Evaluating progress Artificial intelligence

in 1950, alan turing proposed general procedure test intelligence of agent known turing test. procedure allows major problems of artificial intelligence tested. however, difficult challenge , @ present agents fail.

artificial intelligence can evaluated on specific problems such small problems in chemistry, hand-writing recognition , game-playing. such tests have been termed subject matter expert turing tests. smaller problems provide more achievable goals , there ever-increasing number of positive results.

for example, performance @ draughts (i.e. checkers) optimal, performance @ chess high-human , nearing super-human (see computer chess: computers versus human) , performance @ many everyday tasks (such recognizing face or crossing room without bumping something) sub-human.

a quite different approach measures machine intelligence through tests developed mathematical definitions of intelligence. examples of these kinds of tests start in late nineties devising intelligence tests using notions kolmogorov complexity , data compression. 2 major advantages of mathematical definitions applicability nonhuman intelligences , absence of requirement human testers.

a derivative of turing test automated public turing test tell computers , humans apart (captcha). name implies, helps determine user actual person , not computer posing human. in contrast standard turing test, captcha administered machine , targeted human opposed being administered human , targeted machine. computer asks user complete simple test generates grade test. computers unable solve problem, correct solutions deemed result of person taking test. common type of captcha test requires typing of distorted letters, numbers or symbols appear in image undecipherable computer.