Philosophical Foundations of AI

1. Ajay Garg 05005004 Satadru Biswas 05005021 Veeranna 05005023 Praveen Lakhotia 05D05010 Arun Karthikeyan 05D05020 Philosophical Foundations of AI

2. Outline • Turing Test – Satadru • Weak AI – Arun • Strong AI – Veeranna • AI Complete – Ajay • Ethics of AI – Praveen

3. Motivation • When ever we start something new we always start debating over the need for it, its feasibility. • Similar thing happened when AI was born in the early 1950’s. • Philosophers debated about very fundamental and important questions like – “Can machines think?”, “Is AI possible?” etc.

4. Motivation (contd…) • Turing then rephrased the question “Can machines think?” into a test, which became famous as The Turing Test. • Several Variants developed over the years.

5. Turing Test

6. The Imitation Game • Turing described a simple party game which involves three players. Player A is a man, Player B is a woman and Player C is a interrogator • The set up is such that Player C is unable to see either of A or B and can only communicate with them using written media

7. The Imitation Game (contd…) • By asking questions of player A and player B, player C tries to determine which of the two is the man, and which of the two is the woman • A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator

8. The Original Imitation Game Test • Turing proposed that player A be replaced with a computer • The success of the computer is determined by comparing the outcome of the game when player A is a computer against the outcome when player A is a man

9. The Original Imitation Game Test (contd…) • Or to put it in Turing’s words: “the interrogator decides wrongly as often when the game is played [with the computer] as he does when the game is played between a man and a woman, then it can be argued that the computer is intelligent”

10. Standard Turing Test • As with the Original Imitation Game Test, the role of player A is performed by a computer • The difference is that now the role of player B is to be performed by a man, rather than by a woman • In this version both player A (the computer) and player B are trying to trick the interrogator into making an incorrect decision

11. Imitation Game vs. Standard Turing Test • A man can fail the OIG Test, but it is argued that this is a virtue of a test of intelligence if failure indicates a lack of resourcefulness • It is argued that the OIG Test requires the resourcefulness associated with intelligence and not merely "simulation of human conversational behavior"

12. Strengths of the test • The power of the Turing test derives from the fact that it is possible to talk about anything • Turing wrote "the question and answer method seems to be suitable for introducing almost any one of the fields of human endeavor that we wish to include.“ • In order to pass a well designed Turing test, the machine would have to use natural language, to reason, to have knowledge and to learn

13. Weaknesses of the test • It only tests if the subject resembles a human being • It will fail to test for intelligence under two circumstances: • It tests for many behaviors that we may not consider intelligent, such as the susceptibility to insults or the temptation to lie.

14. Weaknesses of the test (contd…) 2. It fails to capture the general properties of intelligence, such as the ability to solve difficult problems or come up with original insights. Image Courtesy: Wikipedia Commons

15. WEAK AI CAN MACHINES ACT INTELLIGENTLY? - ARUN

16. Weak AI • The assertion that machines could possibly act intelligently is called “weak AI” hypothesis by philosophers • Can machines act intelligently? • Can machines think?

17. Objections against AI • The argument from disability • The mathematical objection • The argument from informality

18. The argument from disability • “A machine can never do X” • X according to Turing: being kind, learning from experience, doing something new, differentiating between right and wrong.

19. The argument from disability contd... • Some of the have been achieved over the years. Ex. Machines today do learn from experience. • Fact: Automated programs are used to grade GMAT essay questions. • May be over the years machines can do the rest of “X's”.

20. The Mathematical Objection • Machines are formal systems limited by incompleteness theorem. Ex. They cannot establish the truth of Godel sentence. • Humans have no such limitation. • “Humans are superior to machines”

21. The Mathematical Objection (contd...)‏ • Problems with the claim: • Godels Theorem applies only to formal systems powerful enough to do arithmetic. • Applies to Turing Machines and not to computers. • Turing Machines have infinite memory but not computers.

22. The Mathematical Objection (contd...)‏ • Truth of some sentence should be established by all agents. • Eg: Lucas cannot consistently assert that this sentence is true. • Even if computers have limitations on what they can prove, there is no evidence that humans can prove those results.

23. The argument from informality • Human behavior is far too complex to be captured by any simple set of rules • Computers can do no more than follow a set of rules. • So they cannot generate behavior as intelligent as that of humans. • The inability to capture everything in a set of logical rules is called the “qualification problem” in AI.

24. The argument from informality (contd...)‏ • No one has any idea of incorporating background knowledge into learning process. • This claim has been proved to be wrong. • Ex. Learning algorithms use background knowledge today.

25. The argument from informality (contd...)‏ • Learning requires prior identification of relevant inputs and correct outputs. • This claim has been proved to be wrong. • Ex. Unsupervised learning has been accomplished today.

26. The argument from informality (contd...)‏ • Brain can direct its sensors to seek information and process it according to current situation. • Research is being done over this field and partial success has been achieved.

27. Strong AI – can machines really think ?

28. Strong AI • Machine can be said to have posses Strong AI if it could do whatever human brain could do in every possible way. Should posses casual powers of brain. • Have consciousness, self awareness, understanding, feel emotions, dream, think etc., • No body cares about Strong AI. • Pass the Turing test doesn’t imply actually thinking, but still might be simulating thinking.

