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.

These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why?

A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective In this appendix, we will derive Eq. 4. Analogously to Eq. 3, we optimize the following objective: max

Current perceptual similarity metrics operate at the level of pixels and patches.

What does it mean to have speech without a speaker? For the first time, speech has been decoupled from consequence. We now live alongside AI systems that converse knowledgeably and persuasively--deploying claims about the world, explanations, advice, encouragement, apologies, and promises--while bearing no vulnerability for what they say. Millions of people already rely on chatbots powered by large language models, and have integrated these synthetic interlocutors into their personal and professional lives. An LLM's words shape our beliefs, decisions, and actions, yet no speaker stands behind them. This dynamic is already familiar in everyday use. A chatbot gets something wrong. When corrected, it apologizes and changes its answer.

We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research.

The ability to collect a large dataset of human preferences from text-to-image users is usually limited to companies, making such datasets inaccessible to the public. To address this issue, we create a web app that enables text-to-image users to generate images and specify their preferences. Using this web app we build Pick-a-Pic, a large, open dataset of text-to-image prompts and real users'