Notes on OpenAI o3-mini

No one is forcing those doctors to participate in that system. Many (but not enough) of them don't, and operate cash clinics, which have a better reputation for quality.

But ultimately, healthcare is suffering not because of the insurance companies, but because of the American Medical Association, which worked to artificially limit the supply of doctors so that doctors could be paid more. This makes it more optimal for doctors to choose to participate in the insurance scheme as well.

So I ask again, why are we prioritizing the automation of software development, where neither the skill nor the profession itself is gatekept like healthcare is?

So far ive been impressed.. seems to be in the same ballpark as r1 and claude for coding. I will have to gather more samples.. in this past week ive changed from using 100% claude exclusively (since 3.5) to hitting all the big boys: claude, r1, 4o (o3 now), and gemini flash. Then ill do a new chat that includes all of their generated solutions for additional context for a refactored final solution.

R1 has upped the ante so Im hoping we continue to get more updates rapidly... they are getting quite good

This is a great anecdote and I hope others can learn from it. R1, o1, and o3-mini work best on problems that have a “correct” answer (as in code that passes unit tests, or math problems). If multiple professional translators are given the same document to translate, is there a single correct translation?

its the same thing as:

gpt-3.5 -> gpt-4 (gpt-4-32k premium)

"omni" announced (multimodal fusion, initial promise of gpt-4o, but cost effectively distilled down with additional multimodal aspects)

gpt-4o-mini -> gpt-4o (multimodal, realtime)

gpt-4o + "reasoning" exposed via tools in ChatGPT (you can see it in export formats) -> "o" series

o1 -> o1 premium / o1-mini (equivalent of gpt-4 "god model" becoming basis for lots of other stuff)

o1-pro-mode, o1-premium, o1-mini, somewhere in that is the "o1-2024-12-17" model with not streaming, function calling, and structured outputs and vision

now, distilled o1-pro-mode probably is o3-mini and o3-mini-high-mode (the naming is becoming just as bad as android)

its the repeat, take model, scale it up, run evals, detect innefficiencies, retrain, scale, distill, see what's not working. when you find a good little zone in the efficiency frontier, release it with a cool name

No. People’s tastes and judgments vary too much.

One fundamental area of disagreement is how closely a translation should reflect the content and structure of the original text versus how smooth and natural it should sound in the target language. With languages like Japanese or Chinese translated into English, for example, the vocabulary, grammar, and rhetoric can be very different between the languages. A close literal translation will usually seem awkward or even strange in English. To make the English seem natural, often you have to depart from what the original text says.

Most translators will agree that where to aim on that spectrum should be based on the type of text and the reason for translating it, but they will still disagree about specific word choices. And there are genres for which there is no consensus at all about which approach is best. I have heard heated exchanges between literary scholars about whether or not translations of novels should reflect the original as closely as possible out of respect for the author and the author’s cultural context, even if that means the translation seems awkward and difficult to understand to a casual reader.

The ideal, of course, would be translations that are both accurate and natural, but it can be very hard to strike that balance. One way LLMs have been helping me is to suggest multiple rewordings of sentences and paragraphs. Many of their suggestions are no good, but often enough they include wordings that I recognize are better in both fidelity and naturalness compared to what I can come up with on my own.