Three books unpack our infatuation with prediction, and what we lose when we outsource this task to machines. To be human is, fundamentally, to be a forecaster. Trying to see the future, whether through the lens of past experience or the logic of cause and effect, has helped us hunt, avoid hunted, plant crops, forge social bonds, and in general survive in a world that does not prioritize our survival. Indeed, as the tools of divination have changed over the centuries, from tea leaves to data sets, our conviction that the future can be known (and therefore controlled) has only grown stronger. Today, we are awash in a sea of predictions so vast and unrelenting that most of us barely even register them. As I write this sentence, algorithms on some remote server are busy trying to guess my next word based on those I have already typed.

It is crucial to quantify the uncertainty of these dynamics models when using them for planning.

Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process.

In recent years, machine learning has shown great promise in a variety of real-world applications.

In this paper, we study robust algorithms for the experts problem under memory constraints.

High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained

Recognizing human actions in videos requires spatial and temporal understanding. Most existing action recognition models lack a balanced spatio-temporal understanding of videos.