uncertainty
Active learning: letting the model choose what to label next Lesson
Active learning is a training strategy in which the model picks which unlabeled examples should be labeled next, choosing the ones it is most uncertain about so that a fixed labeling budget buys the most improvement possible.
Out-of-distribution detection: teaching a model to say I have not seen this before Lesson
Out-of-distribution detection is the problem of getting a model to flag inputs unlike its training data instead of confidently guessing, and it has quietly moved from an image-classifier safety concern to core infrastructure for monitoring AI agents in production.
Bayesian optimization: how to search when every guess is expensive Lesson
Bayesian optimization is a strategy for finding the best setting of something when each trial is slow or costly: build a cheap statistical model of what you have learned so far, use it to predict both the value and the uncertainty of every untried option, then test whichever option best balances promise against ignorance.
A discovery system that refuses to trust the language model's own confidence News
Large Discovery Models pair a generative proposer with a Bayesian surrogate that scores candidates from real experimental evidence rather than model confidence, reporting a 2.4 times greater reduction in a training metric and an 18.2 percent relative improvement in antibody binding energy.
Bayesian updating: how a belief should change when evidence arrives Lesson
Bayesian updating is the rule for revising a belief when new evidence comes in: start with a prior, weigh how much more likely the evidence is under one hypothesis than another, and get a posterior.
Ensembles: why averaging several models beats picking the best one Lesson
An ensemble runs several models on the same input and combines their answers, which reliably beats any single member because independent mistakes cancel while correct signal adds -- and it is where forecast probabilities come from.
Calibration: whether a model's confidence means anything Lesson
A model is calibrated when the things it says with 70% confidence turn out to be true about 70% of the time - and calibration is a completely separate property from accuracy, which is why a model can be both frequently right and systematically untrustworthy about when it is wrong.