ESTIEM Academic Days, Kyiv, 1 April 2016

1. Introduction toAI
ESTIEM Academic Days, Kyiv
DymytrYovchev
1 April 2016

2. What are we
doing?
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3. Sci-FiAI?
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4. Why studyAI?
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5. Trends
 BlockChain
 #AI #d14n #IoT
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6. Trends
 Automated Scoring
 #AI #d14n
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7. Trends
 Automated Design
 #AI #VR
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8. Trends
 Health
 #AI #VR #IoT #D14N
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9. Trends
 VR/AR
 #AI #VR #IoT
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10. Trends
 Sensors Everywhere
 #AI #VR #IoT
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11. What is
Intelligence?
 Intelligence: “the capacity to learn and solve problems”
(Websters dictionary)
 in particular,
 the ability to solve novel problems
 the ability to act rationally
 the ability to act like humans
 Artificial Intelligence
 build and understand intelligent entities or agents
 2 main approaches: “engineering” versus “cognitive
modeling”
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12. What isAI?
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13. What isAI?
(John McCarthy, Stanford University)
 What is artificial intelligence?
It is the science and engineering of making intelligent machines,
especially intelligent computer programs. It is related to the similar task
of using computers to understand human intelligence, but AI does not
have to confine itself to methods that are biologically observable.
 Yes, but what is intelligence?
Intelligence is the computational part of the ability to achieve goals in the
world.Varying kinds and degrees of intelligence occur in people, many
animals and some machines.
 Isn't there a solid definition of intelligence that doesn't depend on
relating it to human intelligence?
Not yet.The problem is that we cannot yet characterize in general what
kinds of computational procedures we want to call intelligent.We
understand some of the mechanisms of intelligence and not others.
 More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
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14. Acting
humanly:
TuringTest
 Telegram
 Social Network
 Bots!
 Marvin Minsky on Singularity 1 on 1:TheTuringTest is a Joke!
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15. Thinking
humanly
 1960s "cognitive revolution": information-processing
psychology
 Requires scientific theories of internal activities of the
brain
 How to validate? Requires
1) Predicting and testing behavior of human
subjects (top-down)
or 2) Direct identification from neurological data
(bottom-up)
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16. Thinking
rationally
 Aristotle: what are correct arguments/thought
processes?
 Several Greek schools developed various forms of
logic: notation and rules of derivation for thoughts;
may or may not have proceeded to the idea of
mechanization
 Direct line through mathematics and philosophy to
modernAI
 Problems:
 Not all intelligent behavior is mediated by logical deliberation
 What is the purpose of thinking?What thoughts should I have?
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17. Acting
rationally
 Rational behavior: doing the right thing
 The right thing: that which is expected to maximize goal
achievement, given the available information
 Doesn't necessarily involve thinking – e.g., blinking reflex – but
thinking should be in the service of rational action
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18. What’s
involved inAI?
 Ability to interact with the real world
 to perceive, understand, and act
 e.g., speech recognition and understanding and synthesis
 e.g., image understanding
 e.g., ability to take actions, have an effect
 Reasoning and Planning
 modeling the external world, given input
 solving new problems, planning, and making decisions
 ability to deal with unexpected problems, uncertainties
 Learning and Adaptation
 we are continuously learning and adapting
 our internal models are always being “updated”
 e.g., a baby learning to categorize and recognize animals
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19. Academic
Disciplines
relevant toAI
 Philosophy Logic, methods of reasoning, mind as physical
system, foundations of learning,
language, rationality.
 Mathematics Formal representation and proof, algorithms,
computation, (un)decidability, (in)tractability
 Probability/Statistics modeling uncertainty, learning from data
 Economics utility, decision theory, rational economic agents
 Neuroscience neurons as information processing units.
 Psychology/ how do people behave, perceive, process
cognitive
Cognitive Science information, represent knowledge.
 Computer building fast computers
engineering
 Control theory design systems that maximize an objective
function over time
 Linguistics knowledge representation, grammars
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20. A (Short)
History ofAI
 1943: early beginnings
 McCulloch & Pitts: Boolean circuit model of brain
 1950:Turing
 Turing's "Computing Machinery and Intelligence“
 1956: birth of AI
 Dartmouth meeting: "Artificial Intelligence“ name adopted
 1950s: initial promise
 Early AI programs, including
 Samuel's checkers program
 Newell & Simon's LogicTheorist
 1955-65: “great enthusiasm”
 Newell and Simon: GPS, general problem solver
 Gelertner: GeometryTheorem Prover
 McCarthy: invention of LISP
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21. A (Short)
History ofAI
 1966—73: Reality dawns
 Realization that many AI problems are intractable
 Limitations of existing neural network methods identified
 Neural network research almost disappears
 1969—85:Adding domain knowledge
 Development of knowledge-based systems
 Success of rule-based expert systems,
 E.g., DENDRAL, MYCIN
 But were brittle and did not scale well in practice
 1986-- Rise of machine learning
 Neural networks return to popularity
 Major advances in machine learning algorithms and applications
 1990-- Role of uncertainty
 Bayesian networks as a knowledge representation framework
 1995--AI as Science
 Integration of learning, reasoning, knowledge representation
 AI methods used in vision, language, data mining, etc
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22. A (Short)
History ofAI
 1966—73: Reality dawns
 Realization that many AI problems are intractable
 Limitations of existing neural network methods identified
 Neural network research almost disappears
 1969—85:Adding domain knowledge
 Development of knowledge-based systems
 Success of rule-based expert systems,
 E.g., DENDRAL, MYCIN
 But were brittle and did not scale well in practice
 1986-- Rise of machine learning
 Neural networks return to popularity
 Major advances in machine learning algorithms and applications
 1990-- Role of uncertainty
 Bayesian networks as a knowledge representation framework
 1995--AI as Science
 Integration of learning, reasoning, knowledge representation
 AI methods used in vision, language, data mining, etc
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23. Success
Stories
 Deep Blue defeated the reigning world chess championGarry
Kasparov in 1997
 AI program proved a mathematical conjecture (Robbins
conjecture) unsolved for decades
 During the 1991 GulfWar, US forces deployed anAI logistics
planning and scheduling program that involved up to 50,000
vehicles, cargo, and people
 NASA's on-board autonomous planning program controlled the
scheduling of operations for a spacecraft
 Proverb solves crossword puzzles better than most humans
 Robot driving: DARPA grand challenge 2003-2007
 2006: face recognition software available in consumer cameras
 2016 AplhaGo win Go game.
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24. What canAI
Do?
 Play a decent game of table tennis?
 Play a decent game of Go?
 Drive safely along a curving mountain road?
 Drive safely along center part of the town?
 Buy a week’s worth of groceries on the web?
 Buy a week’s worth of groceries at Khreshatyk Street?
 Discover and prove a new mathematical theorem?
 Converse successfully with another person for an hour?
 Perform a surgical operation?
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25. WhatAICan
Do: Robotics
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26. Challenges-1
 Kaggle.com
 https://www.quora.com/Are-there-any-other-
competition-platforms-like-Kaggle-for-other-fields-in-
computer-science
 russianaicup.ru
 blackboxchallenge.com
 www.xprize.org/ai
 www.quora.com/What-are-the-most-interesting-
online-AI-competitions
 www.hackerrank.com/domains/ai/introduction
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27. Challenges-2
 vindinium.org/starters
 theaigames.com/
 www.battlecode.org/
 www.codecup.nl/intro.php
 sscaitournament.com/
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28. Conferences
 http://ijcai-
16.org/index.php/welcome/view/accepted_tutorials
 http://cig16.image.ece.ntua.gr/
 http://aigamedev.com/
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29. OnlineCourses
 https://www.udacity.com/course/artificial-intelligence-for-
robotics--cs373
 https://www.udacity.com/course/intro-to-artificial-intelligence--
cs271
 https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-
cs188-1x
 https://www.coursera.org/course/aiplan
 https://www.class-central.com/search?q=Artificial+Intelligence
 http://ocw.mit.edu/courses/electrical-engineering-and-computer-
science/6-034-artificial-intelligence-fall-2010/
 http://courses.nucl.ai/courses/pmgai/
 https://developer.nvidia.com/deep-learning-courses
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30. Books
 S. Russell and P. Norvig Artificial Intelligence: A
Modern Approach Prentice Hall, (Last Edition)
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31. Questions?
Thank you for your attention!
Fb:YovchevDK
Skype: dimitr_yo
Telegram: +380964347667
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32. Sources
 https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-
cs188-1x
 http://www.comp.nus.edu.sg/~cs3243
 http://oim.asu.kpi.ua/courses/msai/
 http://iLab.usc.edu/classes/2002cs561/
 http://www.ics.uci.edu/~smyth/courses/cs271/
 http://www.slideshare.net/tceh_com/it-2016
 Images: google.com
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33. Trends+
 Telegram Bots
 Windows&Skype Bots (Skype Bot SDK)
 http://blogs.skype.com/2016/03/30/skype-bots-
preview-comes-to-consumers-and-developers/
 Microsoft.com/cognitive
 Cloud AI (Azure, Watson)
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