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Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantaras @ PAPIs Connect
The document outlines the history, achievements, and future challenges of artificial intelligence (AI), distinguishing between weak AI (specialized tasks) and strong AI (human-level intelligence). It discusses the significant advancements made in the field, particularly in specialized applications, but emphasizes the persistent gap toward achieving general intelligence due to issues like common sense reasoning and system integration. The author suggests that future progress will rely on interdisciplinary research and developing integrated systems, while cautioning against the emergence of uncontrolled autonomous AI.
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Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantaras @ PAPIs Connect
- 1. Past, present, andfuture of AI: A fascinating journey Ramon Lopez de Mantaras Artificial Intelligence Research Institute (IIIA) CSIC http://www.iiia.csic.es/~mantaras
- 2. Outline - Turing onAI From Turing to Dartmouth - Two views on AI: Weak AI vs. Strong AI -The road traveled Achievements of (Weak) AI -The (long) road ahead From Integrated Systems to Strong AI -Conclusions
- 3. Turing on AIIn1948 Turing predicted that by the end of the 20th century there would be intelligent computers capable of performing logical deductions, acquire new knowledge inductively, by experience and by evolution and capable of communicating by means of humanized interfaces. He also speculated about a connection between randomness and creative intelligence by suggesting to add radium in to the ACE in the hope that the random decay of radiation would give its inputs the desired unpredictability. In his famous 1950 paper he also speculated about the emulation of the mind of a child and giving it an appropriate education to obtain an adult mind (mental development)
- 4. From Turing toDartmouth 1948 Hixon Symposium on Cerebral Mechanisms in Behavior in Caltech (McCulloch on NNs, von Neumann, Lashley on limitations of behaviourism) Session on Learning Machines at the 1955 Western Computer Conference in L.A. (Clark & Farley on Hebbian learning in NNs; Selfridge on image classification; Newell on chess; Pitts on NNs) 1956 Summer Research Project on Artificial Intelligence in Dartmouth College (McCarthy, Minsky, Newell, Simon, Shaw, Selfridge, Solomonoff, Rochester, Shannon, Samuel, Bernstein)
- 5. Two views onAI -The view of the founding fathers: The science and engineering of replicating, even surpassing (singularity?), human-level intelligence in machines (“strong AI”) -The view in the early 80’s (after the “AI winter”): The science and engineering of designing machines with the capability to perform tasks that, when done by humans, we agree that they require intelligence (“weak AI”)
- 6. Strong versus WeakAI The Strong AI case Strong AI refers to AI that matches (or even exceeds) general human-level intelligence (intelligent machines will have mental states, consciousness, etc.) Example: The robots from the movies (HAL 9000, Matrix, Terminator, I Robot, etc.) The goal of human-level intelligence remains elusive but has inspired and still inspires our work on AI even though most efforts are on building weak AI (or “idiots savants”)
- 7. Strong versus WeakAI The Weak AI case (or the “idiots savants”) Machines already exhibit specialized intelligences without worrying about having mental states, consciousness, etc. All current forms of AI are “weak AI” We have achieved impressive results along the traveled “weak AI” road
- 8.
