1. Turing’s Legacy Minds & Machines

2. “I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about 10 9, 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, he British government thought Turing’s homosexuality was a crime, and forced him to go through hormonal treatment. In 1954, age 41, Turing died from eating an apple laced with cyanide; probably a suicide In 1999, Turing was listed as one on the top 100 most important people of the 20 th 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 Interrogator Machine 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 17 th and 20 th 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 UTMTM, ITM(I) Turing proved that there exists a Turing-machine that can simulate any other Turing-machine Description of machine TM and input I The output that Machine TM would Give if I would be its input The Universal Turing Machine

23. Computationalism and the Brain, Part II There are reasons to believe that our brain is ‘programmable’ too: –Neural connections get created and are being erased in the brain –More or less neural resources can be devoted to certain tasks –In case of neural defects, other areas of the brain can take over In short, the brain is highly ‘plastic’, and it may be as a result of this that we learn to become more adept at tasks, or learn to do completely new tasks.

24. 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.

25. 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.

26. Computationalism and the Brain, Part III Again, the brain fits with what we saw: –Lots of simple devices, all organized together to perform lots of simple operations Our brain has 10 11 neurons, and 10 14 neural connections Early views on the brain supposed that neurons firing or not would constitute 0’s and 1’s.

27. 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!

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

29. 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 that is ‘programmable’ and is made of large numbers of simple devices that are ‘wired’ to support complex information- processing capacities

30. Back to the 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

31. 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?”

32. 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?

33. A Definition of Intelligence? Many people have similar criticisms with regard to the Turing Test as a test or definition of intelligence: In particular, the Turing Test is real sloppy: –Who is the interrogator? –How long is the conversation? –What is the conversation about? –How does the interrogator decide?

34. Not a Definition Turing himself clearly did not intend to propose a definition of intelligence. In his paper Turing readily acknowledges that one could have intelligent beings not being able to pass the test simply by not having a human- like intellect: –“May not machines carry out something which ought to be described as thinking but which is very different from what a man does? This objection is a very strong one, but at least we can say that if, nevertheless, a machine can be constructed to play the imitation game satisfactorily, we need not be troubled by this objection”

35. A Sufficient Condition for Intelligence? Many commentators interpret Turing’s statement as saying that if a machine passes the Turing Test, then it is intelligent, i.e. that passing the Turing Test is a sufficient condition for intelligence (since intelligence is a necessary condition to pass it), but not a necessary one (and hence it is not a definition). In logic: –We have: P  I –But not: I  P

36. Same Sloppiness … And A Question As a sufficient condition for being intelligent, the Turing Test suffers from some of the same problems as before: –such a criterion would still amount to a subjective judgment based on imprecisely defined behavioral criteria. But why would Turing (not exactly known for his sloppiness!) propose such a sloppy test?

37. 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.

38. 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?

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. Machines Can’t Make Mistakes, Be Creative, Learn “Can’t make mistakes”: A weird objection to being intelligent … And machine do make mistakes! –Not at the level of the program –But at the level of interpretation And they are creative And do learn

41. Machines Can Only Do What They’re told To Do The mistake in this objection is that while this statement is true from the perspective of the underlying program, it is not clear that a machine couldn’t do anything new or creative when looked at from a higher level. Indeed, look at Deep Blue: Deep Blue beats every human in chess, but that would be impossible if Deep Blue couldn’t do any better than any of its programmers. Similarly, pending any mechanical glitches, machines can’t fail but follow some program, and thus indeed not make any errors from that point of view. However, it can still make mistakes as the result of following that program! (…. no quantum randomness needed!)

42. Turing’s Argument It looks like Turing makes the following argument to support his view that computers can pass the Turing Test: –Passing the Turing Test requires nothing more than some kind of information processing ability –Computers can have this information processing ability –Therefore, computers can pass the Turing Test

43. The Earlier Argument for Computationalism But compare this argument to the earlier argument for Computationalism: –Cognition requires nothing more than some kind of information processing ability –Computers can have this information processing ability –Therefore, computers can be cognitive

44. The Real Puzzle So why not change the argument into: –Intelligence requires nothing more than some kind of information processing ability, –Computers can have this information processing ability –Therefore, computers can be intelligent So why didn’t Turing make this very argument? Why bring in the game to make the case for machine intelligence?

45. A Second Puzzle Also, 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?

46. The Super-Simplified Turing Test InterrogatorMachine

47. 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.

48. The Simplified Turing Test InterrogatorMachine or Human

49. 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, we are left with the earlier puzzle: why bring up any kind of test (and a sloppy one at that!) at all?

50. My Answer I propose that the convoluted set-up wasn’t merely a practical consideration to eliminate bias in some strange game, but rather the point of his article, which 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.

51. ‘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.

52. The Turing ‘Test’ as Harmful! Moreover, I believe that seeing 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! So, I think we really should no longer refer to the Turing Test as the Turing ‘Test’!!

53. 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

54. How to Read Turing’s Paper So what did Turing really mean? Ultimately, this is an issue of history, and not an issue we should be concerned about. Better questions to ask are: What, if anything, can we learn from Turing’s paper? What would be a fruitful interpretation of his paper? Well, there are many interesting parts of the paper, especially in Turing’s responses to the ‘Contrary Views’. I also believe that seeing Turing’s paper as laying out a genuine test is harmful, not helpful. Instead, I believe a fruitful reading of his paper is to see the Turing ‘Test’ as a statement about the use of the word ‘intelligence’.

55. Pluto and Planets Asking how many planets there are in our solar system seems to be a factual matter: –We believe there is a straightforward fact of the matter to this issue. If I say: “There are X planets in our solar system” then this statement is either true or false. –How many planets there are is an empirical issue: observations will tell us how many there are However, as the case of Pluto demonstrated, things aren’t that easy. This issue isn’t just an empirical issue, but also one of interpretation. Maybe the same is true for machine intelligence!

56. Artificial Flight and Artificial Intelligence Imagine going back 100 years when the Wright Brothers had their first flight. We can imagine people say: “Well, but that’s not real flight. There is no flapping of the wings!” But over time, we realized that it is, from the standpoint of using concepts that help us think and make sense of the world around us, a good idea to consider airplanes as really flying. Again, maybe the same is true for intelligence!

57. The original question, “Can machines think?”, I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. -Alan Turing (1950)