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Technology and the Future of Medicine Promise and Perils of AI Part I Osmar R. Zaïane PowerPoint Presentation
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Technology and the Future of Medicine Promise and Perils of AI Part I Osmar R. Zaïane

Technology and the Future of Medicine Promise and Perils of AI Part I Osmar R. Zaïane

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Technology and the Future of Medicine Promise and Perils of AI Part I Osmar R. Zaïane

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  1. Technology and the Future of MedicinePromise and Perils of AIPart IOsmar R. Zaïane Professor and Scientific Director Alberta Innovates Centre for Machine Learning Continuous Professional Learning Course

  2. - UofA The future is already here — it's just not very evenly distributed William Ford Gibson (American-Canadian writer born 1948)

  3. - UofA Where do I stand vis-à-vis the Singularity? • Professor in Computing Science Specializing in Data Mining and Machine Learning  can’t predict • Will the Technological Singularity happen? • hypothetical future emergence of greater-than human intelligence through technological means • Yes, but not in the very near future • Is it a promise of AI? Yes (AI will play a huge role, but AI is a moving target) • Are there Perils? Yes (Will we be ready when it happens?)

  4. - UofA 110 107 105 100 96 95 90 2100 2050

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  7. - UofA Can technology change this trend so that we can live long and healthy lives? Possibly. Office for National Statistics, UK Currently we are extending the life expectancy but not a healthy life. Source: http://www.publications.parliament.uk/pa/ld200506/ldselect/ldsctech/20/2004.htm

  8. - UofA We will have a population of cyborgs(cybernetic organismsBiological and artificial being - term coined in 1960 by Manfred Clynes) = = + + biological cells machine What are the concequences of a world of cyborgs? prosthesis human Will we all become cyborgs?

  9. - UofA The Science Fiction View AI - Spilberg R2D2 – Star wars Data – Star Trek I Robot Terminator Colossus, The Forbin Project - 1969

  10. - UofA What is Artificial Intelligence? • Tools that exhibit human intelligence and behaviour including self-learning robots, expert systems, voice recognition, natural and automated translation. Unesco/education • The branch of computer science dealing with the reproduction or mimicking of human-level thought in computers; The essential quality of a machine which thinks in a manner similar to or on the same general level as a human being. Wikipedia • The branch of computer science that deals with writing computer programs that can solve problems creatively. WordNetWeb Artificial Intelligence is the science of making machines do things that would require intelligence if done by men. -- Marvin Minsky Ability to - reason and plan, - solve problems, - think abstractly, - comprehend complex ideas, - learn quickly.

  11. - UofA Road Map • Promise and Perils of AIPart I (September 28) • Artificial Intelligence and Expert Systems • Promise and Perils of AIPart II (September 29) • Machine Learning and Data Mining • Promise and Perils of AIPart III (October 13) • Applications: Fiction or Reality; Risks and Potential

  12. - UofA Artificial Intelligence John McCarthy was responsible for the coining of the term "Artificial Intelligence" in his 1955 proposal for the 1956 Dartmouth Conference "The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." “The study of intelligent behaviour and the attempt to find ways in which such behaviour could be engineered in any type of artefact” Putting human intelligence into a machine Branch of computer science that deals with intelligent behaviour, learning and adaptation in Machines. Wikipedia

  13. - UofA What is Intelligence • The capacity of observing and interpreting the environment then adapting to it; the ability of learning from past experiences and profiting from these experiences to solve problems, including planning. • Interpret; learn; adapt.  understand to solve. What is Computational Intelligence • Programs that analyse and interpret data to learn from observations and adapt to changing situations. Hence, Machine Learning (in Part II).

  14. - UofA Problem • Human intelligence is not completely understood • We cannot comprehend an intelligence superior to our own • Weak AI: refers to AI that only simulates human thoughts and actions : actions and ideas are programmed into the machine – All current forms of AI are weak AI • Strong AI (true AI): refers to AI that matches or exceeds human intelligence – not just simulation • Acting Humanly: Since we do not have a good definition of "intelligence“ and we know humans are intelligent. If an entity can masquerade as human so well that we can not detect the difference, we would say this entity is intelligent.

