981–990 of 1001 posts
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Most index funds are synthetic. They would not be possible if it was not possible to beat the index quite reliably.
Care to explain? Genuinely interested.
I'm just a layperson, so I might be wrong in some way that I don't understand
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Or equivalently, it vastly underestimates the intelligence of parrots
Anyone who has studied Monte Carlo methods and stochastic differential equations and their applications and stochastic algorithms never found “stochastic parrot” a pejorative. In a very real way determinism is a requirement for a small mind that can’t get comfortable or understand advanced probability theory and its application.
i’m not sure if you intended to call those upthread small-minded
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it was never fair to call them stochastic parrots and anybody who is paying any attention knows that sequence models can generalize at least partially OOD
Weird the section of people wanting fairness to LLMs. If it makes you feel better, I'd say the Eliza Effect is good evidence human have a lot of "stochastic parrot" in them also. And there's no reason that being stochastic parrot means something can't generalize. The thing with these terms is LLMs are distinctly new things. Even blind men looking at elephants can improve their performance with good terminology and by…
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> Do you know of any university that host Deepseek? https://chat.zju.edu.cn https://chat.sjtu.edu.cn https://chat.ecnu.edu.cn/html/ To list a few. There are of course many more in China. I won't be surprised if universities in other countries also self-hosting.
Where does it says that it is self hosted? And why it is exposed to public.
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> And LLM's already have tons of productive uses. I disagree strongly with that. Right now they are fun toys to play with, but not useful tools, because they are not reliable. If and when that gets fixed, maybe they will have productive uses. But for right now, not so much.
Hello? Do you have a pulse? LLMs accomplish like 90% of everything I do now so I don’t have to do it… Explain what this code syntax means… Explain what this function does… Write a function to do X… Respond to my teammates in a Jira ticket explaining why it’s a bad idea to create a repo for every dockerfile… My teammate responded with X write a rebuttal… … and the list goes on … like forever
GPT 4.5 pricing is insane: Price Input: $75.00 / 1M tokens Cached input: $37.50 / 1M tokens Output: $150.00 / 1M tokens GPT 4o pricing for comparison: Price Input: $2.50 / 1M tokens Cached input: $1.25 / 1M tokens Output: $10.00 / 1M tokens It sounds like it's so expensive and the difference in usefulness is so lacking(?) they're not even gonna keep serving it in the API for long: > GPT‑4.5 is a very large and comput…
The price is obviously 15-30x that of 4o, but I'd just posit that there are some use cases where it may make sense. It probably doesn't make sense for the "open-ended consumer facing chatbot" use case, but for other use cases that are fewer and higher value in nature, it could if it's abilities are considerably better than 4o. For example, there are now a bunch of vendors that sell "respond to RFP" AI products. The n…
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Those numbers are not off. They are tested on tracks. You need to remove your shoe and drive with like two toes to get the speed just right, though. Test drivers I have done this with takes off their shoes or use ballerina shoes.
Cruise control?
And keeping steady state speed is not that hard.
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Considering that’s the exact opposite of their strategy to date, and they haven’t done anything to indicate that was the case, and they talked about how huge and expensive the model was to run, that is the less reasonable assumption by a mile.
It is true that this does not seem to be their strategy, but the previous strategy to date was actually showing measurable improvements and specific applications, not "vibes". What I said is far-fetched, but still I fail to understand the whole point here, because they do not really explain it. But maybe we just hit the point that the improvement of performance hit the slowing down part of a logistic curve, while the…
GPT 4.5 pricing is insane: Price Input: $75.00 / 1M tokens Cached input: $37.50 / 1M tokens Output: $150.00 / 1M tokens GPT 4o pricing for comparison: Price Input: $2.50 / 1M tokens Cached input: $1.25 / 1M tokens Output: $10.00 / 1M tokens It sounds like it's so expensive and the difference in usefulness is so lacking(?) they're not even gonna keep serving it in the API for long: > GPT‑4.5 is a very large and comput…
The price really is eye watering. At a glance, my first impression is this is something like Llama 3.1 405B, where the primary value may be realized in generating high quality synthetic data for training rather than direct use. I keep a little google spreadsheet with some charts to help visualize the landscape at a glance in terms of capability/price/throughput, bringing in the various index scores as they become ava…
GPT 4.5 pricing is insane: Price Input: $75.00 / 1M tokens Cached input: $37.50 / 1M tokens Output: $150.00 / 1M tokens GPT 4o pricing for comparison: Price Input: $2.50 / 1M tokens Cached input: $1.25 / 1M tokens Output: $10.00 / 1M tokens It sounds like it's so expensive and the difference in usefulness is so lacking(?) they're not even gonna keep serving it in the API for long: > GPT‑4.5 is a very large and comput…
Really depends on your use case. For low value tasks this is way too expensive. But for context, let’s say a court opinion is an average of 6000 words. Let’s say i want to analyze 10 court opinions and pull some information out that’s relevant to my case. That will run about $1.80 per document or $18 total. I wouldn’t pay that just to edify myself, but i can think of many use cases where it’s still a negligible cost,…