29. Argument from consciousness • Jefferson’s Lister Oration for 1949, “Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain – that is not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants”. • Should have Consciousness • Phenomenology: machine has to actually feel emotions • Intentionality: whether the beliefs, desires and intensions are “of” or “about” something in real world.

30. Polite convention • No direct evidence of other people mental states. • Lets accept that everyone thinks.

31. What is mind ? • Artificial urea is urea, artificial insemination is insemination, artificial simulation of chess game is a chess game, artificial simulation of addition is addition but artificial monalisa is not monalisa, artificial simulation of storm is not storm, artificial scotch is not scotch. • Artificial mind ? • Depends upon definition of mental states. • Theory of functionalism. • Biological naturalism theory.

32. Theory of functionalism • Mental state (beliefs, desires, being in pain)is a condition which is between input and output.

33. Theory of functionalism • What is S1 ? • Being in S1 = Being an x such that P Q[If x is in P and gets a ‘1’ input, then it goes into Q and emits "Odd"; if x is in Q and gets a ‘1’ input it goes into P and emits "Even"& x is in P] (Note: read P as There is a property P.)}. • Functional State Identity Theory (FSIT) would identify pain (or, more naturally, the property of having a pain or being in pain) with the second-order relational property. • Being in pain = Being an x such that P Q[sitting on a tack causes P & P causes both Q and emitting ‘ouch’ & x is in P] • The nature of a mental state is just like the nature of an automaton state.

34. Biological naturalism theory • Mental state is a result of neural activity. • John Searle 1980 • 1) all mental phenomena from pain, tickles, and itches to the most abstruse thoughts are caused by lower-level neurobiological processes in the brain. 2) mental phenomena are higher level features of the brain. • brains and only brains can cause consciousness. • Consciousness is ontologically subjective in the sense that it only existswhen experienced by a human or animal subject.

35. Mind body problem • Dualist theory – soul is different from body. René Descartes‘. Ghost in a machine !!. • Mind architecture. • Monist theory – mind and body are same. • Only thing that is proven to exists is matter. • Searle – “brain cause mind”. • Free will – materialist deal with it.

36. Brain in a vat

37. Brain in a vat • Hilary Putnam first presented the argument that we cannot be brains in a vat. • A term refers to an object only if there is an appropriate causal connection between that term and the object. (CC) • 1) Assume we are brains in a vat . 2) If we are brains in a vat, then “brain” does not refer to brain, and “vat” does not refer to vat (via CC) . 3) If “brain in a vat” does not refer to brains in a vat, then “we are brains in a vat” is false .

38. Brain in a vat 4) Thus, if we are brains in a vat, then the sentence “We are brains in a vat” is false (1,2,3). • Mental state that “I need a pizza” are they same in both worlds ? • Wide content – knows everything, from outside. • Narrow content – within same world. • Qualia – difference between human beings and zombies. • Matrix - 1999 , Wachowski brothers.

39. Brain prosthesis experiment • Replace each neuron by electronic devices slowly one by one. • What happens to consciousness ? • Functionalist – consciousness remains • Biological naturalist – consciousness vanishes. • Brain computer interface (BCI)

40. Chinese room problem

41. Chinese room problem • Against strong AI. • Searle’s axioms: • 1) Minds have mental contents; specifically, they have semantic contents. 2) Computer programs are entirely defined by their formal, or syntactical, structure. 3) Syntax is not sufficient for semantics (against functionalism). 4)Brains cause minds.

42. AI COMPLETE -AJAY GARG

43. AI Complete • the most difficult problems are informally known as AI-complete. • implying that the difficulty of these computational problems is equivalent to solving the central artificial intelligence problem—making computers as intelligent as people. • The term was coined by Fanya Montalvo by analogy with NP-Complete in complexity theory.

44. AI Complete (contd...)‏ • To call a problem AI-complete reflects an attitude that it won't be solved by a simple algorithm.

45. Natural Language Understanding • The AI subarea of Natural Language is essentially the overlap of AI and computational Linguistics. • The goal of the area is to form a computational understanding of how people learn and use their native languages.

46. Natural Language Understanding (condt...)‏ • Consider a straight-forward, limited and specific task: machine translation. • To translate accurately, a machine must be able to understand the text.

47. Natural Language Understanding (contd...)‏ • It must be able to follow the author's argument, so it must have some ability to reason. • It must have extensive world knowledge so that it knows what is being discussed. • E.g. We gave the monkeys the bananas because they were hungry and We gave the monkeys the bananas because they were over-ripe.

48. Natural Language Understanding (contd...)‏ • It must also model the authors' goals, intentions, and emotional states to accurately reproduce them in a new language. • E.g. "I never said she stole my money" - Someone else said it, but I didn't. • E.g. “I never said she stole my money" - I said she stole someone else's money.

49. Natural Language Understanding (contd...)‏ • In short, the machine is required to have wide variety of human intellectual skills. • So this problem is believed to be AI-complete.

50. Vision • Vision is interpreting visual images that fall on the human retina or the camera lens. • The actual scene being looked at could be 2-dimensional such as a printed page of text or 3-dimensional such as the world about us.