41–50 of 148 posts
Earlier quoted context omitted.
He, imo correctly, puts them both in the category of extra degrees of freedom that can allow the user to overfit and get results that appear more impressive than the underlying reality about how the model has generalized.
All systems are like that.
Earlier quoted context omitted.
>contain "knowledge" or "intelligence" or "meaning" when in reality, it's just a very fancy Markov chain These are not mutually exclusive. If you have a Markov chain that 100% of the time outputs "A cat is an animal", then it has knowledge that a cat is an animal.
I think you are operating with a different definition of "knowledge" that the parent does.
Since there are certainly people unfamiliar with the symbols, here is the Wikipedia article on them: https://en.wikipedia.org/wiki/Quantifier_(logic)
I just want to know what they're called if someone were to say them out loud. Upside down A and backwards E probably aren't accurate.
Example: ∀ x∈ℝ\{0} ∃y∈ℝ : x*y=2
"For all values x from the real numbers excluding zero, there is a value y from the real numbers so that x*y equals 2."
I can walk and chew bubble gum at the same time: on one hand, yes, there's certainly a lot of Kool-Aid being drank by the AI folks. Even on HN, I constantly argue with people that genuinely think LLMs are some kind of magical black box that contain "knowledge" or "intelligence" or "meaning" when in reality, it's just a very fancy Markov chain. And on the other hand, I think that language interfaces are probably the n…
The Microsoft Research "Sparks of AGI" paper spends 154 pages describing behaviors of GPT-4 that are inconsistent with the understanding of it being a "fancy Markov chain": https://arxiv.org/abs/2303.12712 I expect that the reason people are constantly arguing with you is that your analysis does not explain some easily testable experiences, such as why GPT-4 has the ability to explain what some non-trivial and unique…
Trivially explained as "even a broken clock is right twice a day." I skimmed the paper, as it was linked here on HN iirc. First, it was published by Microsoft, a company that absolutely has a horse in this race (what were they supposed to say? "The AI bot our search engine uses is dumb?"). Second of all, I was very interested in their methodology, so I fully read the first section, which is woefully hand-wavy, a fact with which even the authors would agree:
> We acknowledge that this approach is somewhat subjective and informal, and that it may not satisfy the rigorous standards of scientific evaluation.
The paper, for instance, is amazed that GPT knows how to draw a unicorn in TikZ, but we already know it was trained on the Pile, which includes all Stack Exchange websites, which happens to include answers like this one[1]. So to make the argument that it's being creative, when the answer (or, more charitably, something extremely close to it) is literally in the training set, is just disingenuous.
[1] https://tex.stackexchange.com/questions/312199/i-need-a-tex-...
If I understand correctly, the meat of the argument is "that is a system for every (∀) task, there exists (∃) a setting that gives the correct answer for that one task." My understanding of this (correct me if I'm wrong) is that the scam is convincing users that GPT-X can do anything with say, the correct prompts. This argument misses the mark for me. It's not that it solves all the problems, it's that the problems i…
> Significantly economically impactful in some cases- obvious examples of call centers and first-line customer support. Is it that obvious? Yesterday I had a trivial but uncommon issue with my pharmacy. I reached out to them online - their chatbot was the only channel available. I tried, over the course of 20 minutes and 3 restarted sessions, to communicate an issue that a human would have been able to respond to in…
It is nearly impossible to measure the customers lost.
And you may never return, which they’ll never know.
Earlier quoted context omitted.
All systems are like that.
That is quite an insight. A "programmer" is really an "overfitter".
And in any event, there are lots of systems with fewer degrees of freedom (or in the case of deep learning, more generalization potential) than the training data, that are not at particular risk of being overfit, and there are measures and tests to mitigate the risk of overfitting. It's not some inherent characteristic of "systems".
>>> Convince the user that it is their job to find a instantiation or setting of this control to make the system work for their tasks.
By golly, you just described playing the cello.
it is interesting to postulate if it is the “most likely word” heuristic that leads to this behavior (e.g. never says anything that startles people) or HFRL training systematically teaching it to say what people want to hear.