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![AI
Rational
Agents
[decisions]
Robots
[physically
embodied]
Machine Learning
[learning decisions;
sometimes independent]
NLP
Computer
Vision
Human-AI
Interaction](https://image.slidesharecdn.com/slides01-251003092754-a48d2855/75/What-is-Artificial-Intelligence-AI-Technique-Level-of-the-Model-Problem-Spaces-24-2048.jpg)
![Unintentionally Funny Stories
o 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.
o 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.
o 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]](https://image.slidesharecdn.com/slides01-251003092754-a48d2855/75/What-is-Artificial-Intelligence-AI-Technique-Level-of-the-Model-Problem-Spaces-29-2048.jpg)
![Simulated Agents
[Schulman, Moritz, Levine, Jordan, Abbeel, ICLR 2016]](https://image.slidesharecdn.com/slides01-251003092754-a48d2855/75/What-is-Artificial-Intelligence-AI-Technique-Level-of-the-Model-Problem-Spaces-41-2048.jpg)
![AI
Rational
Agents
[decisions]
Robots
[physically
embodied]
Machine Learning
[learning decisions;
sometimes independent]
NLP
Computer
Vision
Human-AI
Interaction](https://crownmelresort.com/image.slidesharecdn.com/slides01-251003092754-a48d2855/75/What-is-Artificial-Intelligence-AI-Technique-Level-of-the-Model-Problem-Spaces-24-2048.jpg)
![Unintentionally Funny Stories
o 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.
o 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.
o 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]](https://crownmelresort.com/image.slidesharecdn.com/slides01-251003092754-a48d2855/75/What-is-Artificial-Intelligence-AI-Technique-Level-of-the-Model-Problem-Spaces-29-2048.jpg)
![Simulated Agents
[Schulman, Moritz, Levine, Jordan, Abbeel, ICLR 2016]](https://crownmelresort.com/image.slidesharecdn.com/slides01-251003092754-a48d2855/75/What-is-Artificial-Intelligence-AI-Technique-Level-of-the-Model-Problem-Spaces-41-2048.jpg)
What is Artificial Intelligence? AI Technique, Level of the Model,Problem Spaces
- 1. CSE 573: Intro toArtificial Intelligence Hanna Hajishirzi slides adapted from Dan Klein, Pieter Abbeel ai.berkeley.edu And Dan Weld, Luke Zettelmoyer
- 2. Website o Website o tentativeschedule o lecture slides o course policies, etc. o https://courses.cs.washington.edu/courses/cse573/21wi/
- 3. Course Staff Hanna Hajishirzihannaneh@cs Mondays Zoom Aida Amini amini91@cs Fridays Zoom Josh Gardner jpgard@cs Tuesday/Thursdays Zoom • Office hours • Schedule on the website • TAs: concepts, projects, homework • Hanna: concepts, high level guidance, homework • Introductions?
- 4. Logistics o Canvas: grades,submitting assignments: o private matters – private messages o if your message is not answered promptly enough, use the staff email: o Ed: Discussion board: ask and answer questions; announcements
- 5. Course Format o ProgrammingAssignments o 4 projects o Python o Autograded o Give you hands-on experience with the algorithms o I expect you to get 100% on projects o Written homeworks o 2 written homeworks o Gives you a more conceptual understanding of the material
- 6. Course Format (continued) oPaper report o Learn how to read and criticize research papers o Final Project: o Encourage to pick a project related to your research o We will provide recommendations for picking projects o There will be a proposal day.
- 7. Prerequisites o Data Structureor Equivalent: CSE 332 o Math: o Basic exposure to probability and data structures o Programming – Familiar with Python o There is a 0th project (P0)
- 8. Textbook o Not required,but for students who want to read more we recommend o Russell & Norvig, AI: A Modern Approach, 3rd Ed. o Warning: Not a course textbook, so our presentation does not necessarily follow the presentation in the book.
