1. CSCI 4410 Introduction to Artificial Intelligence

2. What is AI? Difficult to define “The Intelligence of a System is inversely proportional to our understanding of it”

3. What is AI? making computer programs that appear to think? the automation of activities we associate with human thinking, like decision making, learning ? the art of creating machines that perform functions that require intelligence when performed by people ? the study of mental faculties through the use of computational models ? the study of computations that make it possible to perceive, reason and act ? a branch of computer science that is concerned with the automation of intelligent behavior ? anything in Computing Science that we don't yet know how to do properly ?

4. AI “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil) “The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight) But what about creativity? Many would argue machines are already writing rap music and reality showsmachines are already writing rap music and reality shows

5. Rational Systems How do we know how humans think? Introspection vs. psychological experiments Brain research (scanning, experiments, testing) Cognitive Science

6. Rational Systems Humans are not always ‘rational’ Rational - defined in terms of logic? Logic can’t express everything (e.g. uncertainty) Logical approach is often not feasible in terms of computation time - needs ‘guidance’ We will never get to intelligence with rules

7. Satisfiability Rule systems must be checked This is the Satisfiability Problem NP-complete Checking all the states of a large rule system is computationally expensive

8. Turing Test Described by Alan Turing in 1950 A human judge engages in a natural language conversation with a human and a machine If the judge cannot reliably tell which is which, then the machine passes the Turing test. The conversation is usually limited to text.

9. Turing Test However…

10. Turing Test A machine passing the Turing test may be able to simulate human conversation Is this intelligence? how do we know humans don't just follow rules? Blockhead – all paths Chinese room - rules Can young children pass the test?

11. Turing Test Turing test measures human-like behavior Even if the Turing test is a good definition of intelligence, it may not indicate consciousness. Does intelligence imply consciousness?

12. Practical AI Do we care whether a system: Replicates human thought processes Makes the same decisions as humans Uses purely logical reasoning

13. AI in Practice Medical advice system Part-picking robots Credit card fraud detection Spam filters Medical diagnosis, teleoperated/micro surgery

14. AI in Practice Information retrieval, Google Scheduling, logistics, supply chain management Aircraft and pipeline inspection Speech recognition, generation, translation

15. AI in Practice And robots and chatbotschatbots

16. Heuristics Two fundamental goals: finding algorithms with good run times and optimal solutions. But… these goals are often mutually exclusive A heuristic is an algorithm that relaxes one or both of these goals

17. Heuristics Special instances of the problem may cause the heuristic to produce poor results or run slowly These instances may be rare Ex: sorting algorithms where the list is already sorted Matching the heuristic to the domain is important Heuristics are very common in real world implementations.

18. Example – Spam Assassin Spam Assassin uses a wide variety of heuristic rules to determine whether an email is a spam or ham Bayesian filter Blacklisting Regular expression matching

19. Modern Focus Artificial intelligence can be considered under a number of headings: Search Representing Knowledge and Reasoning Planning Uncertainty Learning Interacting with the Environment (e.g. Vision, Speech, Robotics)

20. Search Search is the fundamental technique of AI. Possible answers, decisions or courses of action are structured into an abstract space, which we then search. Search is either "blind" or "informed": blind we move through the space without worrying about what is coming next, but recognising the answer if we see it informed we guess what is ahead, and use that information to decide where to look next. Desire for optimal solutions leads to heuristics

21. Knowledge Representation and Reasoning If we are going to act rationally in our environment, then we must have some way of describing that environment. how do we represent what we know about the world ? how do we represent it concisely ? how do we represent it so that we can get hold of the right piece of knowledge when we need it ? how do we generate new pieces of knowledge ? how do we deal with uncertain knowledge ?

22. Planning Given a set of goals, construct a sequence of actions that achieves those goals: often very large search space but most parts of the world are independent of most other parts often start with goals and connect them to actions no necessary connection between order of planning and order of execution what happens if the world changes as we execute the plan and/or our actions don’t produce the expected results?

23. Uncertainty Given the set of “uncertain” information, how can we achieve the goals (and how certain are we of that answer). How do we deal with uncertainty in our daily lives? How can we make this more systematic How can we build systems that deal with uncertainty How can we insure that the systems are reasonable and correct

24. Learning If a system is going to act truly appropriately, then it must be able to change its actions in the light of experience: Generating new facts from old How do we generate new concepts ? How do we learn to distinguish different situations in new environments ?

25. Knowledge Virtually all techniques benefit from ‘common sense’ CYC – a very large database of general purpose knowledge

26. Resolving Ambiguity – Ex. Consider the following pair of sentences: Fred saw the plane flying over Zurich. Fred saw the mountains flying over Zurich. Humans recognize that in the first sentence, "flying" refers to the plane In the second sentence, "flying" almost certainly refers to Fred. Traditional Natural Language systems will have difficulty resolving this syntactic ambiguity Cyc knows that planes fly and mountains do not, and can reject nonsensical interpretations.