Course Material “Research Seminar: Artificial Intelligence”
by Maarten Lamers
Media Technology MSc program, Leiden University
This seminar-style course studies the topic of artificial intelligence, taking a broad and historical view. Presentations are mainly held by the students themselves. Goal of the course is to learn studying, processing and presenting scientific material, and to learn about artificial intelligence. The seminar consists of lectures, homework assignments/tests, and student presentations.
It covers various sexy topics from the field of artificial intelligence, to the level that should enable students to discuss AI comfortably with other scientists. The selected topics were chosen to be practically applicable for future Media Technology projects, or to make students think about future directions. They include the question of whether machines can think, evolutionary computation, neural networks, computing with DNA, computers and emotions, computational creativity and more. It is not a complete overview of AI topics. Some topics are not strictly AI but related; they were included to understand the history and broader context of artificial intelligence.
The course is open to students from other programmes and institutes also, but only if spaces are available. Due to the Seminar-structure, only a limited number of students can be enrolled. Request admission from firstname.lastname@example.org. Media Technology MSc students have priority over other students.
|Lecturer:||Maarten Lamers of LIACS.|
|Teaching Assistant:||Name and email on the course Blackboard.
Contact the TA for questions outside class.
Snellius building, room 413.
|Schedule:||see the Media Technology calendar.|
|Level, Credits:||level 500 (scientifically oriented master course),
|Requirements:||- attendance in all classes is required, no exceptions
- a good grasp of verbal and written English (required)
- active participation in class (required)
- computer programming experience (strongly recommended)
Evaluated works: homework test questions and topical presentations.
|Literature:||no book, only web-available materials.|
Homework & Tests
Before each class, students must read material about the discussed topic. Most classes contain a homework test in which students must answer some basic test questions about the homework reading for that week, or that of the week before. These can be either at the start or end of class. The answers are graded. If a student does not attend the lecture, or is late for the homework test, a 0 is given (no exceptions). The lowest homework result is excluded from consideration.
There are two types of student presentations. Each student does only one of these presentation types.
- Duo-presentations, in which two students present the homework reading material and other material that they found themselves. Both students should speak for approximately equal parts of the total presentation. Each presentation should have two parts of 20 minutes each: the first part discusses the homework reading material, the second discusses applications (or examples) of the technique and why the technique is suited for such applications. For the second part, students should find their own material.
- Single-presentations, in which single students present a published scientific article in 20 minutes. They must start with clearly mentioning the title, authors, year of publication, and mention where it was published.
- Take your presentation very seriously and practice it! With the presentations you teach the rest of the class, which is a serious matter. Therefore I expect that you prepare very well and do your best.
- Presentations are graded on the choice of material, the displayed understanding of the material, and the structure of the presentation. Personal verbal skills are not graded, unless they make the presentation un-understandable. Duo-presentations are graded per-duo, meaning that a single grade is shared by both students. If there is a substantial difference in the amount/quality of the work by both students, then they may be graded separately (lecturer's prerogative). However, since both students are responsible for the total presentation, their separate grades will somehow reflect the average.
- When focussing on a specific article, introduce the author(s). This makes the article stick better in our minds.
- Following each presentation there is room for questions and class discussion.
- Make sure you present clearly, so that I can judge your understanding of the material.
- If you use a video projector/screen, make sure that the laptop and software run perfectly prior to your presentation! If you use a Mac computer, don't forget to bring a Mac-to-HDMI-cable-thingy. Internet is available through eduroam only.
- Topics and articles for presentations are assigned by the lecturer, throughout the course.
- During student presentations it is not allowed to have your laptop open, spend time on your phone, Facebook, Twitter, etcetera.
- Presentation slides must be submitted (in PDF format) prior to the presentation. Do this via the course Blackboard.
- Keep videos in your presentation short; preferably under 20 seconds.
- When you show an example of something, make sure to state clearly of what it is an example, and why you show it.
- Dare to add personal reflection on what you present.
- Dare to create a broad context around the topic or text that you present.
