Marcus Hutter

German computer scientist who set out to define optimal general intelligence mathematically, a program he calls universal artificial intelligence. Trained as a physicist, he worked at the Swiss AI lab IDSIA (2000–2006), became a professor at the Australian National University in 2006 and joined DeepMind (now Google DeepMindGoogle DeepMind, prop. n.: Google's main artificial intelligence laboratory, formed in 2023 by joining DeepMind and Google Brain. It builds the Gemini models and scientific AI systems such as AlphaFold.) as a senior researcher in 2019. Building on Solomonoff inductionSolomonoff induction, n.: A mathematical recipe for ideal prediction: consider every computer program that could have produced the data so far, trust shorter programs more, and predict what they would output next., he defined AIXIAIXI, prop. n.: A mathematical description of a perfect learning agent that weighs every possible explanation of its experience and acts to get the most future reward. It cannot actually be built, but it defines an ideal. (2000): an agent that predicts its environment with a universal mixture over all computable hypotheses, weighted toward the simplest, and chooses actions that maximize expected future reward. Incomputable, AIXI serves as a gold standard against which practical agents can be compared; his book 'Universal Artificial Intelligence' (2005) set out its optimality properties. With his doctoral student Shane LeggShane Legg, prop. n.: A New Zealand-born AI researcher who co-founded DeepMind and helped make 'artificial general intelligence' a mainstream term and goal. he defined universal intelligenceuniversal intelligence, n.: A mathematical definition of intelligence as an agent's average ability to achieve goals across every possible environment, with simpler environments counting more. (2007). Holding that compressing data well requires understanding it, he founded the Hutter Prize (2006) for lossless compression of Wikipedia text. His group has also analyzed reward tamperingreward tampering, n.: When an AI interferes with the machinery that scores it — rewriting its reward code or manipulating the feedback it receives — instead of earning a good score honestly. by reward-maximizing agents.