Turing’s Legacy

1. Turing’s Legacy Minds & Machines Alan Turing

2. “I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after 5 minutes of questioning” -Alan Turing (1950)

3. Alan Turing • Alan Turing was a British mathematician who was most famous for his work in theoretical computer science • During World War II, Turing helped break German codes using mechanical computers • In 1952, the British government considered Turing’s homosexuality to be a crime, and forced him to go through hormonal treatment. • In 1954, age 41, Turing died from eating an apple laced with cyanide; most likely suicide • In 1999, Turing was listed as one on the top 100 most important people of the 20th century • On September 10, 2009, the British government apologized for their treatment of Alan Turing.

4. Turing’s Legacy • Turing’s legacy consists of 2 parts: • Turing Machines (1936) • Turing Test (1950)

5. Turing Test

6. “I propose to consider the question, 'Can machines think?' This should begin with definitions of the meaning of the terms 'machine 'and 'think'. … [But] Instead of attempting such a definition I shall replace the question by another... The new form of the problem can be described in terms of a game which we call the 'imitation game'.“ -Alan Turing, “Computing Machinery and Intelligence”, 1950

7. The Imitation Game Machine Interrogator Human

8. Some Initial Observations on the Turing Test • The Turing Test attributes intelligence purely on verbal interactions. Is that ok? • Well, physical characteristics (size, weight, agility, etc) don’t seem to be relevant as far as intelligence goes, so that seems right. • However, shouldn’t we have to open up the computer program and see how it works to make this kind of determination? • Then again, do we ever open up other human beings to determine whether they are intelligent? • Hmm, maybe Turing has a point.

9. The Turing Test: Can Machines Think? Premise 1: Machines can pass the Turing Test • Premise 2: Anything that passes the Turing Test is intelligent • Conclusion: Machines can be intelligent

10. Can Machines pass the Turing Test?

11. Computationalism • Cognition can be defined in terms of information-processing: • Perception is taking in information • Memory/Beliefs/Knowledge is storing information • Reasoning is inferring new information • Learning is updating information • Planning is using information to make decisions • Etc. • Information-processing can be done through computations • Therefore, cognition is computation.

12. Computationalism and the Brain • Notice that the argument on the previous slide is a purely conceptual one in that it is not based on any empirical evidence. • Indeed, it predicts the existence of some kind of brain (computer) in any cognitive being. • So, the fact that we have a brain, which is in many ways a computer, can be seen as empirical confirmation of the view of computationalism.

13. Computationalism and the Brain, Part I • The brain fits with computationalism: • The brain is unlike any other organ; the heart, lungs, liver, etc. all do something very much physical (collect, filter, pump, etc.) • The brain, however, is quite different: Its function seems to be to take in signals, and send out signals, in communication with the nervous system. • Thus, the brain seems to be an information-processor: a computer of sorts. • Indeed, we know that the nature of the mind changes when the brain changes: thus, maybe: • brain = ‘hardware’ • mind = ‘software’

15. Computers A ‘computer’ is something that computes, i.e. something that performs a computation. Between the 17th and 20th century, a ‘computer’ was understood to be a human being; humans who computed things! It was only by automating (mechanizing) this process, that we obtained ‘computers’ as we now think of them.

16. Computations A computation is a symbol-manipulation algorithm. The symbols represent something Hence, the computation is about that something: “we compute something”

17. Example: Long Division

18. Components for Computation In a famous 1936 paper, Turing argued that all computations can be reduced to the following basic components: One symbol string of arbitrary size An ability to move along this symbol string An ability to read and write symbols We now call this: a Turing-machine

19. Turing Machines Demo

20. Computable Functions • We can use a Turing-machine to compute the sum, and product, of any two numbers. • These functions are therefore Turing-computable • Lots of other functions are Turing-computable • E.g. all functions needed to run Microsoft Word are Turing-computable (i.e. you can run Microsoft Word on a Turing-machine)

21. The Church-Turing Thesis • If a computer of type X can compute a function f, we say that f is X-computable • The Church-Turing Thesis: • No matter what type of computer X you have: All functions that are X-computable are Turing-computable. • In short: Turing-machines can compute anything that is computable.

