Where I write down what I am reading and thinking about. Mostly AI at work.
Something troubles the AI field: a model can predict very accurately and still not say why it concluded what it did.
You have probably felt this: a few minutes into TikTok or Netflix and it has already read you, serving exactly what you wanted, though you never said so.
One of the great turns in technology was the day computers began to see. Before that, teaching a machine to recognise a cat meant describing every feature by hand.
In teaching machines there is a small distinction I find illuminating about management: between what the machine learns itself, and the few things a person must set by hand beforehand.
Many people assume the machine reads through the data once and is done. The reality is the opposite: it studies the same material over and over.
Remember the game of telephone? A sentence whispered through seven people, and by the last one it has gone crooked. Deep networks hit exactly that problem.
Why do some models get past a hard patch easily while others get stuck halfway? Often the difference is something very familiar: momentum.
What decides whether a cell in a neural network should speak up or stay quiet? The answer says a good deal about how we decide things.
There is a trick that makes a model stronger and sounds absurd at first: they deliberately switch part of it off.
We have all met the student who knows every exercise in the book by heart and freezes at a question phrased slightly differently.
When teaching a model, one tiny dial decides almost the whole result: after each lesson, how much should it change itself?
It sounds backwards, and it is the heart of how AI learns: what makes it better is not the times it guessed right, but the times it guessed wrong.
Mỗi sáng tôi gửi một việc nhỏ bạn làm được ngay hôm đó với AI. Đọc hết trong hai phút.