Introduction to AI - Third Lecture

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Introduction to AI - Third Lecture

  1. 1. Introduction to AI – 3rd Lecture1960’s – The Golden Years of AI<br />Wouter Beek<br />me@wouterbeek.com<br />22 September 2010<br />
  2. 2. Overview of the 1960’s<br />Part I<br />
  3. 3. 1964-1966 ELIZA<br />Joseph Weizenbaum @ MIT<br />Natural language processing scripts.<br />Script DOCTOR implementing a Rogerian psychotherapist (still in Emacs).<br />Pattern-matching techniques.<br />Even though intended as a parody, many users took this chatterbot seriously.<br />The first successful Turing test?<br />
  4. 4. 1966-1972, Shakey<br />Shakey: the first mobile robot that was able to reason its own actions.<br />Charles Rosen (and many others), 1966-1972, SRI.<br />Natural language processing: interpretation of the goal.<br />Computer vision: e.g. Hough transformation for feature extraction.<br />Robotics: e.g. visibility graph method for Euclidean shortest path finding.<br />Other techniques: LISP, A* search.<br />
  5. 5.
  6. 6. Late 1960’s, Micro-Worlds<br />Marvin Minsky@ MIT<br />Choose limited problems in idealized settings (as, arguably, is the case in physics).<br />Blocks World: the world consists of a limited number of blocks of different color and shape, and all actions in the world involve arranging these blocks.<br />E.g. SHRDLU, Terry Winograd, 1968-1970<br />
  7. 7. Two SHRDLU examples<br />Person: Pick up a big red block.<br />Computer: OK.<br />Person: Grasp the pyramid.<br />Computer: I don't understand which pyramid you mean.<br />Person (changing their mind):<br />Find a block which is taller than the one you are holding and put it into the box.<br />Computer: By "it", I assume you mean the block which is taller than the one I am holding.<br />Person: What does the box contain?<br />Computer: The blue pyramid and the blue block.<br />Person: What is the pyramid supported by?<br />Computer: The box.<br />Person: How many blocks are not in the box?<br />Computer: Four of them.<br />Person: Is at least one of them narrower than the one which I told you to pick up?<br />Computer: Yes, the red cube.<br />
  8. 8. 1956-1974, Golden Years of AI<br />1957, Sputnik launch<br />U.S. are afraid that the Russians are technologically ahead: huge amounts of Russian scientific articles are translated.<br />U.S. National Research Council starts funding automatic translation research.<br />DARPA funding<br />Based on ambitious claims:<br />“In from three to eight years we will have a machine with the general intelligence of an average human being.” [Marvin Minsky, 1970, Life Magazine]<br />
  9. 9. 1974, first AI winter<br />Too ambitious / too big claims:<br />“The vodka is good, but the meat is rotten.”<br /> “The spirit is willing, but the flesh is weak.”<br />(allusion to Mark 14:38)<br />1966, negative report by an advisory committee, government funding of automatic translation cancelled.<br />Limited knowledge of the outside world:<br />Restricted to micro-worlds (e.g. Blocks World)<br />Restricted to pattern-matching (e.g. ELIZA)<br />Inherent limitations of computability:<br />Intractability, combinatorial explosion (to be discussed next week).<br />Undecidability<br />
  10. 10. Inherent limitations: halting problem<br />Decision problem: any yes-no question on an infinite set of inputs.<br />Halting problem: Given a description of a program and a finite input, decide whether the program finishes running or will run forever.<br />No resource limitations on space (memory) or time (processing power).<br />Example of a program that will finish:<br />writef(‘Hello, world!’).<br />Example of a program that will run forever:<br />lala(X):- lala(X). with query lala(a)<br />Rephrasing the problem: function h is computable:<br />h𝑥,𝑦≔1, 𝑖𝑓 𝑝𝑟𝑜𝑔𝑟𝑎𝑚 𝑥 h𝑎𝑙𝑡𝑠 𝑜𝑛 𝑖𝑛𝑝𝑢𝑡 𝑦0, 𝑖𝑓 𝑝𝑟𝑜𝑔𝑟𝑎𝑚 𝑥 𝑑𝑜𝑒𝑠 𝑛𝑜𝑡 h𝑎𝑙𝑡 𝑜𝑛 𝑖𝑛𝑝𝑢𝑡 𝑦<br /> <br />