- Dare to "take control of the room", lead the audience through your story "with strength".
|course introduction:||Mon Feb 3|
|course organization & rules||Read this page until here|
|context of AI (teacher)||-|
|assigning first topics to students||-|
|artificial minds:||Mon Feb 10|
|the mind-body problem||[Rowlands 2003]|
|can machines think? (student, student)||[Dennett 1990]|
|the chinese room argument (student)||[Searle 1990]|
|artificial minds & representation:||Mon Feb 17|
|beyond the turing test (student)
|consciousness (student)||[Koch 2011]|
|Rodney Brooks / nouvelle AI (student, student)||[Brooks 1991]|
|artificial affective & social beings:||Mon Feb 24|
|affective computing (student, student)||[Picard 2003]|
|social robotics (student, student)||[Dautenhahn 2007; secs 1-3 only]|
|artificial evolution:||Mon Mar 2|
|evolutionary computation (student, student)||[Eiben 2003, secs 2.1-2.3, 2.5]
[Cawsey 1997, sec 7.5]
|assigning remaining topics to students||-|
|artificial evolution:||Mon Mar 9|
|designing an evolutionary algorithm (teacher)||same as previous|
|running an evolutionary algorithm (teacher)||-|
|artificial neurons:||Mon Mar 16|
|feedforward networks (teacher)||[Hinton 1992, pp 105-108]
[Cawsey 1997, secs 7.1, 7.2, 7.6]
|no class||Mon Mar 23|
|artificial neurons & deep learning:||Mon Mar 30|
|feedforward networks (teacher)||same as previous|
|non-linear dimensionality reduction
(teacher) [DeMers 1993]
|deep learning (teacher)
|natural language processing:||Mon Apr 6|
|advances in nlp (student, student)||[Hirschberg 2015]|
|the great awakening (student, student)||[Lewis-Kraus 2016]|
|no class||Mon Apr 13|
|embodiment & agency:||Mon Apr 20|
|apparent behaviour||skim [Heider 1944] quickly|
|synthetic embodied emotions||[Braitenberg 1984, pp 1-19]|
|robots (student, student)||-|
|artificial swarms (student)||[Bonabeau 2008]|
|no class||Mon Apr 27|
|no class||Mon May 4|
|artificial creativity:||Mon May 11|
|creativity and ai (student, student)
|the final frontier? (student, student)||[t.b.a.]|
|"wet" artificial intelligence:||Mon May 18|
|DNA computing (student, student)||[Adleman 1998]|
|biological comp & control (student, student)||[Skinner 1960]
|fear (and cyborgs?):||Mon May 25|
|fear of ai (student, student)
|cyborg intelligence (student)||[Zeng 2014]|
|no class||Mon Jun 1|
|application domains:||Mon Jun 8|
|ai in cars (student)
|Deep Blue & AlphaGo (student)||-|
|ai in games||[Nareyek 2004]|
|miscellaneous:||Mon Jun 15|
|limits of intelligence (student)
|japanese digital pets||[Kusahara 2001]|
|t.b.a.:||Mon Jun 22|
Note that many articles can be downloaded from the university's network only (not from home) due to copyright/publisher restrictions.
