Artificial intelligence influenced by philosophy
History
Not counting the ancient precursors of mechanical reasoning in mythology, philosophy, and mathematics, advances in the modern mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate both mathematical deduction and formal reasoning, which is known as the Church–Turing thesis. This, along with at the time new discoveries in cybernetics and information theory, led researchers to consider the possibility of using electronic components to simulate human  problem-solving.Written in 1943 by McCullouch and Pitts, the formal design for Turing-complete "artificial neurons" is generally recognized as the first paper in AI.More than a decade later, in 1956, the field of AI research was officially born at a workshop at Dartmouth College. The attendees and their students produced programs that the press described as "astonishing": computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".They have, however, underestimated the vastness and probabilistic nature of most human knowledge, implicit in the "remaining" tasks.Both the U.S. and British governments cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects. Additionally, Minsky's and Papert's book Perceptrons was understood as proving that artificial neural networks approach would never be useful for solving real-world tasks, thus discrediting the approach altogether. The "AI winter", a period when obtaining funding for AI projects was difficult, followed.In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting winter began.Many researchers began to doubt that the current practices would be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific machine learning problems. Robotics researchers, such as Rodney Brooks, rejected the discredited rule-based approaches and trained robots that learned to move in their environment based on sensory information, using reasonable guesses (known as "soft computing") to deal with uncertain sensory information.Outside of academic discipline AI, which abandoned artificial neural networks altogether from its toolbox until the 1990s, people from other disciplines, such as psychologist Geoffrey Hinton and David Rumelhart, kept it alive under the name "connectionism".In the 1990s, Yann LeCun successfully showed that  convolutional neural networks can recognize handwritten digits, which made the neural networks approach a "safe choice" again.In the late 1990s and early 2000s, advanced mathematical methods and specific solutions to specific problems has emerged as a new academic discipline named machine learning. Machine learning researchers produced verifiable results and collaborated with other fields, such as statistics, economics and mathematics, culminating in 
deep learning. Deep learning's success was based on both hardware improvements (faster computers, graphics processing units, cloud computing)  
and access to large amounts of data (including curated datasets, such as ImageNet). 
Deep learning soon proved to be highly successful at a wide range of tasks and was adopted throughout the field. For many specific tasks, other methods were abandoned.The machine learning achievements made it safe for media and businesses to refer to them as "AI" again. 
The number of software projects that use machine learning at Google increased from a "sporadic usage" in 2012 to more than 2,700 projects in 2015.In a 2017 survey, one in five companies reported they had incorporated "AI" in some offerings or processes". 
The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019. According to 'AI Impacts', about $50 billion annually was invested in "AI" around 2022 in the US alone and about 20% of new US Computer Science PhD graduates have specialized in "AI";
about 800,000 "AI"-related US job openings existed in 2022.In 2016, issues of fairness and the misuse of technology were catapulted into center stage at machine learning conferences, publications vastly increased, funding became available, and many researchers re-focussed their careers on these issues. The alignment problem became a serious field of academic study.Computationalism and functionalism
Computationalism is the position in the philosophy of mind that the human mind is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind–body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.Philosopher John Searle characterized this position as "strong AI": "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."
Searle counters this assertion with his Chinese room argument, which attempts to show that, even if a machine perfectly simulates human behavior, there is still no reason to suppose it also has a mind.