Artificial Intelligence

1. Artificial Intelligence

2. What is Artificial Intelligence? • Artificial intelligence (AI) is a branch of computer science, which is to make computer intelligent (or as intelligent as human beings). • Is the above a good definition of AI?

3. Discussion What Does ‘Intelligent’ Mean? • Calculation? • Thinking? Logical deduction? Under uncertainty? • Memorizing? Integrated memory? • Using language? • Visual capability? – Pattern recognition and understanding a picture? • Aesthetical sense? • Sentimental subtleties? – love, sympathy, passion, romance, joy, anger, envy, hatred, curiosity, craziness ... ?

4. AI Is Yet Defined Strictly • Strict definition of AI relies on strict definition of ‘intelligence’. • AI can be defined by the issues it is concerned with. • AI is a study whose major goals include its own definition.

5. Discussion A Unique Gift to Human • Intelligence, which allows acquiring knowledge, is a unique gift to human. • Knowledge has enlightened humanity. • Does intellectual ambition lead to disaster, as told in the legends of Prometheus and Eva?

6. Mental vs. Physical • We know less about a mental process than a physical process. • No one has affirmed a fundamental difference between mental states and physical actions. • More evidences show that mind and body are not fundamentally different. • Mental process can be achieved by a physical system as brain or computer.

7. Logic • Logic is the basis of intellectual thinking. • Logic is used to infer something to be ‘true’ when given other things that are known ‘true’. • Syllogism (Modus Ponens): • Given a truth: If A then B • Given a fact: A • Inferring the result: B.

8. Formal Logic • Conjunction: AND, . • (P Q) is true only if both P and Q are true • Disjunction: OR, . • (P Q) is true if at least one of P and Q is true. • Negation: NOT, . • ( P) is true if P is false. • Implication: If...then..., . • (A B) is true if A and B are true, or A is false.

9. A Knowledge Base • Knowledge can be represented by ‘if...then...’ rules. • A collection of knowledge rules forms a knowledge base. • Computer’s thinking is applying Modus Ponens on a knowledge base.

10. A Knowledge Base • Rule 1: If it is a mammal and it is a carnivore and it has black stripes, then it is a tiger. • Rule 2: If it is a mammal and it has black stripes and it is an ungulate, then it is a zebra. • Rule 3: If it has hair then it is a mammal. • Rule 4: If it gives milk then it is a mammal. • Rule 5: If it is a mammal and it has hooves then it is an ungulate. • Rule 6: If it eats meat then it is a carnivore.

11. Turing Test • It gives us an objective notion of intelligence, with reference to a human. • It evaluates intelligence by ‘result’ rather than by ‘process’. • It focuses on intelligence on thinking and written communication.

12. Criticisms of Turing Test • It does not test perceptual skill and manual dexterity that are important components of human intelligence. • It uses human as a standard for intelligence, which is full of flaws per se. To past the Turing test, ‘artificial stupidity’ is required. • It does not test the mechanism of a intelligence process.

13. Lady Lovelace’s Objection • By Ada Lovelacs: “Computers can only do as they are told and consequently cannot perform original (hence, intelligent) actions.” • Achievements of computers have disproved it: • Expert systems can do something unanticipated by their designers; • Deep Blue beat International Chess Master.

14. Informality of Behavior • “It is impossible to create a set of rules that will tell an individual exactly what to do under every possible circumstance.” • It reflects the flexibility existing in human intelligence.

15. Neural Network Model • It is a model to achieve machine intelligence, which is alternative to the logic based approach. • It mimics the structure of biological brain and emphasizes the ability to adapt to the world by modifying the relationships between individual neurons.

16. Intelligence in Global Behaviors • Intelligence is reflected by the collective behaviors of large numbers of very simple interacting, semi-autonomous individuals, or agents. • Intelligence emerges when simple individual persons (or neural cells, or computer ‘agents’) interact.

17. Our Humble Successes • Machine learning – just started; • Natural language – modest; • Representing knowledge, • Commonsense knowledge – just started; • Reasoning or thinking – limited quality and flexibility.

18. AI Areas • Although no one has given an exact definition of AI, people have consensus on areas of AI. • The problems that can be solved by pure calculation or algorithmic process are not AI problems.

