CS 294-5: Statistical Natural Language Processing

CS 294-5: Statistical Natural Language Processing

CAP 5636 Advanced Artificial Intelligence History and positioning Instructor: Lotzi Blni [These slides were adapted from the ones created by Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley, available at http://ai.berkeley.edu.] Textbook Not required, but for students who want to read more we recommend Russell & Norvig, AI: A Modern Approach, 3rd Ed. Today What is artificial intelligence? What can AI do?

What is this course? Sci-Fi AI? What is AI? The science of making machines that: Think like people Think rationally Act like people Act rationally

Rational Decisions Well use the term rational in a very specific, technical way: Rational: maximally achieving pre-defined goals Rationality only concerns what decisions are made (not the thought process behind them) Goals are expressed in terms of the utility of outcomes

Being rational means maximizing your expected utility A better title for this course would be: Computational Rationality Maximize Your Expected Utility What About the Brain? Brains (human minds) are very good at making rational decisions, but not perfect Brains arent as modular as software,

so hard to reverse engineer! Brains are to intelligence as wings are to flight Lessons learned from the brain: memory and simulation are key to decision making A (Short) History of AI Demo: HISTORY MT1950.wmv A (Short) History of AI 1940-1950: Early days 1943: McCulloch & Pitts: Boolean circuit model of brain

1950: Turing's Computing Machinery and Intelligence 195070: Excitement: Look, Ma, no hands! 1950s: Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1956: Dartmouth meeting: Artificial Intelligence adopted 1965: Robinson's complete algorithm for logical reasoning 197090: Knowledge-based approaches 196979: Early development of knowledge-based systems 198088: Expert systems industry booms 198893: Expert systems industry busts: AI Winter 1990: Statistical approaches

Resurgence of probability, focus on uncertainty General increase in technical depth Agents and learning systems AI Spring? 2000: Where are we now? What Can AI Do? Quiz: Which of the following can be done at present?

Play a decent game of table tennis? Play a decent game of Jeopardy? Drive safely along a curving mountain road? Drive safely along University Blvd? Buy a week's worth of groceries on the web? Buy a week's worth of groceries at Publix? Discover and prove a new mathematical theorem?

Converse successfully with another person for an hour? Perform a surgical operation? Put away the dishes and fold the laundry? Translate spoken Chinese into spoken English in real time? Write an intentionally funny story? Unintentionally Funny Stories One day Joe Bear was hungry. He asked his friend Irving Bird where some honey was. Irving told him there was a beehive in the oak tree. Joe walked to the oak tree. He ate the beehive. The End. Henry Squirrel was thirsty. He walked over to the river bank where his good friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity drowned.

The End. Once upon a time there was a dishonest fox and a vain crow. One day the crow was sitting in his tree, holding a piece of cheese in his mouth. He noticed that he was holding the piece of cheese. He became hungry, and swallowed the cheese. The fox walked over to the crow. The End. [Shank, Tale-Spin System, 1984] Natural Language Speech technologies (e.g. Siri) Automatic speech recognition (ASR) Text-to-speech synthesis (TTS) Dialog systems Demo: NLP ASR tvsample.avi

Natural Language Speech technologies (e.g. Siri) Automatic speech recognition (ASR) Text-to-speech synthesis (TTS) Dialog systems Language processing technologies Question answering Machine translation Vision (Perception) Object and face recognition Scene segmentation

Image classification Demo1: VISION lec_1_t2_video.flv Images from Erik Sudderth (left), wikipedia (right) Demo2: VISION lec_1_obj_rec_0.mpg Robotics Robotics Part mech. eng. Part AI Reality much harder than simulations!

Technologies Vehicles Rescue Soccer! Lots of automation In this class: We ignore mechanical aspects

Methods for planning Images from UC Berkeley, Boston Dynamics, RoboCup, Google Robot AI (at UCF) Logic Logical systems Theorem provers NASA fault diagnosis Question answering Methods: Deduction systems

Constraint satisfaction Satisfiability solvers (huge advances!) Image from Bart Selman Game Playing Classic Moment: May, '97: Deep Blue vs. Kasparov First match won against world champion

Intelligent creative play 200 million board positions per second Humans understood 99.9 of Deep Blue's moves Can do about the same now with a PC cluster Open question: How does human cognition deal with the search space explosion of chess? Or: how can humans compete with computers at all?? 1996: Kasparov Beats Deep Blue I could feel --- I could smell --- a new kind of intelligence across the table. 1997: Deep Blue Beats Kasparov

Deep Blue hasn't proven anything. Huge game-playing advances recently, e.g. in Go! Text from Bart Selman, image from IBMs Deep Blue pages Alpha Go Highest ranked human player, Lee Sedol defeated by Alpha Go (March 2016) DeepMind British deep learning startup, bought by Google in 2014 for 500 million dollars Why is this news? Go was considered harder than other games, due to combinatorial explosion. Also, some degree of mystique How does it work

Neural networks bootstrapped from a database of 30 million moves from expert players Further improvements through extensive self-play For some reason, this is claimed to be the way humans learn Go Decision Making Applied AI involves many kinds of automation Scheduling, e.g. airline routing, military Route planning, e.g. Google maps Medical diagnosis Web search engines Spam classifiers Automated help desks Fraud detection

Product recommendations Lots more! An agent is an entity that perceives and acts. A rational agent selects actions that maximize its (expected) utility. Characteristics of the percepts, environment, and action space dictate techniques for selecting

rational actions This course is about: General AI techniques for a variety of problem types Learning to recognize when and how a new problem can be solved with an existing technique Sensors Percepts

? Actuators Actions Environment Agent Designing Rational Agents

Pac-Man as an Agent Agent Sensors Environment Percepts ? Actuators Actions

Pac-Man is a registered trademark of Namco-Bandai Games, used here for educational purposes Demo1: pacman-l1.mp4 or L1D2 Course Topics Part I: Making Decisions Fast search / planning Constraint satisfaction Adversarial and uncertain search Part II: Reasoning under Uncertainty

Bayes nets Decision theory Neural networks, deep learning Machine learning Part III: Miscellaneous applications Natural language, vision, robotics, games,

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