artificial intelligence in general
|[FLI 2015a]||The Future of Life Institute (2015), Research Priorities for Robust and Beneficial Artificial Intelligence: an Open Letter, online resource, Jan 11 2015.|
|[FLI 2015b]||The Future of Life Institute (2015), Research priorities for robust and beneficial artificial intelligence, whitepaper, Jan 11 2015, pp 1-12.|
|[Lewis-Kraus 2016]||Gideon Lewis-Kraus (2016), The Great A.I. Awakening, The New York Times Magazine, Dec 14, 2016.|
|[Turing 1950]||Alan M Turing (1950), Computing Machinery and Intelligence, Mind 49 Num 236, pp 433-460.|
|[Minsky 1982]||Marvin Minsky (1982), Why People Think Computers Can't, AI Magazine, Fall 1982, pp 3-15.|
|[Hoenderdos 1988]||Piet Hoenderdos (1988), Victim of the Brain, a briliant Dutch film about the ideas of Douglas Hofstadter and Daniel Dennett (wikipedia, imdb).|
|[Dennett 1990]||Daniel C Dennett (1990), Can Machines Think?, from The Age of Intelligent Machines, Ray Kurzweil, MIT Press.|
|[Searle 1990]||John R Searle (1990), Is the Brain's Mind a Computer Program?, Scientific American 262(1), pp 26-31.|
|[Churchland 1990]||Paul Churchland and Patricia Churchland (1990), Could a Machine Think?, Scientific American 262(1), pp 32-37.|
|[Dennett 1997]||Daniel C Dennett (1997), Consciousness in Human and Robot Minds, from Cognition, Computation, and Consciousness, by M Ito, Y Miyashita, and ET Rolls, Oxford University Press.|
|[Copeland 2000]||B Jack Copeland (2000), The Turing Test, Minds and Machines 10, pp 519-539.|
|[Rowlands 2003]||Mark Rowlands (2003), Terminator I and II, the mind-body problem, Chapter 3 (pp 57-85) of The Philosopher at the End of the Universe — Philosophy explained through science fiction films, Ebury Press (Note: this excellent book was retitled to Sci-Phi: Philosophy from Socrates to Schwarzenegger).|
|[Koch 2011]||Christof Koch and Giulio Tononi (2011), A Test for Consciousness, Scientific American June 2011, pp 44-47.|
|[You 2015]||Jia You (2015), Beyond the Turing Test, Science Vol 347 Num 6218, p 116.|
artificial neurons & deep learning
|[Hinton 1992]||Geoffrey E Hinton (1992), How Neural Networks Learn from Experience, Scientific American September 1992, pp 104-109.|
|[Van Camp 1992]||Drew van Camp (1992), Neurons for Computers, Scientific American September 1992, pp 125-127.|
|[Dewdney: Neural Nets]||A.K. Dewdney (1993), Neural Networks That Learn, Chapter 36 (pp 241-249) of The New Turing Omnibus, Holt Publishers, NY.|
|[DeMers 1993]||David DeMers and Garrison Cottrell (1993), Non-Linear Dimensionality Reduction, Advances in Neural Information Processing Systems 5, pp 580-587.|
|[Kröse 1996]||Ben Kröse and Patrick van der Smagt (1996), An introduction to Neural Networks, unpublished book.|
|[Cawsey 1997]||Alison Cawsey (1997), Neural Networks, Sections 7.1, 7.2 and 7.6 of The Essence of Artificial Intelligence, Prentice Hall.|
|[Callan 2003]||Rob Callan (2003), Neural Networks I, Chapter 15 (pp 286-311) of Artificial Intelligence, Palgrave Macmillan.|
|[Russell 2003]||Stuart Russell and Peter Norvig (2003), Neural Networks, Section 20.5 (pp 736-748) of Artificial Intelligence, a Modern Approach (second edition), Prentice Hall Series in AI.|
|[Hinton 2006]||Geoffrey.E. Hinton and Ruslan R. Salakhutdinov (2006), Reducing the Dimensionality of Data with Neural Networks, Science Vol 313, pp 504-507.|
|[Wired 2013]||Daniela Hernandez (2013), The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI, Wired magazine, May 2013.|
|[Wired 2014]||Daniela Hernandez (2013), Meet the Man Google Hired to Make AI a Reality, Wired magazine, Jan 2014.|
|[Hayes 2014]||Brian Hayes (2014), Delving into Deep Learning, American Scientist Vol 102 (May-June), pp 186-189.|
