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Joined 3 years ago
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Cake day: August 27th, 2023

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  • Imagine a book with very large pages filled with words and half words. When you ask it a question, it flips through these pages one by one and highlights words. The pages are interconnected in a way, so the word highlighted on one page help guide which will be highlighted on the next. It does this for every word, symbol or half word of it’s reply.

    By threatening it repeatedly while mildly changing the subject manner, they isolated the word groupings that represent pain and fear.

    They then take those words, highlight them and put them at the top of the first page which guides the rest of the highlighting and makes it act silly.





  • There are constantly new techniques being developed to speed up inference or reduce model size post training. There’s about a hundred different levers to pull that play on inference cost, and some of them don’t have much of an impact on quality.

    In the end, it’s probably simply because of competition. I don’t understand why everybody assumes they are running these at cost API wise.

    Edit: here’s a chart from ars technica. The cost of revenue is clearly lower than the actual revenue. They aren’t running inference at a lost or it would be higher. They aren’t profitable because they are spending all that money on capturing the market as quick as they can. For fucks sake, that includes all the free accounts as well running inference. How can anyone think a paying customer is getting it at less than cost?

    And yes, there have been advancement made. The cost of inference isn’t some static number that can never go down. Stop believing them when they tell you there’s no profit to be made. It’s the same playbook as a dozen other companies, they literally get rewarded for it when it comes tax time.