- 9. Course Policies o Grade: oYour grade will be: 5% class participation, 5% paper reports, 30% programming assignments, 30% homeworks, and 30% project. o Assignments should be done individually unless otherwise specified. o Late Policy: Six penalty-free late day for the whole quarter; maximum 4 days per assignment. No late day for the final.
- 10. Today oWhat is artificialintelligence (AI)? oWhat can AI do? oWhat is this course?
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- 15. What is AI? Thescience of making machines that: Think like people Act like people Think rationally Act rationally
- 16. Rational Decisions We’ll usethe 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
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- 21. What About theBrain? ▪ Brains (human minds) are very good at making rational decisions, but not perfect ▪ Brains aren’t 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
- 22. Designing Rational Agents oAn agent is an entity that perceives and acts. o A rational agent selects actions that maximize its (expected) utility. o Characteristics of the percepts, environment, and action space dictate techniques for selecting rational actions o This course is about: o General AI techniques for a variety of problem types o Learning to recognize when and how a new problem can be solved with an existing technique Agent ? Sensors Actuators Environment Percepts Actions
- 23. Topics in ThisCourse o Part I: Intelligence from Computation o Fast search o Adversarial and uncertain search o Part II: Reasoning under Uncertainty o Bayes’ nets o Decision theory o Machine learning o Throughout: Applications o Natural language, vision, robotics, games, …
- 24. AI Rational Agents [decisions] Robots [physically embodied] Machine Learning [learning decisions; sometimesindependent] NLP Computer Vision Human-AI Interaction
- 25. Today oCourse overview oWhat isartificial intelligence (AI)? oWhat can AI do? oWhat is this course?
- 26. A (Short) Historyof AI
- 27. A (Short) Historyof AI o 1940-1950: Early days o 1943: McCulloch & Pitts: Boolean circuit model of brain o 1950: Turing's “Computing Machinery and Intelligence” o 1950—70: Excitement: Look, Ma, no hands! o 1950s: Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine o 1956: Dartmouth meeting: “Artificial Intelligence” adopted o 1965: Robinson's complete algorithm for logical reasoning o 1970—90: Knowledge-based approaches o 1969—79: Early development of knowledge-based systems o 1980—88: Expert systems industry booms o 1988—93: Expert systems industry busts: “AI Winter” o 1990—2012: Statistical approaches o Resurgence of probability, focus on uncertainty o General increase in technical depth o Agents and learning systems… “AI Spring”? o 2012— present: Excitement: Look, Ma, no hands! o Big Data, big compute, neural networks o Some re-unification of subfields o AI is being used in industry.
- 28. What Can AIDo? Quiz: Which of the following can be done at present? o Play a decent game of Jeopardy? o Win against any human at chess? o Win against the best humans at Go? o Play a decent game of tennis? o Grab a particular cup and put it on a shelf? o Unload any dishwasher in any home? o Drive safely along the highway? o Drive safely along University Avenue? o Buy a week's worth of groceries on the web? o Buy a week's worth of groceries at QFC? o Discover and prove a new mathematical theorem? o Perform a surgical operation? o Unload a known dishwasher in collaboration with a person? o Translate spoken Chinese into spoken English in real time? o Write an intentionally funny story?
