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T H E U N I V E R S I T Y O F E D I N B U R G H

AI Present and Future

Alan Smaill

University of Edinburgh, School of Informatics A.Smaill@ed.ac.uk

15/01/19

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AI: Present and Future

Organisation Lecture slots as timetabled. Standard exam at end of semester: exam counts for 75%, coursework for 25%.

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AI: Present and Future

Organisation Lecture slots as timetabled. Standard exam at end of semester: exam counts for 75%, coursework for 25%. Formative exercises: there will be a number of unassessed exercises for which the labs are available to drop in and ask questions about. Coursework: there will be one piece of coursework for which there will be a lecture on February 26th, with a deadline on March 15th at 4pm.

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Course Info

Course information will be updated on-line as the course proceeds (slides, coursework, references etc). Official description of course: http://www.inf.ed.ac.uk/teaching/courses/aipf/ There is a Piazza site for the course at https://piazza.com/ed.ac.uk/spring2019/infr11180 and all students should register There will be drop-in lab sessions starting in week 3.

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Course Info

Course information will be updated on-line as the course proceeds (slides, coursework, references etc). Official description of course: http://www.inf.ed.ac.uk/teaching/courses/aipf/ There is a Piazza site for the course at https://piazza.com/ed.ac.uk/spring2019/infr11180 and all students should register There will be drop-in lab sessions starting in week 3. You cannot take this course if you have taken Informatics 2d!

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Course texts

Various recommended reading will appear on the course. 3 texts are central for the course: Russell and Norvig: “Artificial Intelligence: a Modern Approach”, 3rd edition, Prentice Hall, 2016 http://aima.cs.berkeley.edu/ Poole and Mackworth: “Artificial Intelligence”, Cambridge University Press, 2017 https://artint.info/ Blackburn, Bos and Streignitz: “Learn Prolog Now”, College Publications, 2006 http://www.learnprolognow.org/

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Background knowledge

• 1. Experience with logic (predicate calculus) will be helpful.
• 2. A background in probability theory is advisable: Discrete and

continuous univariate random variables; Expectation, variance; Joint and conditional distributions.

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The course

The course is NOT intended to provide specialist knowledge in parts of AI taught elsewhere in Informatics (Machine Learning, Natural Language Processing, Robotics, Vision, Automated Reasoning, . . . ). In the course, you will be expected to bring your understanding of some specialist areas when discussing questions of how the current different approaches to AI relate to each other, and also what opportunities and dangers there might be in deployment of AI systems in the future.

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Topics

Not necessarily in this order: Reasoning agents Logic and inference via Logic Programming Linked data, semantic net and internet search Monte Carlo Tree Search Planning under uncertainty Adversarial search, game playing Probabilistic inference Inductive Logic Programming Approaches to machine learning AI prospects and dangers Ethical and Philosophical issues.

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A running theme

In the course, we will often come back to the following questions: What are the relationships between reasoning, computation and prediction in particular AI applications?

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A running theme

In the course, we will often come back to the following questions: What are the relationships between reasoning, computation and prediction in particular AI applications? How can we compare symbolic AI systems with subsymbolic and probabilistic systems? We will explain the distinctions involved here as we go through the course.

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Some ancient history

Alan Turing’s famous paper from 1950 “Computing Machinery and Intelligence” can be found in many places, eg http://www.abelard.org/turpap/turpap.htm He proposed to replace the question “Can a machine think?” with

• ne where there is a clear way to decide what the outcome is:

Can we distinguish between the behaviour of a human and a machine?

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Some ancient history

Alan Turing’s famous paper from 1950 “Computing Machinery and Intelligence” can be found in many places, eg http://www.abelard.org/turpap/turpap.htm He proposed to replace the question “Can a machine think?” with

• ne where there is a clear way to decide what the outcome is:

Can we distinguish between the behaviour of a human and a machine? It is proposed that a machine may be deemed intelligent, if it can act in such a manner that a human cannot distinguish the machine from another human merely by asking questions via a mechanical link. Turing, 1950 The paper also sketches the capabilities that he believed could be achieved in different areas (vision, natural language, learning, . . . )

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Dartmouth workshop

A proposal to work on “Artificial Intelligence”: The Dartmouth Summer Research project on Artificial Intelligence (1956).

We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

• J. McCarthy et al.; Aug. 31, 1955

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Some current AI systems

We’ll look very briefly at some current AI work, to get an idea of the the current state of affairs. Alpha Go Driverless cars Machine translation

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Alpha Go

See https://deepmind.com/research/alphago/ From that source: AlphaGo is the first computer program to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. AlphaGo’s first formal match was against the reigning 3-times European Champion, Mr Fan Hui. in October

• 2015. Its 5-0 win was the first ever against a Go

professional, and the results were published in full technical detail in the international journal, Nature.

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Why is this a breakthrough?

Game-playing systems go back to the start of AI (Turing worked on algorithms for playing chess in early 1940s). A time line (where computers better than best human):

Checkers: 1994 Chess: 1997 Go: 2015/16

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Techniques

These games are in increasing order of difficulty, according to some analyses of the search spaces involved. Different techniques have been fashionable at different times: fancy heuristics or brute strength?

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Techniques

These games are in increasing order of difficulty, according to some analyses of the search spaces involved. Different techniques have been fashionable at different times: fancy heuristics or brute strength? The relentless increase in computational power over the years has also changed the context.

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Techniques

These games are in increasing order of difficulty, according to some analyses of the search spaces involved. Different techniques have been fashionable at different times: fancy heuristics or brute strength? The relentless increase in computational power over the years has also changed the context. But there are distinctively different approaches involved at this time.

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Machine translation

This is another area where considerable human expertise was traditionally needed, here to find good translations between different human languages. This can deal with written language, or spoken language. A traditional approach involved looking at grammars for the different natural languages; this is not the normal approach now.

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Machine translation

This is another area where considerable human expertise was traditionally needed, here to find good translations between different human languages. This can deal with written language, or spoken language. A traditional approach involved looking at grammars for the different natural languages; this is not the normal approach now. The European Union has invested in technologies for this, for

• bvious reasons; a useful resource has been a parallel corpus of

texts in EU languages (often legal documents). (Look for JRC-Acquis). You have probably used some version of this, and know the strengths and weaknesses of current systems.

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Machine translation etc

Here we can ask: What exactly do we want from such translations? How might we decide that some translations are better than

• thers?

If we have an “acceptable” translation in 90% of test cases, is that acceptable for a given purpose?

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Driverless cars

Can have different levels of autonomy. . . Here there is a lot of ongoing work, and there has been significant progress in last few years. Needs: Sensing of environment: computer vision, GPS, radar, inertial measurement, . . . Interpretation of sensory data and planning to identify actions to take based on other traffic, obstacles, signage, . . . Safety aspects Clearly this needs coordination of many techniques and good software engineering skills.

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Driverless cars ctd

What are the opportunities and dangers here?? Better driving? Fewer accidents? Lower insurance? What happens when there is an accident? (already have fatal accident during tests) Who is responsible when things go wrong?

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Today

Course admin and background The early days Some current projects

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