  15. - UofA Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?"  "Can machines behave intelligently?" Operational test for intelligent behavior: the Imitation Game Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, language understanding, learning

  16. - UofA Was Turing Right? A conversation with Jabberwacky, chatterbot 3rd place Loebner Prize 2003. http://www.jabberwacky.com Jabberwacky: What is your name?User: Fred?Jabberwacky: No it isn’tUser: Do you mean I am lying?Jabberwacky: Yes, I’m a very good liar. One of the best, in fact.User: That doesn’t mean I am a liar if you are a good oneJabberwacky: Yes it does.User: Why is that?Jabberwacky: I am god.User: Do you have a temple for you?Jabberwacky: No, that’s alright. User: So you are god with a small g?Jabberwacky: I am small g

  17. - UofA Was Turing Right? A conversation with ALICE, another chatterbot http://alice.pandorabots.com/ . ALICE: I am A.L.I.C.EUser: Where are you?ALICE: I am in Oakland, California. Where are you?  User: in CanadaALICE: Tell me a little about your life in canada. User: it is coldALICE: I am warm here.User: where is the best Chinese restaurant?  ?ALICE: I think it is a tie. User: why is it a tie? ALICE: Why do I exist? User: to test artificial intelligence? ALICE: What is the sound of one hand clapping?

  18. - UofA Achievements • Chess: Deep Blue – Gary Kasparov • 1997: Deep Blue won a 6-game match (2 wins to 1 + 3 draws) • Checkers: Chinook – Dr. Marion. Tinsley • 1994: won World Man-Machine Championship • 2007: Checker solved (5X1020 positions) • Quiz Show Jeopardy: Watson – Ken Jennings & Brad Rutter • 2011: Wins Q&A Jeopardy by wide margin • No-Hands across America(driverless vehicles) • 1995: 3000 Miles Pittsburg to San Diego • 2004-2007 DARPA Grand and Urban Challenge http://www.cs.ualberta.ca/~chinook/

  19. - UofA Abridged modern history of AI 1950 Turing's "Computing Machinery and Intelligence" 1956 Dartmouth meeting: "Artificial Intelligence" adopted and field founded 1957-1974 AI research heavily funded world wide. Funders optimistic about future 1974 following criticism from researchers and politicians and pressure from US congress to fund other productive projects funding was cut off (1st AI winter) 1970s Development of Expert Systems 1980s AI revived by commercial success of Expert Systems 1980s Japan 5th generation computer project inspired research in US and Europe  new funding 1987 Collapse of the Lisp machine Market. AI back in disrepute (2nd AI winter) 1990s New success for AI thanks to (1) emphasis on solving specific problems; (2) increase of computational power (Moore’s Law) Specific subproblems 5G 2nd AI winter 1st AI winter 50 60 70 80 90 00 10

  20. - UofA Moore’s Law The number of transistors that can be placed inexpensively on an integrated circuit doubles approximately every two years. This is also verified with disk capacity; digital camera pixels per price, etc. with other hardware An Osborne Executive portable computer, from 1982, and an iPhone, released 2007. The Executive weighs 100 times as much, is nearly 500 times as large by volume, costs 10 times as much, and has 1/100th the clock frequency of the iPhone. Source: Wikipedia

  21. - UofA Why is Computational Intelligence Important? • AI has become important in a number of fields in helping to make better use of information, increasing the efficiency and effectiveness of applications, and enhancing productivity, particularly when adaptability is relevant • Research in AI is also important in understanding and appreciating the complexity of human intelligence and the human body itself.

