Week 2: Artificial Intelligence Introduction - PDF

Topic 1:Introduction to Artificial Intelligence Term 2-ARTI 106 Computer Track 2024-2025  Learning outcomes The main learning objectives of this topic are: ❑ Define and give examples of the basic concepts of AI. ❑ Identify the goals of AI. ❑ Identify the approaches to AI. ❑ Identify pr...

Topic 1:Introduction to Artificial Intelligence Term 2-ARTI 106 Computer Track 2024-2025  Learning outcomes The main learning objectives of this topic are: ❑ Define and give examples of the basic concepts of AI. ❑ Identify the goals of AI. ❑ Identify the approaches to AI. ❑ Identify problems where AI techniques are applicable. Outlines ❑What is the AI? ❑Approaches to AI ❑A short history of AI. ❑Applications of AI. ❑Main topics in AI. ❑Challenges and Risks of AI.  What is intelligence? ❑ Intelligence is the ability to learn about, to learn from, to understand about, and interact with one’s environment. ❑ Intelligence is the ability of understanding. ❑ Intelligence is not to make no mistakes but quickly to understand how to make them good. (German Poet) Characteristics of Intelligence (1) Ability to Communicate (2) Creativity (3) Internal Knowledge (4) Ability to Learn (5) Perceive World Knowledge (6) Goal-Directed Behavior (7) Self Awareness A Hierarchical Model of Intelligence Wisdom + Vision Knowledge Information + Experience Data + Context What Is Artificial Intelligence? Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of algorithms and computer programs that can perform tasks which typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. What Intelligent System Should do? Intelligent systems: ❑ Should have the ability to automatically perform tasks that normally require a human expert. ❑ Should have more autonomy: less requirement for human intervention or monitoring. ❑ Should have flexibility in dealing with variability in the environment in an appropriate manner. ❑ Should be easier to use: they are able to understand what the user wants from limited instructions. ❑ Can improve their performance by learning from experience. Test your knowledge… ❑ Which of the following is NOT a characteristic of intelligent systems? A. The ability to perform tasks requiring human expertise B. Dependence on constant human monitoring C. Flexibility to handle variability in the environment D. Capability to improve performance through learning ❑ What enables intelligent systems to be easier to use? A. Advanced hardware specifications B. Ability to understand user intent from limited instructions C. Regular updates and upgrades D. Continuous human supervision What Is Artificial Intelligence? ❑ AI is the reproduction of the methods or results of human reasoning or intuition. ❑ Artificial Intelligence is a branch of Computer Science concerned with the automation of intelligent behavior. ❑ Artificial Intelligence is concerned with the design of intelligence in an artificial device. ❑ AI is a field of computer science that simulates human performance to make a computer reasons in a manner similar to humans. What Is Artificial Intelligence? How to Achieve AI? THOUGHT Systems that think Systems that think like humans rationally BEHAVIOUR Systems that act Systems that act like humans rationally HUMAN RATIONAL  Acting Humanly (Turing Test) Can a machine be truly “intelligent”? : Turing’s Test The test is composed of : ❑ An interrogator (a person who will ask questions) ❑ A computer (intelligent machine !!) ❑ A person who will answer to questions ❑ A curtain (separator) Example: You enter a room which has a computer terminal. You have a fixed period of time to type what you want into the terminal and study the replies. At the other end of the line is either a human being or a computer system. If it is a computer system, and at the end of the period you cannot reliably determine whether it is a system or a human, then the system is deemed to be intelligent. Acting Humanly (Turing Test) Turing’s Test…Example Acting Humanly (Turing Test) Turing’s Test…Example Acting Humanly (Turing Test) Turing’s Test…Example Acting Humanly (Turing Test) Turing’s Test…Example Acting Humanly (Turing Test) Turing’s Test…Example Acting Humanly (Turing Test) Turing’s Test…Example Systems that act like humans What would a computer need to pass the Turing test? ❑ The Turing Test approach : ❑ a human questioner cannot tell if there is a computer or a human answering his question, via teletype (remote communication). ❑ The computer must behave intelligently. ❑ Intelligent behavior means achieve human-level performance in all cognitive tasks. ❑ Passing Turing test requires the computer to have the following capabilities: – Natural language processing for communication with human – Knowledge representation to store information effectively & efficiently – Automated reasoning to retrieve & answer questions using the stored information – Machine learning to adapt to new circumstances  Test your knowledge… ❑ The Turing Test involves a scenario where a human questioner cannot tell if they are interacting with a _______ or a _______ through remote communication, such as a _______. ❑ To pass the Turing Test, a computer must demonstrate _______ behavior, achieving human-level performance in all _______ tasks. ❑ This requires capabilities like _______ for communication, _______ to store information, _______ to retrieve and answer questions, and _______ to adapt to new circumstances. Systems that act like humans What would a computer need to pass the Turing test? However, the Turing test excludes direct physical contact between the machine and the tester. The Total Turing Test incorporates perceptual abilities and the ability of the person being questioned to manipulate objects. It brings forward two more requirements: – Computer vision :to perceive objects (seeing) – Robotics :to move objects (acting) What Is Artificial Intelligence? THOUGHT Systems that think Systems that think like humans rationally BEHAVIOUR Systems that act Systems that act like humans rationally HUMAN RATIONAL Systems that think like humans: cognitive modeling ❑ Cognitive Science approach: Combination of computer models from AI and experimental techniques from psychology to construct precise and testable theories of human mind. ❑ This approach try to get “inside” minds. ❑ This involves trying to understand