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Philosophical issues of artificial intelligence Alexey Melnikov Institute for Theoretical Physics, University of Innsbruck Philosophy of Scientific Computing, December 1, 2016, Innsbruck Outline ✤ Interaction between philosophy and artificial intelligence ✤ Two big philosophical questions of artificial intelligence ✤ Risks of developing artificial intelligence Alexey Melnikov. Philosophical issues of artificial intelligence 2 How can philosophy help AI? Philosophy plays a relevant role for AI in clarifying its goals and methods. Philosophers are around for much longer than computers, and they are trying to resolve many of the same questions that artificial intelligence (AI) science is addressing: ✤ How can minds work? ✤ How do human minds work? ✤ Can nonhumans have minds? “Much of AI already builds on works by philosophers” (Sloman, 1995) Philosophy has an influence on AI both from a historical and a methodological point of view. V. Schiaffonati. Minds and Machines 13: 537–552 (2003) Alexey Melnikov. Philosophical issues of artificial intelligence 3 How can AI help philosophy? AI offers powerful tools to philosophy in answering different questions. Some philosophers of science use the computational approach provided by AI, partly because it has the tools to give detailed, causal explanations of intelligent behavior. Philosophers are making philosophical claims based on computer simulations of machine learning agents. Alexey Melnikov. Philosophical issues of artificial intelligence 4 General intelligence Artificial intelligence algorithms are becoming better in tasks that were considered to be very difficult for a computer program. Handwriting recognition Classification of images F. Zamora-Martínez et. al., Pattern Recogn. 47, 1642 (2014) Y. Jia et al., in Proc. of the 22nd ACM MM, 675 (2014) Alexey Melnikov. Philosophical issues of artificial intelligence 5 General intelligence Artificial intelligence algorithms are becoming better in tasks that were considered to be very difficult for a computer program. Robot adapts to injury Self-driving cars A. Cully et. al., Nature 521, 503 (2015) google.com/selfdrivingcar Alexey Melnikov. Philosophical issues of artificial intelligence 6 General intelligence Artificial intelligence algorithms are becoming better in tasks that were considered to be very difficult for a computer program. Playing video games Mastering the game of Go V. Mnih et. al., Nature 518, 529 (2015) D. Silver et. al., Nature 529, 484 (2016) Alexey Melnikov. Philosophical issues of artificial intelligence 7 General intelligence Artificial intelligence algorithms are becoming better in tasks that were considered to be very difficult for a computer program. Current AI algorithms can only solve specific, externally-provided problems. Can a single AI agent be good in several different tasks? Can a machine solve any problem that a person would solve by thinking? Can a machine display general intelligence? Alexey Melnikov. Philosophical issues of artificial intelligence 8 The first big question: weak AI Can a machine be intelligent? The positive answer to this question would mean the existence of the so-called weak AI Similar questions: ✤ Can a machine act intelligently? ✤ Can a machine think? ✤ Can a machine solve any problem that a person would solve by thinking? S. Russell and P. Norvig. Artificial intelligence: A Modern Approach, 3rd edition (Prentice Hall, 2009) Alexey Melnikov. Philosophical issues of artificial intelligence 9 The first big question: weak AI Arguments that support the existence of the weak AI (existence of intelligent machines): ✤ Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it (Dartmouth proposal). ✤ The mind can be viewed as a device operating on bits of information according to formal rules (Dreyfus). ✤ AI in computer science is usually defined as the quest for the best agent program on a given architecture (Russell, Norvig). Weak Al is by definition possible: for any digital architecture with k bits of program storage there are exactly 2 k agent programs, and all we have to do to find the best one is enumerate and test them all. Alexey Melnikov. Philosophical issues of artificial intelligence 10 The first big question: weak AI Arguments against the existence of the weak AI: ✤ AI in computer science uses symbolic planning, automated theorem proving, computer vision, machine learning, data mining. Once we understand how to solve a problem using these techniques, it is no longer considered to require intelligence. AI never gets credit for its achievements (Piater). ✤ Scaling up machine learning does not lead to human-level AI (no general intelligence). ✤ Human intelligence relies not only on conscious symbolic manipulation, but also on unconscious instincts, which would never be captured by formal rules (Dreyfus). Alexey