Ultimate Intelligence Part I: Physical Completeness and Objectivity of Induction

  title={Ultimate Intelligence Part I: Physical Completeness and Objectivity of Induction},
  author={Eray {\"O}zkural},
We propose that Solomonoff induction is complete in the physical sense via several strong physical arguments. We also argue that Solomonoff induction is fully applicable to quantum mechanics. We show how to choose an objective reference machine for universal induction by defining a physical message complexity and physical message probability, and argue that this choice dissolves some well-known objections to universal induction. We also introduce many more variants of physical message… 

Ultimate Intelligence Part II: Physical Complexity and Limits of Inductive Inference Systems

A “black-hole equation” of inductive inference is arrived at, which relates energy, volume, space, and algorithmic information for an optimal inductives inference solution.

Epistemological and Ethical Implications of the Free Energy Principle

The cognitive goal that corresponds to the free energy principle is interpreted as seeking a dynamic, fruitful, yet peaceful activity that sustains the organism, which is interestingly similar to the Buddhist intuition of mental equanimity.

Ultimate Intelligence Part III: Measures of Intelligence, Perception and Intelligent Agents

It is proposed that operator induction serves as an adequate model of perception and how to reduce universal agent models to operator induction to show how it can be used in a reinforcement learning model and a homeostasis agent based on the free energy principle.



Logical depth and physical complexity

Some mathematical and natural objects (a random sequence, a sequence of zeros, a perfect crystal, a gas) are intuitively trivial, while others (e.g. the human body, the digits of π) contain internal

An Approximation of the Universal Intelligence Measure

This paper studies the practical issues involved in developing a real-world UIM-based performance metric and develops a prototype implementation which is used to evaluate a number of different artificial agents.

Intelligence as Inference or Forcing Occam on the World

It is proposed to perform the optimization task of Universal Artificial Intelligence through learning a reference machine on which good programs are short and this machine is learnt iteratively through a procedure that generalizes the principle underlying the Expectation-Maximization algorithm.

On Interpreting Chaitin's Incompleteness Theorem

It is shown that the limiting constants provided by the theorem do not in any way reflect the power of formalized theories, but that the values of these constants are actually determined by the chosen coding of Turing machines, and are thus quite accidental.

Quantum theory, the Church–Turing principle and the universal quantum computer

  • D. Deutsch
  • Physics, Philosophy
    Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences
  • 1985
It is argued that underlying the Church–Turing hypothesis there is an implicit physical assertion. Here, this assertion is presented explicitly as a physical principle: ‘every finitely realizible

Ultimate physical limits to computation

The physical limits of computation as determined by the speed of light c, the quantum scale ℏ and the gravitational constant G are explored.

Complexity-based induction systems: Comparisons and convergence theorems

Levin has shown that if tilde{P}'_{M}(x) is an unnormalized form of this measure, and P( x) is any computable probability measure on strings, x, then \tilde{M}'_M}\geqCP (x) where C is a constant independent of x .

The Discovery of Algorithmic Probability

This paper will describe a voyage of discovery — the discovery of Algorithmic Probability, the result of “goal motivated discovery” — like theiscovery of the double helix in biology, but with fewer people involved and relatively little political skullduggery.

Universal Algorithmic Intelligence: A Mathematical Top→Down Approach

This work constructs a modified algorithm AIXItl that is still effectively more intelligent than any other time t and length l bounded agent and gives strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible.