Introduction to Artificial intelligence, Lecture notes of Artificial Intelligence

Download Introduction to Artificial intelligence and more Lecture notes Artificial Intelligence in PDF only on Docsity! Artificial Intelligence Introduction VORERRLEAY eR ULC at eax OLE Arete Locks PRL toes ats) Perrot teeter teeeat tn Pree terete eater tent itt aoe . Pua eae c ace) Y i Rebs h hs .) \ PES SOC anny ' PPOP ERT CELT LE pee ee, peu FORTE cp Lea YEO Peetu : " SESE TEC SREY N , . 3 , aS Po PEeuates Acros eee ahs Tees ae MATT HHO AAY ae AI in the movies Definition of AI “Intelligence: The ability to learn and solve problems” Webster’s Dictionary. “Artificial intelligence (AI) is the intelligence exhibited by machines or software’ Wikipedia. “The science and engineering of making intelligent machines” McCarthy. Definition of AI “Intelligence: The ability to learn and solve problems” Webster’s Dictionary. “Artificial intelligence (AI) is the intelligence exhibited by machines or software’ Wikipedia. “The science and engineering of making intelligent machines” McCarthy. “The study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.” Russel and Norvig AI book. Why AI? “Just as the Industrial Revolution freed up a lot of humanity from physical drudgery, I think AI has the potential to free up humanity from a lot of the mental drudgery.” Andrew Ng. What is AI? Acting humanly: • Turing test (Alan Turing 1950): A computer passes the test of intelligence, if it can fool a human interrogator. Credit: From Russel and Norvig slides. • Major components of AI: knowledge, reasoning, language, understanding, learning. What is AI? Acting humanly: What is AI? Thinking rationally: Laws of thoughts. • Codify “right thinking” with logic. • Several Greek schools developed various forms of logic:notation and rules of derivation for thoughts. • Problems: 1. Not all knowledge can be expressed with logical notations. 2. Computational blow up. Applications of Al > Applications of AI Speech recognition • Virtual assistants: Siri (Apple), Echo (Amazon), Google Now, Cor- tana (Microsoft). • “They” helps get things done: send an email, make an appointment, find a restaurant, tell you the weather and more. • Leverage deep neural networks to handle speech recognition and natural language understanding. Applications of AI Handwriting recognition (check, zipcode) Applications of AI Machine translation • MT has gone through ups and downs. • Today, Statistical Machine Translation leverages the vast amounts of available translated corpuses. • While there is room for improvement, machine translation has made significant progress. Applications of Al Machine translation Google Translate Aramis Engiah French Detect ianguage = s «—Ergten Aratic Fromm ~ Ovtect language Comican Guat Kazakh Maraty were ewe Cocaten —Haltinn Cece Khener Morganen Sra Amarin Caech Mouse Korean Myanmar (rene) Shale Arbertc Cerin nl Kurgan (Kurmarg) = Nepatt Sover antic Oven el Kene Noreegen ‘Sovernan =- Aone fagish Hin Lae Wears tone Type text of @ website address or Wansiate 2 dock Anerbapers Seems >< ee ra! rm nappa Exerias Hungary Later Motan Sercanese Feo hota Utusan Ponranete Saar Preah ito Lummerbourgah Pepa Sweden Frenen eoresan Macedoran Rorrersan ~~ Freee on Malagasy Ronnie to Geboen aber Maley Semecen Tou Gergen Japanese Matryaar ‘Scots Gente The Cerrar CS otahated Varese Sete Tuten Gree Karnode Mace Senco Uwrenien > JOIN THE TRANSLATE COMMUNITY Googie Transiate or Business Teanaiwior Took «= Website Transtior «= Gotu! Matuet Fircter 100+ languages FEeGTOyH? Applications of Al Machine translation Google Transiate Mahe Onginh french Ceteet engemge = ~~ eh ae een Sa out of sight, out of mind “hors de vue, hors de I'esprit “= mx ea See aise AO AE A Ot te OA OF eh. i On OL Ont OF BF deep oe Applications of Al Search engines Google Machine teaming Eg we imager Vans Shopeeng Patents More = Search toots About 148,000,000 renults (0.27 seconds: Machine learning - Wikipedia, the free encyclopedia on witipeda.orpwivMachine leaming + Machine learning. a branch of artificial intelligence, corcems the construction ang study of syaterns thet can learn from Gata For example, a machine learning _. List of machine learning . . Supervised learring - Computational leaning Deory Machine Learning | Coursera hittps:\www.coursera orgfcourseim! + Machine learning is the science of getting computers to act without being exptcitly programmed. in (he pas! Gecade, machine learning has given vs seif- deving ... Machine Leaming - Springer wow. soringer.com » Home » Computer Science » Artificial intelligence ~ Machine Learning is an international forum for research on computational approaches: to inarring. The jours publishes articles reporting substartive cesuits on ... by FS Parsing - 2013 ‘340+ items - What We Do, Much of our work on language, speech, Frane-Semartic Parsing. Ciparjen Das, Desai Chen, Ancht FT. Martine Trering Higty Muti-<class Linear Ciassifiers. Maya R. Gupta, Samy Bengo ICM Beijing kemlee! © Applications of Al ‘The Body Shop Century 21 Dept Store > Senana Republic Email Tele ad ie Deasedt on ornate from your males ant miormatios tom your Googe account Ags Semtings pets you in coctrot of the oda you see. Buy 3 Get 3 or Buy 2 Got 2 FREE All Bath & Body - Vege Have you lopped your food and fitness today? - Food 4 Fimens Planner Dear 0:26 am Say Spaaaaal 50% Off Setal Pampering Package + More V-Day Gifts - The Art 7:06 am 39% off starts right now! - 55% off ents 1/22. Online only. Carr) see the mages © 5:04 am Applications of AI Face detection Viola-Jones method. Applications of Al Face recognition FACE RECOGNITION Applications of AI Detection of breast cancer in mammography images Applications of AI Chess (1997): Kasparov vs. IBM Deep Blue (Left) Copyright 2007, S.M.S.I., Inc. - Owen Williams, The Kasparov Agency, via Wikimedia Commons (Right) By James the photographer, via Wikimedia Commons Powerful search algorithms! Applications of AI Autonomous driving By User Spaceape on en.wikipedia, via Wikimedia Commons • DARPA Grand Challenge – 2005: 132 miles – 2007: Urban challenge – 2009: Google self-driving car State-of-the-art applications • Speech recognition • Autonomous planning and scheduling • Financial forecasting • Game playing, video games • Spam fighting • Logistics planning • Robotics (household, surgery, navigation) • Machine translation • Information extraction • VLSI layout • Automatic assembly • Sentiment analysis • Fraud detection • Recommendation systems • Web search engines • Autonomous cars • Energy optimization • Question answering systems • Social network analysis • Medical diagnosis, imaging • Route finding • Traveling salesperson • Protein design • Document summarization • Transportation/scheduling • Computer animation State-of-the-art applications • Speech recognition • Autonomous planning and scheduling • Financial forecasting • Game playing, video games • Spam fighting • Logistics planning • Robotics (household, surgery, navigation) • Machine translation • Information extraction • VLSI layout • Automatic assembly • Sentiment analysis • Fraud detection • Recommendation systems • Web search engines • Autonomous cars • Energy optimization • Question answering systems • Social network analysis • Medical diagnosis, imaging • Route finding • Traveling salesperson • Protein design • Document summarization • Transportation/scheduling • Computer animation Many more! Foundation of AI • Neuroscience – Study of brain functioning. – How brains and computers are (dis)similar. • Psychology – How do we think and act? – Cognitive psychology perceives the brain as an information processing machine. – Led to the development of the field cognitive science: how could computer models be used to study language, memory, and thinking from a psychological perspective. • Computer engineering – Cares about how to build powerful machines to make AI possible. – E.g., Self-driving cars are possible today thanks to advances in computer engineering. Foundation of AI • Control theory and cybernetics – Design simple optimal agents receiving feedback from the environment. – Modern control theory design systems that maximize an objective function over time. • Linguistics – How are language and thinking related. – Modern linguistics + AI = Computational linguistics (Nat- ural language processing). AI founders • Aristotle • Alan Turing • John Mc Carthy • Warren McCulloh • Walter Pitts • Claude Shannon • Marvin Minsky • Dean Edmonds • Herbert Simon • Allen Newell • David Waltz • Tom Mitchell • Stuart J. Russell • Peter Norvig • etc. History of AI • 1990-present: Scientific approaches – Neural Networks: le retour – The emergence of intelligent agents – AI becomes “scientific”, use of probability to model uncer- tainty – AI Spring! – The availability of very large datasets. ⇤ Data will drive future discoveries and alleviate the com- plexity in AI. Course logistics • Course level: Master’s – challenging! • Prerequisites: You are required to have some knowledge of programming and an understanding of probability. Python is the programming language in this course. • Assignments: There will be two kinds of assignments: – Quizzes (conceptual): Test your understanding of the lectures. Please read the questions very carefully. – Projects (programming): The course o↵ers an excellent opportunity for students to dive into Python while solving AI problems and learning its applications. Course logistics • Suggested readings: – We recommend this book, which is the main reference in the field: Artificial Intelligence, A Modern Approach. Stuart Russell and Peter Norvig. Third Edition. Pearson Education. http://aima.cs.berkeley.edu/ – Check out the list of readings, useful links we suggest for this course. Rational intelligent agents • This course is about designing intelligent agents. • An agent perceives the environment and act upon that envi- ronment to achieve some task. • An agent is function from percepts to actions. • We care specifically about rational agents. • Rationality is relative to how to act to maximize a perfor- mance measure. • AI aims to design the best agents (programs) that achieve the best performance given the computational limitations. Agent = Architecture + Program Search agents • Agents that work towards a goal. • Agents consider the impact of actions on future states. • Agent’s job is to identify the action or series of actions that lead to the goal. • Paths come with di↵erent costs and depths. • Two kinds of search: – Uninformed Search (use no domain knowledge): BFS, DFS, UCS, etc. – Informed Search (use heuristic to reach the goal faster): Greedy search, A⇤, etc. Search agents The 8-queen problem: on a chess board, place 8 queens so that no queen is attacking any other horizontally, vertically or diagonally. Adversarial search: games • Adversarial search problems ⌘ game • Adversarial ⌘ There is an opponent we can’t control! • Game vs. search: optimal solution is not a sequence of actions but a strategy (policy). If opponent does a, agent does b, else if opponent does c, agent does d, etc. • Tedious and fragile if hard-coded (i.e., implemented with rules). • Concepts/methods: Minimax algorithm, ↵ pruning, stochastic games. Machine learning “How do we create computer programs that improve with experience?” Tom Mitchell Machine learning Binary classification (categorization) Input: “Examples” with labels. (x1, y1), . . . , (xn, yn) / xi 2 X ⇢ Rn, yi 2 Y = {1,+1} Output: h : X ! Y Example: Approve credit yes/no, spam/ham. !" #$ %& "' ( ' !"#$%&"')' *"+,-,./'0.%/1#&2' Concepts/methods: Supervised learning, classification, K near- est neighbors, perceptrons, neural networks, linear regression, etc. Constraint satisfaction Variables: X l,c for 1  l  9 and 1  c  9. Constraints: All 3x3 grid, row, column, must contain digits 1..9 and all of them! Solution: Find the assignments to the variables that satisfy the constraints. Constraint satisfaction Variables: X l,c for 1  l  9 and 1  c  9. Constraints: All 3x3 grid, row, column, must contain digits 1..9 and all of them! Solution: Find the assignments to the variables that satisfy the constraints. Logical Agents • Logic can be used by an agent to model the world. • Sentences in PL and FOL have a fixed syntax. • With symbols and connectives we can form logical sentences: Example: hot ^ sunny ) beach _ pool • Syntax and Semantic represent two important and distinct aspects in logic. • Inference: Given a Knowledge Base (KB) (set of sentences in logic), given a query ↵, output whether KB entails ↵, noted: KB |= ↵ • Concepts/methods: Modus Ponens, sound and complete in- ference, horn clauses, etc. Historical moment today In memory of Alan Turing (1912-1954) • Famous British mathematician. • Code breaker during World War II. • Proposed an operational test for intelligent behavior: The Im- itation Game. • In “Computing machinery and intelligence” (1950), he laid down AI major components: (language, reasoning, knowledge, learning, understanding). http://www.turingarchive.org/browse.php/B/9 Summary • AI is a hard (computational complexity, language, vision, etc), and a broad field with high impact on humanity and society. • What can AI do for us is already amazing! • AI systems do not have to model human/nature but can act like or be inspired by human/nature. • How human think is beyond the scope of this course. • Rational (do the right thing) agents are central to our approach of AI. • Note that rationality is not always possible in complicated en- vironment but we will still aim to build rational agents. Summary • AI may be perceived as a scary area! Is AI a threat to our humankind? • Professor Stephen Hawking, eminent scientist told BBC: “The development of full artificial intelligence could spell the end of the human race.” • AI is a flourishing and exciting field: everyone can contribute. • Looking forward for an exciting journey together!