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Second year , 20 credit module Lecturers – Dimitar Kazakov, James Cussens, Sam Devlin

ARIN Introduction to Artificial Intelligence. Second year , 20 credit module Lecturers – Dimitar Kazakov, James Cussens, Sam Devlin Schedule – Spring and Summer Format – 3 Lectures/Week, 2-hr lab/Week. ARIN Assessment. 3 x mini-assessments : Conducted in the labs (40% tot.)

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Second year , 20 credit module Lecturers – Dimitar Kazakov, James Cussens, Sam Devlin

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  1. ARIN Introduction to Artificial Intelligence • Second year,20 credit module • Lecturers – Dimitar Kazakov, James Cussens, Sam Devlin • Schedule – Spring and Summer • Format – 3 Lectures/Week, 2-hr lab/Week.

  2. ARIN Assessment • 3 x mini-assessments: Conducted in the labs (40% tot.) • Search and KR in Spring, Machine learning in Summer • Marks split (almost!) evenly between the 3 • They replace the normal practical for that week • If you are allowed extra time in closed exams you are allowed extra time for these • Being in the SW labs they allow but do not necessitate computer-based questions • 1 closed exam: Summer (60%) • There is a sample exam with answers and one genuine past exam to look at.

  3. Textbooks • We’ve gone for Artificial Intelligence: A Modern Approach by Russell and Norvig • 6 copies in the library (3 on 1-week loan) • It’s not too expensive (for an academic book). Any edition is OK (3rd ed. is the latest). • Also dipping into Machine Learning by Tom Mitchell and Bayesian Reasoning and Machine Learning by David Barber

  4. Follow @gagg_ARIN • Follow me on Twitter • Receive comments and updates • Send me an answer to my question in class and see it live on the screen!

  5. Give it a go: • @gagg_ARINWhat are the first words/phrases that spring to mind when you hear #Artificial_Intelligence?

  6. Let Google guess:

  7. Stick a few pages from an AI book into Wordle:

  8. Definitions from Russell and Norvig

  9. A bit of history: dropping names... • Alan Turing suggested one day you may not be able to tell a computer from a human (“The T. Test”) in 1950 • John McCarthy introduced the term AI in 1955 - "the science and engineering of making intelligent machines” • Donald Michie: early pioneer in UK, see:- 2007 lecture on early AI and more [link]

  10. Strong vs Weak AI • Strong AI: a computer can become conscious, sentient, self-aware… and match a human on any task • Weak AI: (not so bold)- does the job on a specific task • Searle’s distinction b/w:- Strong AI hyp.: corr. simulation of behaviour ≣ mind- Weak AI hyp.: corr. simulation of behaviour ≣ a model of the mind

  11. Early Results and Criticism • Weizenbaum’s Eliza (1966) [link] • Searle’s Chinese Room Argument • The Lighthill Controversy (1973) • Boston Dynamics (hence Google)’s Big Dog (2008) [lnk]

  12. ‘Classical’ vs data-driven AI • White-box, logic-based models vs • Statistical, data-driven models • …and what Noam Chomsky has had to say [link]

  13. ARIN Module Structure • Part 1 (Search): Problem representation and Search • Part 2 (Logic): Propositional Logic and SAT solvers, First order logic • Part 3 (Knowledge Representation and Planning): Semantic ontologies, description logics and planning • Part 4 (Machine Learning) : propositional learning, decision trees, Bayes theorem, Naïve Bayes, MLE, MAP

  14. I, Robot Corner 1 Goal Problem representation and search (DLK) Corner 2 How do I represent this problem to stick to the essence but not oversimplify?

  15. I, Robot Corner 1 Goal Problem representation and search (DLK) States: NW, NE, SE, SW. Initial state: NE; Goal state: SW. Operators: Down(NE)  SE, Down(NW) SW, Left(SE) SW, etc. Corner 2 Solution 1: Down(NE)  SE, Left(SE) SW.

  16. I, Robot Corner 1 Goal Problem representation and search (DLK) Corner 2 Solution 2: Left(NE)  NW, Down(NW) SW.

  17. Search (DLK) Which plan do I go for? Why? • Which initial step will get me the closest to my goal? • Can I take into account the expected cost of the remaining part of my journey? • Do I take into account all alternatives? • Do I backtrack?

  18. Informed Search (DLK) Don’t just search - cut to the chase! But you can’t guarantee to be perfect – or successful! • Genetic algorithms, simulated annealing: the metaphors from nature you keep hearing about • You’ll learn a lot, soon!

  19. AI Societies • AISB (UK) [link] (one year free m-ship for students!) • BCS SGAI (UK) [link] • ECCAI (EU) [link] • AAAI (US) [link] Membership can offer access to publications, newsletters, travel bursaries, conference and summer school fee discounts, e.g.: • see Searle speak at AISB convention in Apr 2014 [link] • Watch the Loebner contest in Nov 2014 [link]

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