Analysis of first prototype universal intelligence tests: evaluating and
  comparing AI algorithms and humans
  Today, available methods that assess AI systems are focused on using
empirical techniques to measure the performance of algorithms in some specific
tasks (e.g., playing chess, solving mazes or land a helicopter). However, these
methods are not appropriate if we want to evaluate the general intelligence of
AI and, even less, if we compare it with human intelligence. The ANYNT project
has designed a new method of evaluation that tries to assess AI systems using
well known computational notions and problems which are as general as possible.
This new method serves to assess general intelligence (which allows us to learn
how to solve any new kind of problem we face) and not only to evaluate
performance on a set of specific tasks. This method not only focuses on
measuring the intelligence of algorithms, but also to assess any intelligent
system (human beings, animals, AI, aliens?,...), and letting us to place their
results on the same scale and, therefore, to be able to compare them. This new
approach will allow us (in the future) to evaluate and compare any kind of
intelligent system known or even to build/find, be it artificial or biological.
This master thesis aims at ensuring that this new method provides consistent
results when evaluating AI algorithms, this is done through the design and
implementation of prototypes of universal intelligence tests and their
application to different intelligent systems (AI algorithms and humans beings).
From the study we analyze whether the results obtained by two different
intelligent systems are properly located on the same scale and we propose
changes and refinements to these prototypes in order to, in the future, being
able to achieve a truly universal intelligence test.