19. Game Playing • Intelligence in dealing with ambiguities and complexities in game/chess playing is relatively easy to be represented on a computer. • Heuristic • A heuristic is a practical problem-solving method / algorithm, which works effectively and efficiently on some instances, but may fail or be slow on some other instances.

20. Automated Reasoning • It enables a computer to think logically by applying formal logic. • Propositional logic and predicate logic are basis of formal logic. • Inference is a process of searching in the knowledge base. • Inference and Inference-guiding.

21. Expert System • An intelligent computer system that possesses deep but narrow knowledge and acts as a human expert in a domain.

22. Natural Language • Translation from one language to another. • Recording oral speech into paper document. • Reading a paper document. • Understanding the meaning of a speech. • Face-to-face conversation.

23. Modeling Human Performance • To simulate the process of human internal mental process. • Purposes: • To reach the level of human intelligence; • To formulating and testing theories of human cognition for psychology, psychoanalysis, and philosophy.

24. Autonomous Planning and Robotics • A robot (not the ‘robot’ on an assembly line) is an autonomous computer agent that is able to accomplish some missions independently. • Necessary capabilities of a robot: • Planning based on incomplete information; • Implementing the plan; • Correcting the plan in execution.

25. AI Languages • AI need its own computer languages to express the knowledge and thinking process. • LISP and Prolog are examples.

26. Machine Learning • Machine learning refers to the ability of computer to accumulate knowledge by itself. • From where to learn: • Experience • Analogy • Examples • Being taught or told to do.

27. Neural Network and Emergent Computation • To realize intelligence by mimicking the structure of neurons in human brain. • Each computational unit computes some function of its inputs and passes the result along to the connected units in network. Instead of using explicit symbols and operations, the knowledge emerges out of the entire network of neural connections.

28. Is Human Mind a ‘Machine’ • Machine is something that is (1) composed of parts that follows the known physical laws, and (2) able to accomplish certain tasks/assignments. • A human brain gives rise to thoughts, feelings, and consciousness, and is composed of cells that follow the physical laws. • So, a brain is a machine. Mind is machine either?

29. Implications of “Human Mind Is a Machine” • If human mind is a machine, then: • Human mind can be decomposed, analyzed, and eventually understood. • Human mind can be duplicated or ‘manufactured’.

30. Comments AI vs. AS • Artificial intelligence (AI) is to make a machine do things at which people are better. • Artificial stupidity (AS) is to make a machine do things at which people are worse. • AI + AS is to make a machine more like a person.

31. Intelligence = On/Off Switches? • Most feel that thinking or true intelligence / consciousness is not an automatic technique based on on/off switches. • Recent AI achievements showed at least ‘some’ intelligence can be realized through on/off switches.

32. Comments A Psychological Myth • A magic is not magical once we know its trick. • When we figure out how an expert thinks and operates, what once seemed very intelligent somehow seems less so. • The level of intelligence is inversely related to the extent you understand.

33. Can We Understand Ourselves? • It could be a fate that our brains are too weak to understand themselves, which may be completely comprehensible to more intelligent beings. • The Gödel’s incompleteness theorem: • The system of formal arithmetic is not complete. That is, using the theorems of this system only cannot prove or generate all the theorems of this system.

34. Kursweil Three Processes of Intelligence • Learning • Self-accumulating knowledge. • Reasoning • Inferring logical implications from known facts. • Symbolic reasoning • Abstract reasoning, and applying abstract symbols to individual cases.

35. Evolution vs. Entropy • Entropy is a theorem in physics that the elements in a closed system will eventually go complete random (disordered). • Evolution has created very ordered intelligence. • How has evolution beat entropy so far?

36. Class End Comments • We have covered computers and AI, and their impact on us at present and in the future. • We may have posed more questions than we have solved. - That is a difference between this course and other courses. • The world is complex, marvelous, and mystical. We, as highly educated humans, ought to be sophisticated. • Sophistication is not chaos or confusion. We have our basis values and logic, as the progress I have seen in the recent assignments. • What you have learned from this course: • Knowledge about computers and AI; • Knowledge about impacts of AI on society and us; • Realized that the complication of this world; • Thinking complex problem in a sophisticated way; • Thinking logically and in depth. • Organize your thoughts into writing; • Answer the questions to the point.