Progress in artificial intelligence

How AI-related technologies evolve / From Wikipedia, the free encyclopedia

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Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a multidisciplinary branch of computer science that aims to create machines and systems capable of performing tasks that typically require human intelligence. Artificial intelligence applications have been used in a wide range of fields including medical diagnosis, economic-financial applications, robot control, law, scientific discovery, video games, and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."[1][2] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry."[3] In the late 1990s and early 21st century, AI technology became widely used as elements of larger systems,[3][4] but the field was rarely credited for these successes at the time.

Progress in machine classification of images
The error rate of AI by year. Red line - the error rate of a trained human on a particular task.

Kaplan and Haenlein structure artificial intelligence along three evolutionary stages: 1) artificial narrow intelligence – applying AI only to specific tasks; 2) artificial general intelligence – applying AI to several areas and able to autonomously solve problems they were never even designed for; and 3) artificial super intelligence – applying AI to any area capable of scientific creativity, social skills, and general wisdom.[2]

To allow comparison with human performance, artificial intelligence can be evaluated on constrained and well-defined problems. Such tests have been termed subject matter expert Turing tests. Also, smaller problems provide more achievable goals and there are an ever-increasing number of positive results.

Humans still substantially outperform both GPT-4 and models trained on the ConceptARC benchmark that scored 60% on most, and 77% on one category, while humans 91% on all and 97% on one category.[5]