|[Bengio 2016]||Yoshua Bengio (2016), Machines Who Learn, Scientific American 314(6), pp 38-43.|
|[Deep Wired]||Wired magazine's articles tagged with "deep learning".|
|[Turing 1950]||Alan M Turing (1950), Computing Machinery and Intelligence, Mind 49 Num 236, pp 433-460.|
|[Minsky 1982]||Marvin Minsky (1982), Why People Think Computers Can't, AI Magazine, Fall 1982, pp 3-15.|
|[Hofstadter 1982]||Douglas R Hofstadter (1982), Can Inspiration Be Mechanized?, Scientific American, September 1982, pp 18-31.|
|[Boden 1998]||Margaret A Boden (1998), Creativity and Artificial Intelligence, Artificial Intelligence Vol 103 Num 1, pp 347-356.|
|[Boden 2004]||Margaret A Boden (2004), The Creative Mind: Myths and Mechanisms, Routledge, pp 1-10. Later reprinted as Creativity in a Nutshell, Think Vol 5(15), 2007, pp 83-96.|
|[Cardoso 2009]||Amílcar Cardoso, Tony Veale and Geraint A. Wiggins (2009), Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity, AI Magazine Vol 30 Num 3, Fall 2009, pp 15-22.|
|[Colton 2012]||Simon Colton and Geraint A. Wiggins (2012), Computational Creativity: The Final Frontier?, Frontiers in Artificial Intelligence and Applications Vol. 242, pp 21-26, DOI 10.3233/978-1-61499-098-7-21.|
|[McCormack 2014]||Jon McCormack, et al., (2014), Ten Questions Concerning Generative Computer Art, Leonardo, Vol 47(2), pp 135-141.|
|[Cawsey 1997]||Alison Cawsey (1997), Genetic Algorithms, Section 7.5 of The Essence of Artificial Intelligence, Prentice Hall.|
|[Eiben 2003]||AE Eiben and JE Smith (2003), Introduction to Evolutionary Computing, Springer.
- Chapter 1, Introduction, pp 1-14
- Chapter 2, What is an Evolutionary Algorithm?, pp 15-35
- explanation of symbols used in Chapter 2,
- Guszti Eiben worked at LIACS before becoming a full professor in Amsterdam.
|[De Jong 2006]||Kenneth A De Jong (2006), Introduction, Chapter 1 (pp 1-22) of Evolutionary Computing, a Unified Approach, MIT Press.|
|[Picard 1995]||Rosalind W Picard (1995), Affective Computing, MIT Media Laboratory Perceptual Computing Section, Technical Report No 321.|
|[Picard 1996]||Rosalind W Picard (1996), Does HAL Cry Digital Tears? Emotion and Computers, Chapter 13 of HAL's Legacy: 2001's Computer as Dream and Reality, MIT Press.|
|[Picard 1997]||Rosalind W Picard (1997), Affective Computing, MIT Press. (Google Books)|
|[Picard 2000]||Rosalind W Picard (2000), Toward Computers That Recognize and Respond to User Emotion, IBM Systems Journal, Vol 39 Num 3-4, pp 705-719.|
|[Picard 2003]||Rosalind W Picard (2003), What Does It Mean for a Computer to “Have” Emotions?, MIT Media Laboratory Technical Report, Num 534.|
|[Gibbs 2003]||W Wayt Gibbs (2003), Why Machines Should Fear, Scientific American, December 2003, pp 37-37A.|
|[Picard 2004]||Rosalind W Picard, et al. (2004), Affective Learning — a Manifesto, BT Technical Journal, Vol 22(4), pp 253-269.|
|[Picard 2015]||Rosalind W Picard (2015), Virtual Love, Science Vol 349(6245), pp 243.|
|[Fong 2003]||Terrence Fong, Illah Nourbakhsh, Kerstin Dautenhahn (2003), A Survey of Socially Interactive Robots, Robotics and Autonomous Systems 42, pp 143–166.|
|[Dautenhahn 2007]||Kerstin Dautenhahn (2007), Socially Intelligent Robots: Dimensions of Human–Robot Interaction, Philosophical Transactions of the Royal Society B vol. 362, pp 679–704.|
|[Levy 2008]||Not Tonight, Dear, I Have to Reboot, Scientific American, March 2008, pp 94-97.|
|[Skinner 1960]||B.F. Skinner (1960), Pigeons in a Pelican, American Psychologist, Vol. 15, No. 1, pp 28-37.
Silent video showing Skinner's experiments.
|[Nakagaki 2000]||Toshiyuki Nakagaki, Hiroyasu Yamada, Ágota Tóth (2000), Maze-Solving by an Amoeboid Organism, Nature 407, p 470.