- 9. The road traveled AIis everywhere (though most of the time is not visible!): -Fuel injection systems in our cars designed using AI algorithms. - Jet turbines are designed using genetic algorithms. - 10.000 engineers carry out 2.600 maintenance works nightly on Hong Kong’s subway, scheduled by an AI system - There are a millions of AI-powered specialized robots in people’s homes and robots running on the surface of Mars. - Computer games (NPCs) use many AI techniques (including ML) - Web search engines use AI techniques - Automatic detection of credit card fraudulent transactions use ML algorithms - Routing of cell phone calls is based on AI - Detection of consumer habits is based on AI (ML) - The world’s best chess players are computer programs - Complex mathematical theorems have been proven by automatic theorem provers (i.e. Robbins conjecture) - An ML system revals passing patterns in soccer teams - There are robots that play soccer -There are AI systems composing beautiful music and systems performing music expressively (among other artistic applications)
- 10. RoboCup: Learning toplay cooperatively R. Ros, R. Lopez de Mantaras, J.L. Arcos, M. Veloso; A Case-Based approach for Action Selection and Coodination in Robot Soccer Gameplays, Artificial Intelligence Journal 173(9-10) (2009) 1014-1039. doi:10.1016/j.artint.2009.02.004
- 11. Inexpressive Input phrase SaxEx Affective value Happy Sad Allof me *R. Lopez de Mantaras, J.L. Arcos; AI and Music: From Composition to Expressive Performance, AI Magazine 23(3), 43-57, 2002 Playing Jazz ballads expressively
- 12. The road traveled Wehave achieved many of the things that the field’s founders used as motivators, but not always in the way the “founding fathers” imagined: -Very recently we have seen an impressive variety of application achievements. Most of them based on the availability of very large sets of data processed by very high performance computers, but NOT based on emulating human’s mental processes: -one of the world’s best Go players is a computer program -self-driving cars have successfully run milions of miles (gathers 1 Gb/sec of data to make predictions about its surroundings) -there are high-performance speech recognition systems (SIRI, CORTANA,..) -Watson outperformed the best “Jeopardy” players (and now… turns medic and financial advisor) -An ML system, trained on data from 133.000 patients from 4 Chicago’s hospitals, can predict heart attacks in IC patients 4 hours before they happen - …
- 13. The road traveled Inspite of all these great successes along specialized lines in each of the areas of AI, we do not seem to be getting any closer to “general AI” because of the following 3 problems: 1-We have given up the explainability of the AI systems (as well as the cognitive plausability of AI models) the “reasoning” made by today’s massive data-driven AI is a massively complex statistical analysis of an immense number of datapoints. We have traded the “why” for simply the “what” 2-We have focused too much on the isolated components of AI but not on the whole AI itself We have wonderful bricks but, to build the house, we need an architecture and the cement to tie the bricks together (sensing, knowledge acquisition & representation, reasoning, communication, action, planning, etc) 3- We have no idea of how to model and acquire common sense knowledge
- 14. The long roadahead: Future Challenges
- 15. The road ahead:Integrated systems Intelligence seems to emerge from a complex combination of many specialized abilities, such as sensing, reasoning, learning, planning, socializing, and communicating. But not a mere juxtaposition of these abilities! Rather, there is some set of deep interdependencies that tie these elements together. For example: -learning must result on knowledge that needs to be represented so that reasoners, planners, etc can use it efficiently. -perception requires reasoning and learning and viceversa. Most important challenge: We need to think about how all the components of an artificial intelligence should work together and how they need to be connected (the architecture!). We need to focus on comprehensive, totally integrated systems. Integrated systems might be a necessary step towards strong (human-level) AI (assuming this is a realistic goal!).
- 16. The road ahead Exampleof Integrated System Building a multipurpose, social, robot that can accumulate diverse knowledge over long periods of time (continuous learning) and that can use it effectively to decide what to do and how to do it. Requirements -A robot’s knowledge must be grounded in the physical world and capable of learning by interacting with the world (“embodied cognition”) -Because learning is prone to error, and the world is not deterministic, reasoning with such learned knowledge must deal with uncertainty -The representation languages must be expressive enough to represent the complex connections between objects, places, actions, people, time, and causation (understanding these requires common sense knowledge). - Also requires deep natural language understanding (Watson does not understand anything! neither does Google translator!) which depends on common sense knowledge too! - We need reliable computer vision systems capable of general object recognition and deep scene understanding which again depends on commons sense knowledge too!
- 17. Examples of thecommon sense knowledge that the multipurpose social robot should have • If a guest asks my waiter-robot for a glass of wine at a party, and the robot sees the glass he is picked up is cracked, or has a dead cockroach at the bottom, the robot should not simply pour the wine into the glass and serve it. • If a cat runs in front of my cleaning-robot while it is cleaning my house, the robot should neither run it over nor sweep it up nor put it away on a shelf. But, unfortunately we are not quite there!
