Universal Intelligence: A Definition of Machine Intelligence

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.Comment: 50 gentle page

Universal Intelligence: A Definition of Machine Intelligence

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machine

Applying management science methods to health care diagnostics

The purpose is to encourage the growth of data analytics, total quality management and computer methods including artificial intelligence in the growth of procedures to diagnose and treat those inflicted with disease or indications of the spread of infectious diseases. With the rapid advances in machine intelligence, we have seen the development of the application of machine learning in business forecasting, analyzing treatment data and the results of analytic and diagnostic tests. This is important especially because of the world wide pandemic and the severe Covid-19 pandemic in the United States

How universal can an intelligence test be?

[EN] The notion of a universal intelligence test has been recently advocated as a means to assess humans, non-human animals and machines in an integrated, uniform way. While the main motivation has been the development of machine intelligence tests, the mere concept of a universal test has many implications in the way human intelligence tests are understood, and their relation to other tests in comparative psychology and animal cognition. From this diversity of subjects in the natural and artificial kingdoms, the very possibility of constructing a universal test is still controversial. In this paper we rephrase the question of whether universal intelligence tests are possible or not into the question of how universal intelligence tests can be, in terms of subjects, interfaces and resolutions. We discuss the feasibility and difficulty of universal tests depending on several levels according to what is taken for granted: the communication milieu, the resolution, the reward system or the agent itself. We argue that such tests must be highly adaptive, i.e., that tasks, resolution, rewards and communication have to be adapted according to how the evaluated agent is reacting and performing. Even so, the most general expression of a universal test may not be feasible (and, at best, might only be theoretically semi-computable). Nonetheless, in general, we can analyse the universality in terms of some traits that lead to several levels of universality and set the quest for universal tests as a progressive rather than absolute goal.This work was supported by the MEC/MINECO (projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02), the GVA (project PROMETEO/2008/051) and the COST-European Cooperation in the field of Scientific and Technical Research (project IC0801 AT).Dowe, DL.; Hernández Orallo, J. (2014). How universal can an intelligence test be?. Adaptive Behavior. 22(1):51-69. https://doi.org/10.1177/1059712313500502S516922

Instrumental Properties of Social Testbeds

The evaluation of an ability or skill happens in some kind of testbed, and so does with social intelligence. Of course, not all testbeds are suitable for this matter. But, how can we be sure of their appropriateness? In this paper we identify the components that should be considered in order to measure social intelligence, and provide some instrumental properties in order to assess the suitability of a testbed.Insa Cabrera, J.; Hernández Orallo, J. (2015). Instrumental Properties of Social Testbeds. Lecture Notes in Artificial Intelligence. 9205:101-110. doi:10.1007/978-3-319-21365-1_11S1011109205Horling, B., Lesser, V.: A Survey of Multi-Agent Organizational Paradigms. The Knowledge Engineering Review 19, 281–316 (2004)Simao, J., Demazeau, Y.: On Social Reasoning in Multi-Agent Systems. Inteligencia Artificial 5(13), 68–84 (2001)Roth, A.E.: The Shapley Value: Essays in Honor of Lloyd S. Shapley. Cambridge University Press (1988)Insa-Cabrera, J., Hernández-Orallo, J.: Definition and properties to assess multi-agent environments as social intelligence tests. Technical report, CoRR (2014)Legg, S., Hutter, M.: Universal Intelligence: A Definition of Machine Intelligence. Minds and Machines 17(4), 391–444 (2007)Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Hernández-Orallo, J.: A (hopefully) unbiased universal environment class for measuring intelligence of biological and artificial systems. In: 3rd Conference on Artificial General Intelligence, pp. 182–183 (2010)Hernández-Orallo, J., Dowe, D.L., Hernández-Lloreda, M.V.: Universal psychometrics: Measuring cognitive abilities in the machine kingdom. Cognitive Systems Research 27, 50–74 (2014