Video showing Nakagaki's experiment.
|[ScienceDaily 2004]||'Brain' In A Dish Acts As Autopilot, Living Computer, ScienceDaily.com, 22 October 2004.|
|[NewScientist 2006]||Robot Moved by a Slime Mould's Fears, NewScientist.com, 13 February 2006.|
|[Nature 2008]||Cellular Memory Hints at the Origins of Intelligence (2008), Nature 451, pp 385.|
|[ScienceDaily 2008]||Robot With A Biological Brain: New Research Provides Insights Into How The Brain Works, ScienceDaily.com, 14 August 2008.|
|[Adamatzky 2010]||Andrew Adamatzky and Jeff Jones (2010), Road Planning with Slime Mould: If Physarum built motorways it would route M6/M74 through Newcastle, in print.|
|[Zeng 2014]||Daniel Zeng, and Zhaohui Wu (2014), From Artificial Intelligence to Cyborg Intelligence, IEEE Intelligent Systems 29(5), pp 2-4.|
|[Wu 2014]||Zhaohui Wu, Gang Pan, Jose C. Principe, and Andrzej Cichocki (Eds), Special Issue on Cyborg Intelligence: Towards Bio-Machine Intelligent Systems, IEEE Intelligent Systems 29(6), 2014.|
|[Gardner 1970]||Martin Gardner (1970), The fantastic combinations of John Conway's new solitaire game "life", Scientific American 223, October 1970, pp 120-123.|
|[Wikipedia:Conway]||Wikipedia Entry for Conway's Game of Life.|
|[Dewdney: Cellular]||A.K. Dewdney (1993), Cellular Automata, Chapter 44 (pp 295-300) of The New Turing Omnibus, Holt Publishers, NY.|
|[Steels 1994]||Luc Steels (1994), The Artificial Life Roots of Artificial Intelligence, Artificial Life Journal, Vol 1 Num 1-2, pp 75-110.|
|[Sipper 1995]||Moshe Sipper (1995), An Introduction to Artificial Life, Explorations in Artificial Life (special issue of AI Expert), pp 4-8.|
|[Bedau 2000]||Mark A Bedau, John S McCaskill, Norman H Packard, Steen Rasmussen, Chris Adami, David G Green, Takashi Ikegami, Kunihiko Kaneko, and Thomas S Ray (2000), Open Problems in Artificial Life, Artificial Life, Vol 6 Num 4, pp 363-376.|
|[Brooks 2001]||Rodney Brooks (2001), The Relationship Between Matter and Life, Nature 409, pp 409-411.|
|[Packard 2003]||Norman H Packard and Mark A Bedau (2003), Artificial Life, Encyclopedia of Cognitive Science, Vol 1, Macmillan Publ., pp 209-215.|
artificial creatures & behaviour
|[Heider 1944]||Fritz Heider and Marianne Simmel (1944), An Experimental Study of Apparent Behavior, American Journal of Psychology 57(2), pp 243-259 (original animation, remake animation)|
|[Braitenberg 1984]||Valentino Braitenberg (1984), Vehicles: Experiments in Synthetic Psychology, MIT Press, pp 1-19.|
|[Reynolds 1987]||Craig W Reynolds (1987), Flocks, Herds, and Schools: A Distributed Behavioral Model, ACM SIGGRAPH Computer Graphics 21(4), July 1987, pp 25-34.|
|[Brooks 1990]||Rodney Brooks (1990), Elephants Don't Play Chess, Robotics and Autonomous Systems 6, pp 3-15.|
|[Brooks 1991]||Rodney Brooks (1991), Intelligence Without Representation, Artificial Intelligence 47, pp 139-159.|
|[Kusahara 2001]||Machiko Kusahara (2001), The Art of Creating Subjective Reality: An Analysis of Japanese Digital Pets, Leonardo Vol 34 Num 4, pp 299-302.|
|[Nareyek 2004]||Alexander Nareyek (2004), AI in Computer Games, ACM Queue, February 2004, pp 58-65.|
|[Bonabeau 2008]||Eric Bonabeau and Guy Theraulaz (2008), Swarm Smarts, Scientific American Special Editions 18(1), Your Future With Robots, pp 42-49.|