  11. 11. Halting problem<br />We do this for any totally computable function f(x,y).<br />Define a partial function g: gx≔ 0, 𝑖𝑓 𝑓𝑥,𝑥=0𝑢𝑛𝑑𝑒𝑓𝑖𝑛𝑒𝑑, 𝑜𝑡h𝑒𝑟𝑤𝑖𝑠𝑒<br />If f is computable, then g is partially computable.<br />The algorithm that computes g is called e.<br />Two possibilities:<br />If g(e)=0, then f(e,e)=0 (definition of g), but then h(e,e)=1 (since e halts on input e).<br />If g(e)=undefined, then f(e,e)≠0 (definition of g), but then h(e,e)=0 (since e does not halt when run on e).<br />So no computable function f can be h, i.e. the halting problem is undecidable.<br /> <br />
  12. 12. Some undecidable problems<br />Halting problem<br />But also: first-order logic (FOL)<br />Used for the blocks world, Logic Theorist, etc.<br />More general: any logical language including the equality predicate and any other binary predicate.<br />Entailment in FOL is semidecidable:<br />For every sentence S: if S is entailed, then there exists an algorithm that says so.<br />For some sentence S: if S is not entailed, then there does not exist an algorithm that says so.<br />
  13. 13. Physical symbol systems<br />Part II<br />
  14. 14. Physical Symbol System (PSS): Ingredients<br />Symbols: physical patterns.<br />Expressions / symbol structures: (certain) sequences of symbols.<br />Processes: functions mapping from and to expressions.<br />
  15. 15. PSS: Designation & interpretation<br />E is an expressions, P is a process, S is a physical symbol system.<br />We call all physical entities objects, e.g. O.<br />Symbols are objects.<br />Expressions are objects, and are collections of objects that adhere to certain strictures.<br />Processes are objects!<br />Machines are objects, and are collections of the foregoing objects.<br />EdesignatesO according to S:<br />Given E, S can affect O, or<br />given E, S can behave according to O.<br />SinterpretsE:<br />E designates P, as in (II).<br />Machines are experimental setups for designating and interpreting symbols.<br />
  16. 16. PSS Hypothesis<br />“A Physical Symbol System has the necessary and sufficient means for general intelligent action.”<br />Necessary: if something is intelligent, then it must be a PSS.<br />Sufficient: if something is a PSS, then it must be intelligent.<br />General intelligent action: the same scope of intelligence as we see in human action.<br />Behavioral or functional interpretation of intelligence (as in Turing1950).<br />
  17. 17. Remember: Church-Turing Thesis<br />Chruch-Turing Thesis: Any computation that is realizable can be realized by a Universal Machine (or Turing Machine, or General Purpose Computer).<br />This thesis is likely since the following three abstractions of computability were developed independently and are yet equivalent:<br />Post productions (Emil Post)<br />Recursive (lambda-)functions (Alonzo Church)<br />Turing Machines (Allan Turing)<br />
  18. 18. PSS: Conceptual History<br />Reasoning as formal symbol manipulation (Frege, Whitehead, Russell, Shannon)<br />Reasoning/information/communication theory abstracts away from content.<br />Think of Shannon’s notion of information entropy and of logical deduction.<br />Automating (1): Computation is a physical process.<br />Stored program concept: programs are represented and operated as data.<br />Think of the tape in a Turing Machine.<br />Interpretation in a PSS.<br />List processing: patterns that have referents<br />Designation in a PSS.<br />
  19. 19. PSS: Evaluating the hypothesis<br />Remember the PSS Hyptohesis: “A Physical Symbol System has the necessary and sufficient means for general intelligent action.”<br />This is not a theorem.<br />The connection between PSS and intelligence cannot be proven.<br />This is an empirical generalization.<br />Whether it is true or false is found out by creating machines and observing their behavior.<br />This makes AI an empirical science (e.g. like physics).<br />AI can corroborate hypotheses, but cannot prove theorems.<br />

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