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AI and Automation

AI and Automation. Media and Culture Lecture 9 John Lee. Introduction: what is “AI”?. Two major areas where “AI” is talked about: engineering/automation studying, perhaps emulating, human cognition In practice, these do not often overlap (maybe they do in this video !)

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AI and Automation

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  1. AI and Automation Media and Culture Lecture 9 John Lee

  2. Introduction: what is “AI”? • Two major areas where “AI” is talked about: • engineering/automation • studying, perhaps emulating, human cognition • In practice, these do not often overlap • (maybe they do in this video!) • but at a theoretical level they share many issues and approaches … • Crucial general issue: how do we bring formal techniques to bear in an informal world? • “In logic, mathematics, and computer science, a formal system is a formal grammar used for modelling purposes. Formalization is the act of creating a formal system, in an attempt to capture the essential features of a real-world or conceptual system in formal language.” (Wikipedia, 26.10.05)

  3. An unusual case …

  4. Examples of formal systems • Arithmetic (formal theory and calculus of numbers) • Logic (formal theory and calculus of propositions) • Natural language grammars • Chomsky and all that … • Shape grammars • (http://www.mit.edu/~tknight/IJDC/) • Music grammars • (Lehrdahl, F. and R. Jackendoff. 1983. A Generative Theory of Tonal Music, Cambridge,
Mass: MIT Press) • Databases • Knowledge bases • Meteorological models (fluid dynamics) • Economic models

  5. Formality and formalisation • Central issue in AI and automation (but also much else): • Computer is an entirely formal system, but world (and people) seem not to be • How to go from informal world to formal system, derive some result, and then get back again without losing anything important?

  6. (What is important?) • What should be preserved? • truth? • meaning? • Use of any formal system inevitably involves a number of translation steps: Formal statement Calculation (Inference) Informal statement Reinterpretation Result

  7. Basic logical principles • Analysis of natural language (e.g. English) argument: • translation into logical form, application of rules, then translation back … • Compare analysis of arithmetical calculation: • Suppose 82 students get 175 pages of notes each … • Form is: result = A x B = 82 x 175 … • Calculation gives: result = 14,350 • So we need (e.g.) to budget for 14,350 copies Formal statement Calculation (Inference) Informal statement Reinterpretation Result

  8. … basic principles (continued) • A simple argument (application of modus ponens): • If the switch is down, (then) the light is on; the switch is down … <Informal> • If P then Q; P …<(semi-) formal translation> P –> QP Q <formal inference> • … therefore Q • Sothe light is on<reinterpretation> Formal statement Calculation (Inference) Informal statement Reinterpretation Result

  9. COMPUTATION • What is it? • Why is it important?

  10. Turing's machine • The first properly worked out theory of computation … • an abstract formal machine • head and tape: • head can read, erase, write symbols, and move tape one square left or right • head is defined by a few rules e.g.: if the symbol below head is ‘1’, erase it, write a ‘0’, and move one square left • input for problem is posed by writing it on the tape at start time • output from the problem is on the tape at ‘halt’ time • given machine defines a mathematical function (set of pairs of input/output)

  11. Simple example … • an adding machine — two numbers in ‘tally notation’ separated by blank • machine finds blank, ‘moves 1s across blank’ until finished • infinite (or extendable) machines — can always add more tape Head I I I I I I I I I I I I I I

  12. Universal machines • a Universal machine can mimic any other Turing machine • mimicked machine is encoded as number on U-machine's tape, along with input for particular problem for mimicked machine • U-machine can mimic the encoded machine solving the problem <emulation> • Turing then proved that there are functions which U-machine can't compute … • notably the ‘halting problem’: will machine halt when computing a given function? • … but developed the “Church-Turing” thesis that: • a Universal Turing Machine can compute anything that can be computed at all • A staggering result from such a simple starting point! • Corollary: some functions cannot be computed at all …

