UNIT 1.pdf
- 4. • ML and AI to build and optimize systems and also provide AI technology with new data inputs for interpretation. • Machine learning is about extracting knowledge from data. • It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. • AI and ML has become ubiquitous in everyday life -commercial applications, data-driven research Artificial Intelligence
- 5. ML and AI to build and optimize systems and also provide AI technology with new data inputs for interpretation. Artificial Intelligence
- 6. What is AI? 6 Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach
- 7. Acting humanly: Turing Test • Turing (1950) "Computing machinery and intelligence": • "Can machines think?" à "Can machines behave intelligently?" • Operational test for intelligent behavior: the Imitation Game The computer would need to possess the following capabilities: • natural language processing to enable it to communicate successfully in English (or some other human language); • knowledge representation to store information provided before or during the interrogation; • automated reasoning to use the stored information 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, the computer will need • computer vision to perceive objects • robotics to move them about. 7 Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach
- 8. Thinking humanly: cognitive modeling Determining how humans think • through introspection—trying to catch our own thoughts as they go by • through psychological experiments Express the theory as a computer program • program's input/output and timing behavior matches human behavior 8 Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach
- 9. Thinking rationally: "laws of thought" • • • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization Direct line through mathematics and philosophy to modern AI Problems: 1. Not all intelligent behavior is mediated by logical deliberation 2. What is the purpose of thinking? What thoughts should I have? 9 Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach
- 10. History of AI • AI has roots in a number of scientific disciplines – computer science and engineering (hardware and software) – philosophy (rules of reasoning) – mathematics (logic, algorithms, optimization) – cognitive science and psychology (modeling high human/animal thinking) – neural science (model low level human/animal brain activity) – linguistics level • The birth ofAI (1943 – 1956) – McCulloch and Pitts (1943): simplified mathematical model of neurons (resting/firing states) can realize all propositional logic primitives (can compute all Turing computable functions) – Alan Turing: Turing machine and Turing test (1950) – Claude Shannon: information theory; possibility of chess playing computers – Boole, Aristotle, Euclid (logics, syllogisms) 10 Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach
- 11. • Early enthusiasm (1952 – 1969) – 1956 Dartmouth conference John McCarthy (Lisp); Marvin Minsky (first neural network machine); – Emphasis on intelligent general problem solving Lisp (AI programming language); Resolution by John Robinson (basis for automatic theorem proving); heuristic search (A*, AO*, game tree search) • Emphasis on knowledge (1966 – 1974) – domain specific knowledge is the key to overcome existing difficulties – knowledge representation (KR) paradigms – declarative vs. procedural representation History of AI 11 Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach
- 12. • Knowledge-based systems (1969 – 1999) – DENDRAL: the first knowledge intensive system (determining 3D structures of complex chemical compounds) – MYCIN: first rule-based expert system (containing 450 rules for diagnosing blood infectious diseases) EMYCIN: an ES shell – PROSPECTOR: first knowledge-based system that made significant profit (geological ES for mineral deposits) • AI became an industry (1980 – 1989) – wide applications in various domains – commercially available tools – AI winter • Current trends (1990 – present) – more realistic goals – more practical (application oriented) – distributedAI and intelligent software agents – resurgence of natural computation - neural networks and emergence of genetic algorithms – many applications – dominance of machine learning (big apps) History of AI 12 Stuart J. Russell, Peter Norwig , Artificial Intelligence –A Modern approach
- 14. • more powerful and more useful computers • new and improved interfaces • solving new problems • better handling of information • relieves information overload • conversion of information into knowledge Advantages of Artificial Intelligence 14
- 15. The Disadvantages • increased costs • difficulty with software development - slow and expensive • few experienced programmers • few practical products have reached the market as yet. 15
- 16. • AI deals with a large spectrum of Problems • Applications spread across the domains, from medical to manufacturing with their own complexities • AI Deals with • Various Day-to-day Problem • Different identification and authentication problems (in security) • Classification problems in Decision-making systems • Interdependent and cross-domain problems (Such as Cyber-Physical • Systems) • The problems faced by AI is hard to resolve and also computationally AI Technique 14 Parag Kulkarni, Prachi Joshi, Artificial Intelligence –Building Intelliegent Systems
- 17. AI Technique • Intelligence requires knowledge (less desirable properties) – voluminous – hard to characterize accurately – constantly changing – differ from data by being organized in a way that corresponds to the ways it will be used 17 Parag Kulkarni, Prachi Joshi, Artificial Intelligence –Building Intelliegent Systems
- 18. Main reasons for AI advances • Computing power (GPU and Cloud computing) • Big data - Internet and sensors - Large datasets • Deep learning algorithms - Software - Improved techniques - Toolboxes