Melnikov. Philosophical issues of artificial intelligence 11 The first big question: weak AI Similar question: Can a machine think? “The question of whether Machines Can Think is about as relevant as the question of whether Submarines Can Swim” (Dijkstra, 1984). The practical possibility of “thinking machines” has been with us for only about 50 years, not long enough for speakers of English to settle on a meaning for the word “think” — does it require “a brain” or just “brain-like parts”. Alexey Melnikov. Philosophical issues of artificial intelligence 12 Turing test Can a machine be intelligent? Can a machine act intelligently? Can a machine think? Instead of asking these difficult questions we should probably ask whether machines can pass a behavioral intelligence test (Turing). If a machine acts as intelligently as a human, then it is as intelligent as a human https://commons.wikimedia.org/wiki/ File:Turing_Test_Version_3.svg Alexey Melnikov. Philosophical issues of artificial intelligence 13 Turing test. Some criticism If a machine acts as intelligently as a human, then it is as intelligent as a human The Turing test is explicitly anthropomorphic (human behavior is attributed to non-human entities). Aeronautical engineers do not define the goal of their field as “making machines that fly so exactly like pigeons that they can fool other pigeons” (Russell, Norvig). http://csunplugged.org/the-turing-test/ http://benniemols.blogspot.co.at/2010_10_01_archive.html https://commons.wikimedia.org/wiki/ File:Turing_Test_Version_3.svg Alexey Melnikov. Philosophical issues of artificial intelligence 14 The second big question: strong AI Can a machine act intelligently by actually thinking (not just by simulating thinking)? The positive answer to this question would mean the existence of the so-called strong AI Similar questions: ✤ Can a machine have a mind, mental states, and consciousness in the same way that a human being can? ✤ Can it feel how things are? ✤ Are human intelligence and machine intelligence the same? ✤ Is the human brain essentially a computer? S. Russell and P. Norvig. Artificial intelligence: A Modern Approach, 3rd edition (Prentice Hall, 2009) Alexey Melnikov. Philosophical issues of artificial intelligence 15 The second big question: strong AI Position of the AI researchers “Most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis — as long as their program works, they don't care whether you call it a simulation of intelligence or real intelligence” (Russell, Norvig). Alexey Melnikov. Philosophical issues of artificial intelligence 16 The second big question: strong AI Many philosophers have worries that a machine that passes the Turing test would still not be actually thinking, but would be only a simulation of thinking. On the other hand, why should we insist on a higher standard for machines than we do for humans? After all, in ordinary life we never have any direct evidence about the internal mental states of other humans. Alexey Melnikov. Philosophical issues of artificial intelligence 17 Chinese Room argument Imagine a hypothetical system that is clearly running a program and passes the Turing test, but that equally clearly does not understand anything of its inputs and outputs (Searle, 1980). The system consists of a human, who understands only English, equipped with a rule book, written in English. The instructions may include writing symbols on new slips of paper, finding symbols in the stacks, rearranging the stacks, and so on. Eventually, the instructions will cause one or more symbols to be transcribed onto a piece of paper that is passed back to the outside world. http://theness.com/neurologicablog/ index.php/ai-and-the-chinese-roomargument/ Alexey Melnikov. Philosophical issues of artificial intelligence 18 Risks of developing AI ✤ We develop an AI that can learn complex concepts ✤ We develop an AI that can learn more complex concepts than we can ✤ An AI develops an AI that can learn more complex concepts than its creator can ✤ The latter AI does the same thing, only better and faster ✤ We can no longer understand what the AI does ✤ We can no longer control the AI The Singularity An AI develops a better AI that faster develops an even better AI, and so on … Alexey Melnikov. Philosophical issues of artificial intelligence 19 Risks of developing AI ✤ People might lose their jobs to machines ✤ AI machines might be used toward undesirable ends ✤ The use of AI machines might result in a loss of accountability ✤ The success of AI might mean the end of humans OR ✤ Robots will take over all our labor, we will have more leisure time ✤ Diseases will be eliminated ✤ Death will no longer be inevitable ✤ There will be no wars, hunger, natural and human-made environmental disasters Thank you for your attention! Alexey Melnikov. Philosophical issues of artificial intelligence 20