When a SpaceX rocket failure set the skies aflame over western Europe last February, no-one was sure if the debris was also polluting our atmosphere. Now scientists are directly linking the uncontrolled rocket re-entry to a plume of lithium measured less than 100km above Earth. It is the first time researchers have drawn a direct link between a known piece of space debris crashing to Earth and pollution levels. They warn that as SpaceX chief Elon Musk pledges to launch one million satellites in the coming years, this contamination could be the tip of the iceberg. The scientists were already investigating the problem of pollution from space debris when they realised a SpaceX Falcon 9 had failed in flight.

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.

Decision trees are a fundamental workhorse in machine learning. Their logical and hierarchical structure makes them easy to understand and their predictions easy to explain.

We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.

Bayesian brain theory suggests that the brain employs generative models to understand the external world.

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

Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.