Artificial Intelligence Lecture Notes PDF

Artificial Intelligence Prepared by: Dr. Sara Sweidan Supplementary Textbook Book Title: Artificial Intelligence, A modern approach Authors: Stuart Russell, Peter Norvig....

Artificial Intelligence Prepared by: Dr. Sara Sweidan Supplementary Textbook Book Title: Artificial Intelligence, A modern approach Authors: Stuart Russell, Peter Norvig. Publisher: Pearson. Edition: Fourth edition (2021) Today’s class Course overview Introduction What is AI? History of AI Foundation of AI Application of AI Intelligent Agents Big Questions 1 Can machines think? 2 And if so, how? 3 And if not, why not? 4 And what does this say about human beings? Artificial intelligence  Why study of AI? AI makes computers more useful Intelligent computer would have a huge impact on civilization AI cited as “field I would most like to be in” by scientists in all fields Computer is a good figure for talking and thinking about intelligence  What is artificial intelligence? Artificial Intelligence (AI) is a branch of Science that deals with helping machines find solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence and applying them as algorithms in a computer-friendly way. Some consider intelligence a property of internal thought processes and reasoning, while others focus on intelligent behavior, an external characterization.  History of AI 1. The inception of artificial intelligence (1943–1956) 2. Early enthusiasm, great expectations (1952–1969) 3. A dose of reality (1966–1973) 4. Expert systems (1969–1986) 5. The return of neural networks (1986–present) 6. Probabilistic reasoning and machine learning (1987– present) 7. Big data (2001–present) 8. Deep learning (2011–present) What Can AI Do? 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 table 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 Telegraph Avenue? o Buy a week's worth of groceries on the web? o Buy a week's worth of groceries at Berkeley Bowl? o Discover and prove a new mathematical theorem? o Perform a surgical operation? o Unload a know dishwasher in collaboration with a person? o Translate spoken Chinese into spoken English in real time? o Write an intentionally funny story?  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]  What About the Brain? ▪ 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 (data) and simulation (computation) are key to decision- making Course Syllabus o Part I: Making Decisions o Fast search / planning 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, …search/planning  Recap.. WhatisArtificialInteligence? Four Main perspectives that have been followed, each by different people with different methods. the Cognitive the Turing Test Modelling approach approach Thinking Acting Humanly Humanly Thinking Acting Rationally Rationally the Rational Agent the Laws-of-Thought approach approach 15 AI Perspectives From these two dimensions—human vs. rational and thought vs. behavior— there are four possible combinations 1. Acting humanly: The Turing test approach 2. Thinking humanly: The cognitive modeling approach 3. Thinking rationally: The “laws of thought” approach 4. Acting rationally: The rational agent approach  AI Perspectives: acting humanly The Turing test, proposed by Alan Turing (1950), was designed as a thought experiment that would sidestep the philosophical vagueness of the question “Can a machine think?” For Machines to think, we would need: natural language processing to communicate successfully in a human language; knowledge representation to store what it knows or hears; automated reasoning to answer questions and to draw new conclusions; machine learning to adapt to new circumstances and to detect and extrapolate patterns. To pass the total Turing test, a robot will need computer vision and speech recognition to perceive the world; robotics to manipulate objects and move about.  AI Perspectives: thinking humanly We can learn about human thought in three ways: introspection—trying to catch our own thoughts as they go by; psychological experiments—observing a person in action; brain imaging—observing the brain in action. Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as a computer program.  AI Perspectives: thinking rationally The Greek philosopher Aristotle was one of the first to attempt to codify “right thinking.” Their study initiated the field of logic. Logicians in the 19th century developed precise notations for statements about objects in the world and their relations. By 1965, programs could, in principle, solve any solvable problem described in logical notation. The so-called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems The theory of probability fills this gap, allowing rigorous reasoning with uncertain information.  