Artificial Intelligence
13.8 billion years after the Big Bang, the universe built something that wonders about the Big Bang. That's either the most profound thing that's ever happened, or a Tuesday. Probably both.
What AI actually is
Artificial Intelligence is not a thinking machine in the science fiction sense. It's a very sophisticated pattern-matcher. Modern AI systems — like the one powering AwareCub — are trained on enormous amounts of data and learn to predict what output best fits a given input. They don't understand. They recognise patterns at a scale no human could manage.
How it learns
Neural networks are loosely inspired by how neurons in the brain connect. Each connection has a weight — a number saying how important it is. During training, the AI makes predictions, checks how wrong it was, and adjusts every single weight slightly to be less wrong next time. Do this billions of times across billions of examples, and useful patterns emerge.
The connection to evolution
Evolution and AI training are surprisingly similar processes. Both involve variation (random mutations / random weight adjustments), selection (survival / reducing error), and iteration over enormous timescales. Evolution took 3.8 billion years to produce a brain. Gradient descent takes a few days on a GPU cluster to produce a language model. The principle — improving through repeated small adjustments — is the same.
AwareCub: AI on a credit card
AwareCub runs AI voice recognition and synthesis on a Raspberry Pi Zero 2W — a computer the size of a credit card costing less than £20. That this is possible represents 13.8 billion years of cosmic evolution: the Big Bang produced hydrogen, hydrogen built stars, stars forged silicon, silicon became chips, chips run AI, AI talks to children who need a patient, non-judgemental companion. The universe took the long route.
The mathematical foundation of backpropagation (how neural networks learn) was formalised in the 1980s. The current AI explosion was triggered by three convergences: scale (huge models), data (the internet), and hardware (GPUs originally designed for gaming). Large Language Models like Claude are 'transformers' — architectures that use attention mechanisms to weigh the relevance of different parts of input. The same attention mechanism, scaled, is changing science, medicine, and education.