Collection
The field concerned with building systems that exhibit intelligent behaviour. Spanning machine learning, neural networks, and natural language processing, AI raises deep empirical and philosophical questions about what intelligence is and how it can arise from physical substrates.
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Key principles
Modern telescopes do not just magnify. They extract meaningful signal from noise. Mads Fredslund Andersen explains how spectrographs, adaptive optics, and indirect detection have transformed what we can know about the universe.
A model trained to detect skin cancer learned to look for rulers instead. The explainability problem is not a glitch. It is Goodhart's Law running at machine speed — and the gap between what AI systems can do and what we understand about how they do it is where the most consequential work in the field is now happening.
The most powerful advances in astronomy come from smarter signal extraction, not bigger hardware. A spectrograph on a modest telescope can reveal more about a distant star than a raw image from a larger one, because what you extract from the light matters more than how much of it you gather.
In 2012, a neural network cracked image recognition in a way that reshaped the entire AI field. Andreas Møgelmose traces what neural networks actually are, how convolutional architectures build understanding in layers, and why a cancer-detection model that learned to spot rulers instead tells us something important about where the field still cannot go.
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ComputationE19
TechnologyE13
ComputationE11
TechnologyE10
TechnologyE5
TechnologyThe application of biological knowledge to engineering.
The science and engineering of the universe beyond Earth.
The capacity to do work, and the central organising challenge of modern civilisation.
Systems defined by their connections rather than their components.
The science and practice of making things at scale.