Progress in artificial intelligence

Artificial intelligence applications have been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery 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.' 'Many thousands of AI applications are deeply embedded in the infrastructure of every industry.' In the late 1990s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. Artificial intelligence applications have been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery 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.' 'Many thousands of AI applications are deeply embedded in the infrastructure of every industry.' In the late 1990s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. 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. 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. In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. Yet, there are many other useful abilities that can be described as showing some form of intelligence. This gives better insight into the comparative success of artificial intelligence in different areas. In what has been called the Feigenbaum test, the inventor of expert systems argued for subject specific expert tests. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior. AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at. Some versions of Moravec's paradox observe that humans are more likely to outperform machines in areas such as physical dexterity that have been the direct target of natural selection. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a 'highly imperfect rule of thumb', that 'almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.' Games provide a high-profile benchmark for assessing rates of progress; many games have a large professional player base and a well-established competitive rating system. AlphaGo brought the era of classical board-game benchmarks to an close. Games of imperfect knowledge provide new challenges to AI in the area of game theory; the most prominent milestone in this area was brought to a close by Libratus' poker victory in 2017. E-sports continue to provide additional benchmarks; Facebook AI, Deepmind, and others have engaged with the popular StarCraft franchise of videogames.