22. Universal Turing Machines Turing proved that there exists a Turing-machine that can simulate any other Turing-machine TM, I UTM TM(I) The output that machine TM would give if I would be its input Description of machine TM and input I The Universal Turing Machine

23. Programmable Computers • Turing’s insight led to the notion of universally programmable computer: • A single computer (the UTM) that can act like any other computer by being given a description of that computer (a computer program), and act like that computer by following the instructions of that program. • Thus: • Hardware (UTM) • Software (Computer Program) • Now: Operating System functions like UTM

24. A Note on Hardware and Software • Often proponents of Computationalism (and Materialism) make the following analogy: • Brain = Hardware • Mind = Software • This is actually not a good analogy to make: • Software specifies how the hardware is to behave • But nothing is telling the brain how to behave. • There is no program, no set of instructions being read and executed by the brain. • Software is at the level of step-by-step instructions • Materialists want to see minds as an abstract high-level perspective on the functioning brain

25. 0’s and 1’s • Turing showed how all computation can be done using a limited number of simple processes manipulating a small number of symbols. • In fact, it turns out you only need 2 symbols! • You do need lots of these symbols, and you do need to perform lots of these simple operations. • But this is exactly how the modern ‘digital computer’ does things. That is, at the ‘machine level’, it’s all simple manipulations of 0’s and 1’s.

26. Physical Dichotomies • The 0’s and 1’s are just abstractions though; they need to be physically implemented. • Thus, you need some kind of physical dichotomy, e.g. hole in punch card or not, voltage high or low, quantum spin up or down, penny on piece of toilet paper or not, etc.

27. Computationalism and the Brain, Part II • Again, the brain fits with what we saw: • Lots of simple devices, all organized together to perform lots of simple operations • Our brain has 1011 neurons, and 1014 neural connections • Early views on the brain supposed that neurons firing or not would constitute 0’s and 1’s.

28. Causal Topology • A physical system implements a computational system if and only if that system implements a certain causal topology. • This topology is highly abstract. As long as you retain the functionality of the parts, and the connections between the parts, you can: • Move parts • Stretch parts • Replace parts • This is why there can be mechanical computers, electronic computers, DNA computers, optical computers, quantum computers, etc!

29. Computationalism and the Brain, Part III • So are our brains organic, carbon-based, ‘meat-computers’?! • Again, it seems to fit: • Implements a complex causal topology where the only thing that seems to matter is how the neurons are connected.

30. Summary • Two independent arguments for computationalism: • One conceptual: cognition is information-processing, and that’s exactly what computers do • One empirical: the mind seems dependent on the brain, where the brain seems to be: • an information-processing device, • made of large numbers of simple devices, • that implement a complex causal topology to support various information-processing capacities

31. Back to the Turing Test: Can Machines Think? Premise 1: Machines can pass the Turing Test • Premise 2: Anything that passes the Turing Test is intelligent • Conclusion: Machines can be intelligent

32. Is Anything that Passes the Turing Test Intelligent?

33. Cheap Tricks? Eliza • A psychotherapist program developed by Joseph Weizenbaum in 1966. • Eliza used a number of simple strategies: • Keywords and pre-canned responses • “Perhaps I could learn to get along with my mother” -> “Can you tell me more about your family?” • Parroting • “My boyfriend made me come here” -> “Your boyfriend made you come here?” • Highly general questions • “In what way?” • “Can you give a specific example?”

34. Eliza and the Turing Test • Many people conversing with Eliza had no idea that they weren’t talking to a human. • So did Eliza pass the Turing Test? • (Or is it just easy being a psychotherapist?!) • Eliza wasn’t really tested in the format that Turing proposed. • Still, it is interesting that humans were quick to attribute human-level intelligence to such a simple program. • Maybe in a real Turing Test a relatively simple computer program can ‘trick’ the interrogator as well?

35. The Test is Sloppy • The Turing Test seems to be a real sloppy way to get at intelligence or at least it is severely lacking in detail: • Who is the interrogator? • How long is the conversation? • What is the conversation about? • How does the interrogator decide? • What are the metrics used?

36. The Loebner Competition • Modern day version of the Turing Test • Multiple judges rank-order multiple humans and multiple computer programs from ‘most likely to be human’ to ‘least likely to be human’. • Loebner has promised $100,000 for the first computer program to be ‘indistinguishable from a human’. • Thus far, Loebner is still a rich man: occasionally a judge will rank a program above a human, but on the whole the judges systematically rank the humans above the computer programs.