human thought and an effort to build machines that emulate human. ❑ This approach requires to know how humans think. Problems ❖ Humans don’t behave rationally. ❖ The reverse engineering is very hard to do. ❖ The brain’s “hardware” is very different to a computer program. How to Achieve AI? THOUGHT Systems that think Systems that think like humans rationally BEHAVIOUR Systems that act Systems that act like humans rationally HUMAN RATIONAL Systems that think ‘rationally’: laws of thought approach ❑ Humans are not always ‘rational’. ❑ This approach is related to LOGIC, suggesting that logical rules form the mental mind of humans. ❑ This approach represent facts about the world via logic. ❖ Syllogism: Example: Socrates is a man %Fact All men are mortal % Rule if X is a Man, then X is Mortal Therefore Socrates is mortal % Inference ❖ Logic: ▪ Precise notation for statements about all kinds of objects in the world and the relation among them. ❖ Drawbacks ▪ Not easy to take informal knowledge and state it in formal terms required by logical notation, particularly when the hen problem is less than 100% certain. ▪ Big difference between solving problem in principal and solving it in practice. How to Achieve AI? THOUGHT Systems that think Systems that think like humans rationally BEHAVIOUR Systems that act Systems that act like humans rationally HUMAN RATIONAL Systems that act rationally: “Rational agent” ❑A Rational behavior involves making the right decisions or taking the appropriate actions which is expected to maximize goal’s achievement, given the available information. ❑“Acting” rationally means acting to achieve one's goals. ❑The Rational Agent: is an agent that acts to achieve the best outcome or best expected outcome if there is uncertainty.  Relations to Other Fields ❑ Philosophy ❑ Logic, methods of reasoning and rationality. ❑ Mathematics ❑ Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability, probability. ❑ Economics ❑ utility, decision theory (decide under uncertainty) ❑ Neuroscience ❑ neurons as information processing units. ❑ Psychology/Cognitive Science ❑ how do people behave, perceive, process information, represent knowledge. ❑ Computer engineering ❑ building fast computers ❑ Control theory ❑ design systems that maximize an objective function over time ❑ Linguistics ❑ knowledge representation, grammar  A (Short) History of AI 1940-1950: Early days ❑ 1943: McCulloch & Pitts: Boolean circuit model of brain ❑ 1950: Turing's “Computing Machinery and Intelligence” 1950—70: Excitement: Look, Ma, no hands! ❑ 1950s: Early AI programs: chess, checkers program, theorem proving ❑ 1956: Dartmouth meeting: “Artificial Intelligence” adopted ❑ 1965: Robinson's complete algorithm for logical reasoning 1970—90: Knowledge-based approaches ❑ 1969—79: Early development of knowledge-based systems ❑ 1980—88: Expert systems industry booms ❑ 1988—93: Expert systems industry busts: “AI Winter” 1990—2012: Statistical approaches + subfield expertise ❑ Resurgence of probability, focus on uncertainty ❑ General increase in technical depth ❑ Agents and learning systems… “AI Spring”? 2012—___: Excitement: Look, Ma, no hands again? ❑ Big data, big compute, neural networks ❑ Some re-unification of sub-fields ❑ AI used in many industries Applications of AI ❑ Language translation services (Google) ❑ News aggregation and summarization (Google) ❑ Speech recognition (Nuance) ❑ Song recognition (Shazam) ❑ Face recognition (Recognizr) ❑ Image recognition (Google Goggles) ❑ Question answering (Apple Siri, IBM Watson) ❑ Chess playing (IBM Deep Blue) ❑ 3D scene modeling from images (Microsoft Photosynth) ❑ Driverless cars (Google, Tesla, etc.) ❑ Chatbot (Amy A.I.) ❑ Augmented reality travel guide (mTrip) State of AI Systems in Practice ❑ Email communications Email Filters Smart Replies ❑ Social media Chatbots Facebook Proactive Detection ❑ Web searching Google Predictive Searches Youtube's Algorithm ❑ Stores and services Maps and Directions Product Recommendations - Amazon , Netflix Commercial Airline Flights Banking Digital voice assistants  The main topics in AI ❑ Knowledge representation ❑ Reasoning and automatic proving ❑ Search and optimization ❑ Problem solving ❑ Learning and understanding ❑ Pattern classification / recognition ❑ Planning ❑ Natural language processing. ❑ Expert Systems ❑ Interacting with the Environment (e.g. Vision, Speech recognition, Robotics) Challenges and Risks of AI Ethical concerns Job Displacement The use of AI raises ethical concerns such as AI has the potential to automate many privacy, bias, and accountability. As AI jobs, which could lead to significant job systems are designed to learn from data, displacement. It is important to consider they can maintain existing biases and the impact of AI on the workforce and discrimination present in the data. develop strategies to support workers Additionally, there is a lack of accountability who may be displaced by automation. for AI systems, as it can be difficult to determine who is responsible for their actions.  Challenges and Risks of AI Security Risks Biased decision making As AI systems become more Careless or deliberate misuse of sophisticated, they also become more machine learning algorithms for tasks vulnerable to cyber-attacks. There is such as evaluating parole and loan a risk that malicious actors could use applications can result in decisions AI systems to launch attacks or to that are biased by race, gender, or gain access to sensitive information. other protected categories. Often, It is important to develop robust the data themselves reflect pervasive security measures to protect against bias in society. these risks.  Questions 1) Define intelligence. 2) Why Would You Study Artificial Intelligence? 3) What are the different approaches in defining artificial intelligence? 4) Suppose you design a machine to pass the Turing test, what are the capabilities such a machine must have? 5) Design ten questions to pose to a man/machine that is taking the Turing test. 6) Do you think that building an artificially intelligent computer automatically shed light on the nature of natural intelligence? 7) List 5 tasks that you will like a computer to be able to do within the next 5 years. 8) List 5 tasks that computers are unlikely to be able to do in the next 10 years. Thank you for you attention