- 29. Unintentionally Funny Stories oOne 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. o 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. o 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]
- 30. Natural Language o Speechtechnologies (e.g. Siri) o Automatic speech recognition (ASR) o Text-to-speech synthesis (TTS) o Dialog systems o Language processing technologies o Question answering o Machine translation o Web search o Text classification, spam filtering, etc…
- 31. Computer Vision • ObjectRecognition • Scene Classification • Image Segmentation • Human Activity Recognition
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- 35. The flower wasso vivid and attractive. Blue flowers are running rampant in my garden. Scenes around the lake on my bike ride. Blue flowers have no scent. Small white flowers have no idea what they are. Spring in a white dress. This horse walking along the road as we drove by. Image captioning: What begins to work We sometimes do well: 1 out of 4 times, machine captions were preferred over the original Flickr captions:
- 36. The couch isdefinitely bigger than it looks in this photo. My cat laying in my duffel bag. A high chair in the trees. Yellow ball suspended in water. But many challenges remain (better examples of when things go awry)
- 38. Game Agents o ClassicMoment: May, '97: Deep Blue vs. Kasparov o First match won against world champion o “Intelligent creative” play o 200 million board positions per second o Humans understood 99.9 of Deep Blue's moves o Can do about the same now with a PC cluster o 1996: Kasparov Beats Deep Blue “I could feel --- I could smell --- a new kind of intelligence across the table.” o 1997: Deep Blue Beats Kasparov “Deep Blue hasn't proven anything.” Text from Bart Selman, image from IBM’s Deep Blue pages
- 39. Game Agents o Reinforcementlearning Pong Enduro Beamrider Q*bert
- 40. 2016 AlphaGo deep RLdefeats Lee Sedol (4-1)
- 41. Simulated Agents [Schulman, Moritz,Levine, Jordan, Abbeel, ICLR 2016]
- 42. Robotics o Robotics o Partmech. eng. o Part AI o Reality much harder than simulations! o Technologies o Vehicles o Rescue o Help in the home o Lots of automation… o In this class: o We ignore mechanical aspects o Methods for planning o Methods for control Images from UC Berkeley, Boston Dynamics, RoboCup, Google
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- 49. Tools for Predictions& Decisions
- 50. Decision Making • AppliedAI in many kinds of automation: • Scheduling, airline routing • Route planning • Medical diagnosis • Web search • Spam classification • Automated help desks • Smarter devices, like cameras • Fraud detection • Product recommendation • … Lots more!
- 51. Today oCourse overview oWhat isartificial intelligence (AI)? oWhat can AI do? oWhat is this course?
- 52. Designing Rational Agents oAn agent is an entity that perceives and acts. o A rational agent selects actions that maximize its (expected) utility. o Characteristics of the percepts, environment, and action space dictate techniques for selecting rational actions o This course is about: o General AI techniques for a variety of problem types o Learning to recognize when and how a new problem can be solved with an existing technique Agent ? Sensors Actuators Environment Percepts Actions
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- 54. Topics in ThisCourse o Part I: Intelligence from Computation o Fast search o Adversarial and uncertain search o Part II: Reasoning under Uncertainty o Bayes’ nets o Decision theory o Machine learning o Throughout: Applications o Natural language, vision, robotics, games, …
- 55. Pac-Man as anAgent Agent ? Sensors Actuators Environment Percepts Actions Pac-Man is a registered trademark of Namco-Bandai Games, used here for educational purposes
- 56. Assignments: Pac-man Originally developedat UC Berkeley: http://www-inst.eecs.berkeley.edu/~cs188/pacman/pacman.html
- 57. This course vs.others Agent Sensors ? Actuators Environment Percepts Actions o CSE 515 – Stat methods o CSE 517 – NLP o CSE 546 – ML o CSE 571 – Robotics o CSE 576,7 – Vision o Advanced RL
- 58. PS1: Search Goal: • HelpPac-man find his way through the maze Techniques: • Search: breadth-first, depth-first, etc. • Heuristic Search: Best-first, A*, etc.
- 59. PS2: Game Playing Goal: •Play Pac-man! Techniques: • Adversarial Search: minimax, alpha-beta, expectimax, etc.
- 60. PS3: Ghostbusters Goal: • HelpPac-man hunt down the ghosts Techniques: • Probabilistic models: HMMS, Bayes Nets • Inference: State estimation and particle filtering
- 61. PS4: Reinforcement Learning Goal: •Help Pac-man learn about the world Techniques: • Planning: MDPs, Value Iterations • Learning: Reinforcement Learning
- 62. Important This Week •Important this week: • Check out canvas--- our main resource for assignments and grades • Check out website – for schedule and slides • Check out Ed – for discussions; we are going to add everyone to Ed • P0: Python tutorial is out • Also important: • Office Hours start next week.