Lecture 1.pdf

Fundamentals of AI SENG 4082  Chapter 1 Introduction to AI Outline: What is Artificial Intelligence (AI)? AI Vocabulary Branches of AI AI applications The State of the Art 3 What is Artificial Intelligence (AI)? According to the fath...

Fundamentals of AI SENG 4082  Chapter 1 Introduction to AI Outline: What is Artificial Intelligence (AI)? AI Vocabulary Branches of AI AI applications The State of the Art 3 What is Artificial Intelligence (AI)? According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs” Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think AI is accomplished by studying how human brain thinks and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems What is Artificial Intelligence (AI) ? (CONTD…) Two dimensions Some have defined intelligence in terms of dependability to human performance, while others prefer an abstract, formal definition of intelligence called rationality—loosely speaking, doing the “right thing” The subject matter Rationality itself also varies: some consider intelligence to be a property of internal thought processes and reasoning, while others focus on intelligent behavior, an external characterization From these two dimensions—human vs. rational and thought vs. behavior—there are four approaches What is Artificial Intelligence (AI) ? (CONTD…) Four Approaches Acting humanly: The Turing test approach Turing test The Turing test, proposed by Alan Turing (1950): A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer We note that programming a computer to pass a rigorously applied test provides plenty to work on What is Artificial Intelligence (AI) ? (CONTD…) Four Approaches (CONTD…) Acting humanly: The Turing test approach (CONTD…) The computer would need the following capabilities: 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 What is Artificial Intelligence (AI) ? (CONTD…) Four Approaches (CONTD…) Acting humanly: The Turing test approach (CONTD…) Other researchers have proposed a total Turing test, which requires interaction with objects and people in the real world 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 What is Artificial Intelligence (AI) ? (CONTD…) Four Approaches (CONTD…) Thinking humanly: The cognitive modeling approach To say that a program thinks like a human, we must know how humans think 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 What is Artificial Intelligence (AI) ? (CONTD…) Four Approaches (CONTD…) Thinking rationally: The “laws of thought” approach The Greek philosopher Aristotle was one of the first to attempt to codify “right thinking”—that is, irrefutable reasoning processes His syllogisms provided patterns for argument structures that always yielded correct conclusions when given correct premises The canonical example starts with Socrates is a man and all men are mortal and concludes that Socrates is mortal These laws of thought were supposed to govern the operation of the mind; their study initiated the field called logic What is Artificial Intelligence (AI) ? (CONTD…) Four Approaches (CONTD…) Acting rationally: The rational agent approach A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome In the “laws of thought” approach to AI, the emphasis was on correct inferences Making correct inferences is sometimes part of being a rational agent, because one way to act rationally is to deduce that a given action is best and then to act on that conclusion On the other hand, there are ways of acting rationally that cannot be said to involve inference For example, recoiling from a hot stove is a reflex action that is usually more successful than a slower action taken after careful deliberation AI Vocabulary Intelligence relates to tasks involving higher mental processes, e.g. creativity, solving problems, pattern recognition, classification, learning, induction, deduction, building analogies, optimization, language processing, knowledge and many more is the computational part of the ability to achieve goals Intelligent behavior is depicted by perceiving one’s environment, acting in complex environments, learning and understanding from experience, reasoning to solve problems and discover hidden knowledge, applying knowledge successfully in new situations, thinking abstractly, using analogies, communicating with others and more AI Vocabulary Science based goals of AI pertain to developing concepts, mechanisms and understanding biological intelligent behaviour The emphasis is on understanding intelligent behaviour Engineering based goals of AI relate to developing concepts, theory and practice of building intelligent machines The emphasis is on system building AI Techniques depict how we represent, manipulate and reason with knowledge in order to solve problems Knowledge is a collection of ‘facts’ To manipulate these facts by a program, a suitable representation is required A good representation facilitates problem solving AI Vocabulary Learning means that programs learn from what facts or behaviour can represent denotes changes in the systems that are adaptive In other words, it enables the system to do the same task(s) more efficiently next time AI Vocabulary Problems of AI: Intelligence does not imply perfect understanding; every intelligent being has limited perception, memory and computation Many points on the spectrum of intelligence versus cost are viable, from insects to humans AI seeks to understand the computations required from intelligent behaviour and to produce computer systems that exhibit intelligence Aspects of intelligence studied by AI include perception, communicational using human languages, reasoning, planning, learning and memory Branches of AI Logical AI — In general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language The program decides what to do by inferring that certain actions are appropriate for achieving its goals Search — Artificial Intelligence programs often examine large numbers of possibilities – for example, moves in a chess game and inferences by a theorem proving program Discoveries are frequently made about how to do this more efficiently in various domains Branches of AI Pattern Recognition — When a program makes observations of some kind, it is often planned to compare what it sees with a pattern For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face More complex patterns are like a natural language text, a chess position or in the history of some event These more complex patterns require quite different methods than do the simple patterns that have been studied the most Representation — Usually languages of mathematical logic are used to represent the facts about the world Branches of AI Inference — Others can be inferred from some facts Mathematical logical deduction is sufficient for some purposes, but new methods of non-monotonic inference have been added to the logic since the 1970s The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default But the conclusion can be withdrawn if there is evidence to the divergent For example, when we hear of a bird, we infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin Branches of AI Planning — Planning starts with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal From these, planning programs generate a strategy for achieving the goal In the most common cases, the strategy is just a sequence of actions Heuristics — A heuristic is a way of trying to discover something or an idea embedded in a program The term is used variously in AI Heuristic functions are used in some approaches