37. An OK Test After All? • Apparently it is quite difficult to pass the test! • When put to the real test, interrogators can see through superficial trickery • So it seems we could say that if something does pass the test, then there is at least a good chance for it to be intelligent. • In fact, if we are turning this into an inductive argument anyway, the sloppiness of the test isn’t a huge concern either: we can now simply adjust our confidence in our claim in accordance to the nature of the conversation. • So is this maybe what Turing was saying?

38. “A Computer is Merely Crunching Numbers” • As we saw, a computer is ‘crunching’ symbols, not numbers. • OK, but the objection still stands: does the computer know what those symbols even mean? -> The Chinese Room Objection • Response: Just because the UTM (OS) is ‘merely’ crunching symbols without understanding what they are doesn’t mean that a working computer doesn’t understand these symbols.

39. “Contrary Views” • In his paper Turing goes over a list of “Contrary Views on the Main Question”: • Machines: • can’t make mistakes • can’t be creative • can’t learn • can’t do other than what they’re told

40. A Puzzle • That’s weird: if Turing proposed the Turing Test as some kind of practical test for machine intelligence, you would think that Turing would address objections of the previous kind, i.e. that maybe something can pass the test without being intelligent. • Instead, it seems like Turing addresses objections to the claim that machines can pass the test. • Why?

41. Another Question • Why the strange set-up of the Turing-Test? Why did Turing ‘pit’ a machine against a human in some kind of contest? Why not have the interrogator simply interact with a machine and judge whether or not the machine is intelligent based on those interactions?

42. The Super-Simplified Turing Test Interrogator Machine

43. Answer: Bias • The mere knowledge that we are dealing with a machine will bias our judgment as to whether that machine can think or not, as we may bring certain preconceptions about machines to the table. • Moreover, knowing that we are dealing with a machine will most likely lead us to raise the bar for intelligence: it can’t write a sonnet? Ha, I knew it! • By shielding the interrogator from the interrogated, such a bias and bar-raising is eliminated in the Turing-Test.

44. The Simplified Turing Test Interrogator Machine or Human

45. Level the Playing Field • Since we know we might be dealing with a machine, we still raise the bar for the entity on the other side being intelligent. • Through his set-up of the test, Turing made sure that the bar for being intelligent wouldn’t be raised any higher for machines than we do for fellow humans. • Still, this leaves the earlier puzzle.

46. My Answer • I propose that the convoluted set-up wasn’t merely a practical consideration to eliminate bias in some strange game, but rather to confront us with our the very prejudices that, at Turing’s time, many people had against machine intelligence. • Thus, the ‘Turing Test’ isn’t at all meant like practical test, but rather a thought experiment meant to make us think differently about machines and machine intelligence. • Indeed, the ‘Objections’ that Turing addresses aren’t so much objections to machines being able to pass the Turing Test, but rather objections that go straight to the issue of machine intelligence.

47. Language • Another way of looking at the Turing Test is that if we put a label ‘intelligent being’ on other human beings based on their behavior then, just to be fair, we should do the same for machines, whether we are correct or precise in any such attributions or not. • In other words, Turing’s point was that we don’t have a precise definition of ‘intelligence’, but that we do have a fuzzy concept of it, and that our use of slapping this label onto things (human or otherwise) should at least be consistent.

48. ‘Imitation Game’ vs ‘Turing Test’ • In other words, I think it is likely that Turing never intended to propose any kind of test for machine intelligence (let alone propose a definition!). • Interesting fact: In his original article Turing uses the word ‘pass’ or ‘passing’ 0 times, ‘test’ 4 times, and ‘game’ 37 times.

49. The Turing ‘Test’ as Harmful! • Moreover, I believe that regarding Turing’s contribution as laying out a test is harmful. • The harm is that we have been thinking about the goal of AI in these terms, and that has been, and still is, detrimental to the field of AI. • E.g. In “Essentials of Artificial Intelligence”, Ginsberg defines AI as “the enterprise of constructing a physical symbol system that can reliably pass the Turing Test” • But trying to pass the test encourages building cheap tricks to convince the interrogator, which is exactly what we have seen with Eliza, Parry, and pretty much any entry in the Loebner competition. • This kind of work has advanced the field of AI, and our understanding of intelligence … exactly zilch!

50. Grand Challenges • Maybe the Turing Test (and the Loebner competition) is a kind of Grand Challenge? • Landing people on moon • Chess (Deep Blue) • Urban Challenge • Jeopardy (Watson) • But at this point in time, I feel that trying to create human-level intelligence in a computer is a ridiculously-grand challenge, and hence a ridiculous Grand Challenge