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Undergraduate mathematicians usually have a hard time defining functions from quotients in Lean, because they have been taught a specific model for quotients in their classes, which is not the model that Lean uses. This post is an attempt to … Continue reading →
My feed was recently clogged up with news articles reporting that Sam Altman thinks that AGI is here, or will be here next year, or whatever. I will refrain from giving even more air to this nonsense by linking to … Continue reading →
So the big news this week is that o3, OpenAI's new language model, got 25% on FrontierMath. Let's start by explaining what this means. Continue reading →
So I'm two months into trying to teach a proof of Fermat's Last Theorem to a computer. We already have one interesting story, which I felt was worth sharing. Continue reading →
More in AI
It can be bleak out there, but the candor is very helpful, and you occasionally get a win.
Society is once again left holding the bag
How do projects fail at large tech companies? As I’ve said many times, failure means executives aren’t happy with how the project turned out. At healthy companies, that typically means that a sensible engineer wouldn’t be happy either, because the project didn’t work or users hated it. But what actually causes the projects to fail? I’ve seen a lot of projects go wrong - both up close and at a distance - in the last ten years. Here are the main reasons why. Doomed from the start Lots of projects fail because there’s no way they could possibly have succeeded. In American law, some cases get dismissed at “summary judgment”: even if the plaintiff succeeds in proving everything they aim to prove, it still wouldn’t add up to demonstrating enough illegal activity to win their case. At tech companies, some projects are like that: even if the plan goes off without a hitch, the project is still doomed to fail. Some doomed projects begin with over-ambitious plans. For instance, an executive…
Two simple questions to help make Society's Backend better
