not funny. I have no doubt right now there are at least 50 youtube videos being made testing different glue's effectiveness holding cheese on a pizza. And some of those idiots are going to taste-test it, too. And then people will try it at home, some…
> The right answer is no rocks.
It's impossible to completely eliminate such trace contaminants from produce.
Pedantic? Yes, but you also can't expect a machine to confidently give you absolutes is response to questions that don't even warrant them, or to distinguish them from questions like "do mammals lay eggs?".