DNA- and quantum computing
|[Adleman 1998]||Leonard M Adleman (1998), Computing with DNA, Scientific American August 1998, pp 54-61.|
|[Gershenfeld 1998]||Neil Gershenfeld and Isaac L Chuang (1998), Quantum Computing with Molecules, Scientific American June 1998, pp 66-71.|
|[West 2000]||Jacob West (2000), The Quantum Computer: An Introduction, online resource.|
|[Shapiro 2006]||Ehud Shapiro and Yaakov Benenson (2006), Bringing DNA Computers to Life, Scientific American May 2006, pp 44-51.|
|[Dewdney: Logic Prog]||A.K. Dewdney (1993), Logic Programming, Chapter 64 (pp 420-426) of The New Turing Omnibus, Holt Publishers, NY.|
|[Lenat 1995]||Douglas B Lenat (1995), Artificial Intelligence, Scientific American, September 1995, pp 80-82.|
|[Liebowitz 1995]||Jay Liebowitz (1995), Expert systems: a short introduction, Engineering Fracture Mechanics Vol 50, Num 5/6, pp 601-607.|
|[Berners-Lee 2001]||Tim Berners-Lee, James Hendler And Ora Lassila (2001), The Semantic Web, Scientific American, May 2001, pp 35-43.|
|[Feigenbaum 2007]||Lee Feigenbaum, Ivan Herman, Tonya Hongsermeier, Eric Neumann, and Susie Stephens (2007), The Semantic Web in Action, Scientific American, December 2007, pp 90-97.|
|[Dewdney: Analog]||A.K. Dewdney (1993), Analog Computation, Chapter 33 (pp 223-230) of The New Turing Omnibus, Holt Publishers, NY.|
|[Maes 1994]||Pattie Maes (1994), Agents That Reduce Work and Information Overload, Communications of the ACM 37(7), pp 30-40.|
|[Sutton 1998]||Richard S Sutton and Andrew G Barto (1998), Introduction, Chapter 1 of Reinforcement Learning: An Introduction , MIT Press.|
|[Webb 2002]||Barbara Webb (2002), Robots in Invertebrate Neuroscience, Nature 407, pp 359-363.|
|[Linden 2003]||Greg Linden, Brent Smith, and Jeremy York (2003), Amazon.com Recommendations: Item-to-Item Collaborative Filtering, IEEE Internet Computing Vol 7(1), pp 76-80.|
|[King 2011]||Ross D King (2011), Rise of the Robo Scientists, Scientific American January 2011, pp 72-77.|
|[Fox 2011]||Douglas Fox (2011), The Limits of Intelligence, Scientific American July 2011, pp 36-43.|
|[Dube 2015]||Ryan Dube (2015), How Hollywood Has Depicted Artificial Intelligence Over The Years, makeuseof.com article, March 26 2015.|
|[Bohannon 2015]||John Bohannon (2015), Fears of an AI pioneer, Science Vol 349(6245), pp 252.|
|[Jordan 2015]||Michael I. Jordan and Tom M. Mitchell (2015), Machine Learning: Trends, Perspectives, and Prospects, Science Vol 349(6245), pp 255-260.|
|[Hirschberg 2015]||Julia Hirschberg and Christopher D. Manning (2015), Advances in Natural Language Processing, Science Vol 349(6245), pp 261-266.|
|[Schultz 2015]||David Schultz (2015), Which Movies Get Artificial Intelligence Right?, Science Magazine News, July 17 2015, DOI:10.1126/science.aac8859.|
|[Shladover 2016]||Steven E. Shladover (2016), The Truth About "Self-Driving" Cars, Scientific American 314(6), pp 44-49.|
|[Self-driving Cars 2016]||Firner et al. (2016), End-to-End (Deep) Learning for Self-Driving Cars, on NVIDIA blog and arXiv.|
|[Russell 2016]||Stuart Russell (2016), Should We Fear Supersmart Robots?, Scientific American 314(6), pp 50-51.|