- 18. Big failures inscene understanding! A red and white bus in front A man sitting in a bench with a dog of a building "a young boy is holding a baseball bat"
- 19. Big failures inlanguage understanding (Google translator)
- 20. The road ahead.An alternative to the common sense problem The development and rapid deployment of ubiquitous sensing and actuator devices makes it possible to create AI systems robustly grounded in direct experience with the world and learn (including common sense knowledge) from interacting with the world (i.e. work on Developmental Robotics)
- 21. Developmental Robotics: Learningthe musical instrument and playing by imitation (in collaboration with Imperial College) A.Ribes, J. Cerquides, Y. Demiris, R. Lopez de Mantaras; Active Learning of Object and Body Affordances with Time Constraints on a Humanoid Robot (in press) IEEE Transactions on Autonomous Mental Development
- 23. The road ahead:Very ambitious predictions -Robotic scientists that will serve as companions in discovery by formulating hypothesis and pursuing their confirmation (initial work on the ADAM and EVE systems by R. King et al. "The Automation of Science". Science 324 (5923): 85–89) -AI will play a central role in solving challenges in energy, the environment, and in healthcare. -A team of robots will beat the world’s human soccer champion team. (H. Kitano) -AI and other sciences (biology, material sciences, nanotechnology, economics,…) will come together and will have wide-ranging influences on our ideas about AI and on the machines we will build.
- 24. Hydrogen muscle forsilent robots Copper and nickel-based metal hydride powder is compressed into peanut-sized pellets and secured in a vessel. Hydrogen is pumped in to “charge” the pellets with the gas. A heater coil surrounds the vessel. Heat breaks the weak chemical bonds and releases the stored hydrogen. (Kim & Vanderhoff, Smart Mat. and Struct., 18, 2009 DOI: 10.1088/0964-1726/18/12/125014) Inflatable rubber tube surrounded by Kevlar fibre braiding
- 25. Artificial cartilage Chen, Briscoe,Armes, Klein; Lubrication at Physiological Pressures by Polyzwitterionic Brushes, Science 323, 2009 Each molecular group attracts 25 water molecules Performs well in pressures up to 5 megapascals 60 nm backbone
- 26. Touch sensitive artificialskin 1977 2008 Capacitive copper contacts A layer of silicone rubber acts as a spacer between those contacts and an outer layer of Lycra that carries a metal contact above each copper contact. The whole constitutes a pressure-sensing capacitor that can detect a touch as light as 1 gram. (Schmitz et al. IEEE Transactions on Robotics, 27(3). 2011) Carbon, or metal, charged polymer coats the fingers and palm. The transversal electrical resistance varies as a function of the pressure. Detected a touch greater than 20 grams. Applied to tactile object recognition. (López de Mántaras. PhD Thesis, Univ. Paul Sabatier. 1977)
- 27. Touch sensitive artificialskin (cont.)
- 28. Conclusions -AI is awell stablished research discipline with demonstrated successess and clear trajectories for its immediate future (but no “singularity”: the brain is much too complex!). -AI techniques are everywhere (although often are invisible): AI Algorithms increasingly run our lives: They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs.” -Most exciting opportunities for research lie on the interdisciplinary boundaries of AI with biology, linguistics, economics, material sciences, etc. That will provide insights and technologies towards building large-scale integrated systems. -AI is mature enough to undertake again the research on cognitive architectures and integrated systems (perhaps leading towards the goals of more general and strong AI?) and not only working on massive data-driven AI (which will NOT lead us towards general and strong AI).
- 29. BUT… …progress will beslow because: The problem of common sense knowledge is much too hard The field is much too dominated by massive data- driven AI …and because AI suffers from fragmentation (separate conferences and over-specialized college curricula) However the progress in weak AI will continue to be formidable
- 30. Final thoughts No matterhow sophisticated will future Artificial Intelligences be they will necessarily be different to human intelligences because: THE BODY SHAPES THE WAY WE THINK These artificial intelligences will be alien to human needs and therefore we should put limits on the developments of AI, particularly in fully autonomous (and therefore uncontrolable!) systems In any case…”KEEP CALM AND FORGET ABOUT THE SINGULARITY”