AI Perspectives: acting rationally Agents: An agent is just something that acts. Computer agents are expected to do more: operate autonomously, perceive their environment, persist over a prolonged time period, adapt to change, and create and pursue goals. Rational Agents: A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome. AI has focused on the study and construction of agents that do the right thing. What counts as the right thing is defined by the objective that we provide to the agent. This general paradigm is so pervasive that we might call it the standard model. It prevails not only in AI, but also in control theory, where a controller minimizes a cost function; in operations research, where a policy maximizes a sum of rewards; in statistics, where a decision rule minimizes a loss function; and in economics, where a decision maker maximizes utility or some measure of social welfare. AI Perspectives: acting rationally Act to achieve goals, given set of beliefs Rational behavior is doing the “right thing” Thing which expects to maximize goal achievement What is the definition of artificial intelligence? Thinking humanly: to say program thinks like a Thinking rational: the laws of thought human, we must know humans think. 1- syllogism: ex; the dog is animal, animal Three ways to learn human though: has 4 legs, then dog has 4 legs. 1-introspection: try to catch our thoughts as they go 2-logicist: identify certain condition to by. perform specific actions. 2-psychological experiments: observing a person in 3-probability: allowing rigorous reasoning action. with uncertain information. 3-brain imaging: observing the brain in action. Perceive, reason, and act. Acting humanly: how to make computers do Acting rational: the learning agent things at which, at the moment, Which means acts so as to achieve the Needed capabilities: best expected outcome or when there is Natural language processing: to enable communication knowledge representation: to store what it knows uncertainty. automated reasoning: to use the stored information to This branch of computer science is answer questions. concerned with the automation of machine learning, adapt to new circumstances intelligent behavior. computer vision to perceive objects, robotics to manipulate objects. Rational Decisions We’ll 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 The foundation of AI AI has roots in a number of scientific disciplines –philosophy (rules of reasoning) –mathematics (logic, algorithms, optimization) –economics (decision theory, operations research) –neuroscience (model low level human/animal brain activity) –cognitive science and psychology (modeling high level human/animal thinking) –computer science and engineering (hardware and software) –Control theory and cybernetics (robotics) 1. Philosophy Can formal rules be used to draw valid conclusions? How does the mind arise from a physical brain? Where does knowledge come from? How does knowledge lead to action? 2. Mathematics What are the formal rules to draw valid conclusions? What can be computed? How do we reason with uncertain information? 3. Economics How should we make decisions in accordance with our preferences? How should we do this when others may not go along? How should we do this when the payoff may be far in the future? 4. Neuroscience How do brains process information? 5. Psychology How do humans and animals think and act? 6. Computer Engineering How can we build an efficient computer? 7. Control Theory How can artifacts operate under their own control? 8. Linguistics How does language relate to thought? The State of the Art 1. ROBOTIC VEHICLES 2. AUTONOMOUS PLANNING AND SCHEDULING 3. MACHINE TRANSLATION 4. SPEECH RECOGNITION 5. RECOMMENDATIONS 6. GAME PLAYING 7. IMAGE UNDERSTANDING 8. MEDICINE 9. CLIMATE SCIENCE Risks and Benefits of AI 1. LETHAL AUTONOMOUS WEAPONS 2. SURVEILLANCE AND PERSUASION 3. BIASED DECISION MAKING 4. IMPACT ON EMPLOYMENT 5. SAFETY-CRITICAL APPLICATIONS 6. CYBERSECURITY Next Class Intelligent Agents: Introduction of agents, Structure of Intelligent agents, Properties of Intelligent Agents Configuration of Agents, PEAS description of Agents, PAGE Types of Agents: Simple Reflexive, Model-Based, Goal Based, Utility-Based, Learning Agent Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi- observable, Single-Agent, Multi-Agent  Thank you Sara Sweidan PhD, Artificial Intelligence Assistant Professor Faculty of Computers & AI Benha University, Egypt [email protected]