The approaches to Artificial Intelligence (AI) in the last century may be labelled as
- trying to understand and copy (human) nature,
- being based on heuristic considerations,
- being formal but from the outset (provably) limited,
- being (mere) frameworks that leave crucial aspects unspecified.
This decade has spawned the first theory of AI, which is principled, formal, complete, and general. This theory, called Universal AI, is about ultimate super-intelligence. It can serve as a gold standard for General AI, and implicitly proposes a formal definition of machine intelligence.
After a brief review of the various approaches to (general) AI, I will give an introduction to Universal AI, concentrating on the philosophical, mathematical, and computational aspects behind it. I will also discuss various implications and future challenges.
Marcus Hutter is Professor in the Research School of Computer Science at the Australian National University in Canberra, Australia. He received his PhD and BSc in physics from the LMU in Munich and a Habilitation, MSc, and BSc in informatics from the TU Munich.
Since 2000, his research at IDSIA and now ANU is centered around the information-theoretic foundations of inductive reasoning and reinforcement learning, which has resulted in 100+ publications and several awards.
His book "Universal Artificial Intelligence" (Springer, EATCS, 2005) develops the first sound and complete theory of AI. He also runs the Human Knowledge Compression Contest (€50,000 H-prize).