to search or to measure how far a node in a search tree seems to be from a goal Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, and may be more useful AI applications 1. AI Application in E-Commerce Personalized Shopping Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your customers These recommendations are made in accordance with their browsing history, preference, and interests AI-Powered Assistants Virtual shopping assistants and chatbots help improve the user experience while shopping online Natural Language Processing is used to make the conversation sound as human and personal as possible Moreover, these assistants can have real-time engagement with your customers AI applications 1. AI Application in E-Commerce Fraud Prevention Credit card frauds and fake reviews are two of the most significant issues that E-Commerce companies deal with By considering the usage patterns, AI can help reduce the possibility of credit card fraud taking place Many customers prefer to buy a product or service based on customer reviews AI can help identify and handle fake reviews AI applications 2. Applications of Artificial Intelligence in Education Administrative Tasks Automated to Aid Educators Artificial Intelligence can help educators with non-educational tasks like task-related duties like facilitating and automating personalized messages to students, back-office tasks like grading paperwork, arranging and facilitating parent and guardian interactions, routine issue feedback facilitating, managing enrollment, courses, and HR-related topics Creating Smart Content Personalized Learning AI applications 3. Applications of Artificial Intelligence in Healthcare Artificial Intelligence finds diverse applications in the healthcare sector AI applications are used in healthcare to build sophisticated machines that can detect diseases and identify cancer cells Artificial Intelligence can help analyze chronic conditions with lab and other medical data to ensure early diagnosis The State of the Art Robotic vehicles: In 2018, Waymo test vehicles passed the landmark of 10 million miles driven on public roads without a serious accident, with the human driver stepping in to take over control only once every 6,000 miles Soon after, the company began offering a commercial robotic taxi service Legged locomotion: Atlas, a humanoid robot, not only walks on uneven terrain but jumps onto boxes and does backflips (Ackerman and Guizzo, 2016) The State of the Art Autonomous planning and scheduling: A hundred million miles from Earth, NASA’s Remote Agent program became the first on- board autonomous planning program to control the scheduling of operations for a spacecraft (Jonsson et al., 2000) Every day, ride hailing companies such as Uber and mapping services such as Google Maps provide driving directions for hundreds of millions of users, quickly plotting an optimal route taking into account current and predicted future traffic conditions The State of the Art Machine translation: Online machine translation systems now enable the reading of documents in over 100 languages, including the native languages of over 99% of humans, and render hundreds of billions of words per day for hundreds of millions of users While not perfect, they are generally adequate for understanding For closely related languages with a great deal of training data (such as French and English) translations within a narrow domain are close to the level of a human (Wu et al., 2016b) The State of the Art Speech recognition: In 2017, Microsoft showed that its Conversational Speech Recognition System had reached a word error rate of 5.1%, matching human performance on the Switchboard task, which involves transcribing telephone conversations (Xiong et al., 2017) About a third of computer interaction worldwide is now done by voice rather than keyboard; Skype provides real-time speech-to-speech translation in ten languages Alexa, Siri, Cortana, and Google offer assistants that can answer questions and carry out tasks for the user; for example the Google Duplex service uses speech recognition and speech synthesis to make restaurant reservations for users, carrying out a fluent conversation on their behalf  Chapter 2 Intelligent Agents Outline: The Agent and the Environment Good Behavior: The Concept of Rationality The Nature of Environments The Structure of Agents 29 Agents and Environments This word agent used to mean “something that acts” An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators This simple idea is illustrated in Figure 2.1 30 Agents and Environments An agent could be a robot a web shopping program a factory a traffic control system… The environment could be everything—the entire universe! In practice it is just that part of the universe whose state we care about when designing this agent— the part that affects what the agent perceives and that is affected by the agent’s actions 31 Agents and Environments We begin by examining agents, environments, and the coupling between them The observation that some agents behave better than others leads naturally to the idea of a rational agent— one that behaves as well as possible How well an agent can behave depends on the nature of the environment; some environments are more difficult than others 32 Agents and Environments We use the term percept to refer to the content an agent’s sensors are perceiving An agent’s percept sequence is the complete history of everything the agent has ever perceived. In general, an agent’s choice of action at any given instant can depend on its built-in knowledge and on the entire percept sequence observed to date Mathematically speaking, we say that an agent’s behavior is described by the agent function that maps any given percept sequence to an action 33 Agents and Environments The agent function for an artificial agent will be implemented by an agent program The agent function is an abstract mathematical description; the agent program is a concrete implementation, running within some physical system 34 Good Behavior: The Concept of Rationality A rational agent is one that does the right thing Performance measures AI has one notion of the “right thing,” called consequentialism: we evaluate an agent’s behavior by its consequences When an agent is plunked down in an environment, it generates a sequence of actions according to the percepts it receives This sequence of actions causes the environment to go through a sequence of states If the sequence is desirable, then the agent has performed well This notion of desirability is captured by a performance measure that evaluates any given sequence of environment states 35 Good Behavior: The Concept of Rationality A rational agent is one that does the right thing Rationality What is rational at any given time depends on four things: The performance measure that defines the criterion of success The agent’s prior knowledge of the environment The actions that the agent can perform The agent’s percept sequence to date 36 Good Behavior: The Concept of Rationality A rational agent is one that does the right thing Rationality This leads to a definition of a rational agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has To the extent that an agent relies on the prior knowledge of its designer rather than on its own percepts and learning processes, we say that the agent lacks autonomy A rational agent should be autonomous—it should learn what it can to compensate for partial or incorrect prior knowledge 37 The Nature of Environments 38