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AI Present and Future
Alan Smaill
University of Edinburgh, School of Informatics
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%.
Formative exercises: there will be a number of unassessedexercises for which the labs are available to drop in and askquestions about.
Coursework: there will be one piece of coursework for whichthere will be a lecture on February 26th, with a deadline onMarch 15th at 4pm.
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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 unassessedexercises for which the labs are available to drop in and askquestions about.
Coursework: there will be one piece of coursework for whichthere will be a lecture on February 26th, with a deadline onMarch 15th at 4pm.
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Course Info
Course information will be updated on-line as the courseproceeds (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 athttps://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 Info
Course information will be updated on-line as the courseproceeds (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 athttps://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 ModernApproach”, 3rd edition, Prentice Hall, 2016http://aima.cs.berkeley.edu/
Poole and Mackworth: “Artificial Intelligence”, CambridgeUniversity Press, 2017https://artint.info/
Blackburn, Bos and Streignitz: “Learn Prolog Now”, CollegePublications, 2006http://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 andcontinuous univariate random variables; Expectation, variance;Joint and conditional distributions.
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The course
The course is NOT intended to provide specialist knowledge inparts 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 ofsome specialist areas when discussing questions of how the currentdifferent approaches to AI relate to each other,
and also what opportunities and dangers there might be indeployment 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?
How can we compare symbolic AI systems withsubsymbolic and probabilistic systems?
We will explain the distinctions involved here as we go through thecourse.
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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 withsubsymbolic and probabilistic systems?
We will explain the distinctions involved here as we go through thecourse.
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Some ancient history
Alan Turing’s famous paper from 1950 “Computing Machinery andIntelligence” can be found in many places, eg
http://www.abelard.org/turpap/turpap.htm
He proposed to replace the question “Can a machine think?” withone where there is a clear way to decide what the outcome is:Can we distinguish between the behaviour of a human and amachine?
It is proposed that a machine may be deemed intelligent,if it can act in such a manner that a human cannotdistinguish the machine from another human merely byasking questions via a mechanical link.
Turing, 1950
The paper also sketches the capabilities that he believed could beachieved in different areas (vision, natural language, learning, . . . )
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Some ancient history
Alan Turing’s famous paper from 1950 “Computing Machinery andIntelligence” can be found in many places, eg
http://www.abelard.org/turpap/turpap.htm
He proposed to replace the question “Can a machine think?” withone where there is a clear way to decide what the outcome is:Can we distinguish between the behaviour of a human and amachine?
It is proposed that a machine may be deemed intelligent,if it can act in such a manner that a human cannotdistinguish the machine from another human merely byasking questions via a mechanical link.
Turing, 1950
The paper also sketches the capabilities that he believed could beachieved 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 ArtificialIntelligence (1956).
We propose that a 2 month, 10 man study of artificialintelligence be carried out during the summer of 1956 atDartmouth College in Hanover, New Hampshire.The study is to proceed on the basis of the conjecturethat every aspect of learning or any other feature ofintelligence can in principle be so precisely described thata machine can be made to simulate it. An attempt willbe made to find how to make machines use language,form abstractions and concepts, solve kinds of problemsnow 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 ofthe 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 aprofessional human Go player, the first program to defeata Go world champion, and arguably the strongest Goplayer in history.AlphaGo’s first formal match was against the reigning3-times European Champion, Mr Fan Hui. in October2015. Its 5-0 win was the first ever against a Goprofessional, and the results were published in fulltechnical 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: 1994Chess: 1997Go: 2015/16
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Techniques
These games are in increasing order of difficulty, according to someanalyses 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 hasalso changed the context.
But there are distinctively different approaches involved at thistime.
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Techniques
These games are in increasing order of difficulty, according to someanalyses 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 hasalso changed the context.
But there are distinctively different approaches involved at thistime.
Alan Smaill AI Present and Future 15/01/19 14/19
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Techniques
These games are in increasing order of difficulty, according to someanalyses 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 hasalso changed the context.
But there are distinctively different approaches involved at thistime.
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Machine translation
This is another area where considerable human expertise wastraditionally needed, here to find good translations betweendifferent human languages. This can deal with written language, orspoken language.
A traditional approach involved looking at grammars for thedifferent natural languages; this is not the normal approach now.
The European Union has invested in technologies for this, forobvious reasons; a useful resource has been a parallel corpus oftexts in EU languages (often legal documents). (Look forJRC-Acquis).
You have probably used some version of this, and know thestrengths and weaknesses of current systems.
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Machine translation
This is another area where considerable human expertise wastraditionally needed, here to find good translations betweendifferent human languages. This can deal with written language, orspoken language.
A traditional approach involved looking at grammars for thedifferent natural languages; this is not the normal approach now.
The European Union has invested in technologies for this, forobvious reasons; a useful resource has been a parallel corpus oftexts in EU languages (often legal documents). (Look forJRC-Acquis).
You have probably used some version of this, and know thestrengths 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 thanothers?
If we have an “acceptable” translation in 90% of test cases, isthat 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 significantprogress in last few years. Needs:
Sensing of environment: computer vision, GPS, radar, inertialmeasurement, . . .
Interpretation of sensory data and planning to identify actionsto take based on other traffic, obstacles, signage, . . .
Safety aspects
Clearly this needs coordination of many techniques and goodsoftware 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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