This is pure gold data Companies focused and lost by the hype of AI without build their own SDLC powered by AI is the recipe to fail. Learn -> Measure -> Build 🔁 That's the cicle. No "please use AI for whatever you build". Then the billing hurts to pay and I'm not talking about tokens.
The future is multi-model. Trying to hide the choice confuses and hurts customers, who can't participate in the upside of the most exciting market competition of our times, nor master the best tool for the job.
But, simply just having your loops build everything without having some guardrails around the blast radius without having guardrails around how you think about quality, I think is a recipe for disaster.
we find that the performance on held out tasks decreases dramatically. Whereas if we um just take out a random 20% of the data that's less diverse than the most diverse subset, the performance um only decreases a little bit. And so this suggests that actually having really diverse data plays an important role in enabling it to generalize to new tasks.
Could this phrase or sentence or paragraph or section be improved, even a little? That is, can I make myself (on this very localized turf) make myself dislike it less?
In other words, I still use exactly the same recipe that I developed for the Pulsar when I brew with the mini, I just brew with a dose smaller by a factor 0.6 (e.g., 15 grams instead of 25).
Each and every time, it took at most a few years but typically months before other companies would achieve the same level of performance using a broadly similar, and always relatively simple, recipe.
o3's improvement over the GPT series proves that architecture is everything. You couldn't throw more compute at GPT-4 and get these results. Simply scaling up the things we were doing from 2019 to 2023 -- take the same architecture, train a bigger version on more data -- is not enough.
Google’s the first — and often the last — place we go to for answers, so snagging top billing in a Google search results page is, approximately speaking, equivalent to being “true.”
This, as was noted on Twitter, is a recipe for bad studies: it encourages the cherry-picking of results that went your way and the hiding of those that didn’t.
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