  13. What is so important about Turing's machine? • active head vs. passive memory: treating program as data • hardware vs. software — distinguish abstract computation from physical implementation • can consider large range of alternative implementations • establishes an abstract ‘informational’ level for describing behaviour • in fact, engineered computers are like Turing machines with random access memory (RAM) (not infinite, unfortunately) • and vastly complicated heads called central processing units (CPUs) • (these are technically “von Neumann” machines)

  14. Automation of logical proof • Sometimes proofs can be computable • Even whole systems of proof • Programming languages can be based on this • E.g. Prolog • A language based on theorem proving from • FACTS and • RULES Compare: factorial(1, 1). factorial(Num, Factorial):- M is Num-1, factorial(M, FM), Factorial is FM*Num. int factorial(int x) { if (x == 1) return x; else return x*factorial(x-1); } (Declarative) (Procedural)

  15. Applications of AI • What can we do with these ideas, and how?

  16. General applications of AI (1):Representation of knowledge • (Contrast with data … • knowledge is richer and includes means of deriving consequences) • Rule-based systems • Cf Prolog: represent everything with facts and rules … • … then derive consequences by proof. • Assumes all knowledge can be captured this way • As in traditional expert systems • Case-based reasoning • Suppose that systems of rules will be too complicated … • Instead store cases that have worked in the past, • and some rules for working out how to re-use these

  17. General applications of AI (2): Approaches to formal semantics • Meaning as truth conditions • What does the world have to be like for a sentence to be true? • Provides semantics for simple systems like propositional or predicate calculus • Can be elaborated for use with natural languages, e.g. • Consider the world at other points in time • Consider otherpossible worlds • What can this approach not capture?

  18. Understanding humans • How can we use computational theories to understand the workings of the human mind? • Is this an illusory goal?

  19. Representational theories of mind • The Computational Metaphor: hard and soft AI • Contrast between focus on representation and focus on behaviour • What is "intelligence"? • Is it what you can do or is it how you do it? • The Turing Test • The Loebner Prize – http://hps.elte.hu/~gk/Loebner/TT.html • Eliza • Dennett, the "Intentional Stance" and instrumentalism • Idea that notions like “intelligence” are attributed • Linked to anti-essentialism and anti-realism

  20. Connectionist approaches andnon-representationalism • Connectionism, or “neural-net”-based theories • Distributed processing • No explicit locus of symbols or syntactic structures • Emergence • The sum of a system can be more than its parts • Environmental embedding and situated action • Lucy Suchman • Compare philosophical approaches of, e.g. • Heidegger (existential embedding) • Wittgenstein (social embedding)

  21. Searle – the “Chinese Room” • More pragmatic argument • Homunculus knows nothing, hence system cannot be a locus of understanding • Extended as claim that no mere symbol-processing system could ever “understand” anything at all • Claimed to be an “in-principle” argument 近义词 Two classic critiques • Dreyfus – phenomenology & Heidegger – Winograd & Flores • Fundamentalist anti-representationalism • Strong AI is impossible in principle

  22. AI in practical use • What is actually being done using these ideas?

  23. Practical considerations:AI as software engineering • Various general application fields • Expert systems • Either rule-based or case-based • Verification systems • To prove e.g. properties of safety-critical software • Language engineering – LSA – etc. • Used e.g. to mark essays • Information extraction, e.g. as in Edinburgh-Stanford Link • Combined maybe with text/speech generation: www.dj4me.com • Dialogue systems • Increasingly multimodal: speech, gesture, etc. • Telephone sales etc. applications; commercial “chatbots” • Entertainment, e.g. the BBC’s Jamie Kane • ITSs • Will teachers be replaced by computers? • Importance of the social …

  24. Design/architecture applications • Representation of design knowledge (contrast with Schön!) • Cf. Coyne et al. Knowledge-Based Design Systems • Intelligent information design and presentation • Automated musical composition • Shape grammars (http://www.mit.edu/~tknight/IJDC/) • CBR • Building performance evaluation systems • Standardisation and automation in construction • Issues of “prescriptiveness” …

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