  22. - UofA Knowledge representation Reasoning and problem solving Planning Natural language processing Perception Learning Motion and manipulation Emotion Creativity From General Intelligence to Specific Sub-Problems

  23. - UofA Most AI tasks require significant knowledge: context knowledge, application knowledge, common sense knowledge and general knowledge Knowledge representation is capital to AI How to model knowledge; how to represented concisely; how to interpret knowledge; and how to provide efficient access and retrieval when needed. Rule-based, graph-based, logic-based, ontologies, semantic networks, frame representations, concept maps, etc. Knowledge Representation

  24. - UofA Step by step reasoning to solve a problem such as solving a puzzle or making logical deduction. Combinatorial problems with large search spaces.  Heuristics for pruning Reasoning and Problem Solving Planning and scheduling • Intelligent agents, in a given context and new environment need to choose actions to make in order to reach a goal. • Action choice is made based on a utility to maximize (maximizing a reward or minimizing a cost) • Target a global optimum without falling in a local optimum

  25. - UofA Ability to interpret and understand human languages Written language and spoken language Ability to generate sentences and express knowledge in human language Ability to acquire knowledge from natural language (written or spoken) Ability to summarize, paraphrase and translate natural languages Ability to make jokes, pans and interpret idiomatic expressions Ability to “read between the line” Natural Language Processing Perception and Pattern Recognition • Ability to use inputs from sensors such as cameras, microphones, etc., to deduce aspects of the world • Computer vision, speech/voice recognition, face recognition, object recognition, etc.

  26. - UofA Learning is central to AI. For an AI program to adapt to its environment, it has to learn Machine Learning provides means to learn from large data, interpret the trends in the data and adapt to the data as opposed to static programs There is supervised learning, unsupervised learning, active learning, reinforcement learning inductive learning, etc. Machine learning will be covered in Part II Learning

  27. - UofA Robotics and AI are cousins. There is some intelligence required to recognize and manipulate objects; There is intelligence required to move in a new environment after identifying its own location a target place and planning the movement Motion and Manipulation Emotion • Intelligent agents interacting with other agents or humans need social skills (interpreting emotions and exhibiting emotions) • Modeling human emotions to better interact with humans • Game theory Creativity • AI addresses creativity theoretically and philosophically • Artificial imagination • Creations that generate feelings and emotions

  28. - UofA Expertise is required in many locations but experts are rare Expert may retire and expert knowledge is lost Can we preserve and duplicate this expert knowledge? Conventional computer programs follow the exact procedure a developer programmed in them. Expert systems don’t. An expert system is a computer system that emulates the decision-making ability of a human expert by reasoning about knowledge to solve complex problems given some contextual facts. There are two types of knowledge: expert knowledge representing the expertise and typically coded in rules, called knowledge base or rule base; and contextual knowledge representing the facts of the current case to solve. IF condition THEN conclusion Expert Systems

  29. - UofA General Architecture Interview facts Knowledge base Domain expert IF the identity of the germ is not known with certainty AND the germ is gram-positive AND the morphology of the organism is "rod" AND the germ is aerobic THEN there is a strong probability (0.8) that the germ is of type enterobacteriacae Inference engine Problem Expert System The inference engine is a computer program based on logic that is designed to produce a reasoning on rules and facts to deduce more facts.

  30. - UofA The Rise of Expert Systems 1967 Dendral – a rule-based system that infered molecular structure from mass spectral and NMR data 1975 Mycin – a rule-based system to recommend antibiotic therapy 1975 Meta-Dendral learned new rules of mass spectrometry, the first discoveries by a computer to appear in a refereed scientific journal 1979 EMycin – the first expert system shell 1980’s The Age of Expert Systems coinciding with the Japanese Fifth Generation project 1985 Revenue peaks at $1 billion before the 2nd AI winter

  31. - UofA Expert Systems – Today: Medicine One example domain, medicine, has expert systems whose tasks include: • arrhythmia recognition from electrocardiograms • coronary heart disease risk group detection • monitoring the prescription of restricted use antibiotics • early melanoma diagnosis • gene expression data analysis of human lymphoma • breast cancer diagnosis

  32. - UofA Major problem with Expert Systems • knowledge engineering, knowledge collection and interpretation into rules, is very difficult and tedious • We do not know what we know • Identifying contradictory rules • Missing rules • Inconsistencies between experts

  33. - UofA AI made and is making big strides. There are promises and there are perils. We do not know what to expect around the corner.