Efficiency Theory: a Unifying Theory for Information, Computation and Intelligence

The paper serves as the first contribution towards the development of the theory of efficiency: a unifying framework for the currently disjoint theories of information, complexity, communication and computation. Realizing the defining nature of the brute force approach in the fundamental concepts in all of the above mentioned fields, the paper suggests using efficiency or improvement over the brute force algorithm as a common unifying factor necessary for the creation of a unified theory of information manipulation. By defining such diverse terms as randomness, knowledge, intelligence and computability in terms of a common denominator we are able to bring together contributions from Shannon, Levin, Kolmogorov, Solomonoff, Chaitin, Yao and many others under a common umbrella of the efficiency theory. © Taru Publications

What are the ultimate limits to computational techniques: Verifier theory and unverifiability

Despite significant developments in proof theory, surprisingly little attention has been devoted to the concept of proof verifiers. In particular, the mathematical community may be interested in studying different types of proof verifiers (people, programs, oracles, communities, superintelligences) as mathematical objects. Such an effort could reveal their properties, their powers and limitations (particularly in human mathematicians), minimum and maximum complexity, as well as self-verification and self-reference issues. We propose an initial classification system for verifiers and provide some rudimentary analysis of solved and open problems in this important domain. Our main contribution is a formal introduction of the notion of unverifiability, for which the paper could serve as a general citation in domains of theorem proving, as well as software and AI verification

On the origin of synthetic life: Attribution of output to a particular algorithm

With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to gain an ability to distinguish between natural and artificial life forms. In this paper, we address this challenge as a generalized version of Darwin\u27s original problem, which he so brilliantly described in On the Origin of Species. After formalizing the problem of determining the samples\u27 origin, we demonstrate that the problem is in fact unsolvable. In the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal, both explanations are equality likely. Our results should attract attention of astrobiologists and scientists interested in developing a more complete theory of life, as well as of AI-Safety researchers

Personal Universes: A Solution to the Multi-Agent Value Alignment Problem

AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of each user. We conclude by analyzing benefits and limitations of the proposed approach

Feature Extraction Methods for Character Recognition

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