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!)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/)(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: Informal statement Formal statement Calculation (Inference) ResultReinterpretation

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 Informal statement Formal statement Calculation (Inference) ResultReinterpretation

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 … –If P then Q; P … P –> Q P Q –… therefore Q –So the light is on Informal statement Formal statement Calculation (Inference) ResultReinterpretation

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 IIIIIII IIIIIII Head

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 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 factorial(1, 1). factorial(Num, Factorial):- M is Num-1, factorial(M, FM), Factorial is FM*Num. Compare: 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 other possible 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.htmlhttp://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 and non-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. Two classic critiques Dreyfus – phenomenology & Heidegger – Winograd & Flores –Fundamentalist anti-representationalism –Strong AI is impossible in principle 近义词近义词 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

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 LinkEdinburgh-Stanford Link Combined maybe with text/speech generation: www.dj4me.comwww.dj4me.com –Dialogue systems Increasingly multimodal: speech, gesture, etc. Telephone sales etc. applications; commercial “chatbots”commercial “chatbots” Entertainment, e.g. the BBC’s Jamie KaneJamie 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 compositionmusical composition Shape grammars (http://www.mit.edu/~tknight/IJDC/)(http://www.mit.edu/~tknight/IJDC/) CBR Building performance evaluation systems Standardisation and automation in construction Issues of “prescriptiveness” …