Lecturer: Tony Veale
Fields: Artificial Intelligence
Until quite recently, AI was a scientific discipline defined more by its portrayal in science fiction than by its actual technical achievements. Real AI systems are now catching up to their fictional counterparts, and are as likely to be seen in news headlines as on the big screen. Yet as AI outperforms people on tasks that were once considered yardsticks of human intelligence, one area of human experience still remains unchallenged by technology: our sense of humour.
This is not for want of trying, as this course will show. The true nature of humour has intrigued scholars for millennia, but AI researchers can now go one step further than philosophers, linguists and psychologists once could: by building computer systems with a sense of humour, capable of appreciating the jokes of human users or even of generating their own, AI researchers can turn academic theories into practical realities that amuse, explain, provoke and delight.
This course will use the ideas and achievements of AI to explore what it means to have a sense of humour, and moreover, to understand what it is to not have one. It will challenge the archetype of the humourless machine in popular culture, to celebrate what science fiction gets right and to learn from what it gets wrong. It will make a case for the necessity of a computational understanding of humour, to better understand ourselves and to better construct machines that are more flexible, more understanding, and more willing to laugh at their own limitations.
The course will comprise four lectures, which will explore the following topics.
Newspaper personal columns are routinely filled with people seeking partners with a good sense of humour (GSOH), with many rating this as highly as physical fitness or physical appearance. Yet what does it mean to have a sense of humour? Conversely, what does it mean to have NO sense of humour, and how might we imbue a humourless machine with a capacity for wit and a flair for the absurd? We begin by unpacking these questions, to suggest some initial answers and models.
So, for example, what would it mean for a computer to have a numeric humour setting, as in the case of the robot TARS in the film Interstellar? Can a machine’s sense of humour be reduced to a single number or parameter setting? Is humour a modular ability? Can it be gifted to computers as a bolt-on unit like Commander Data’s “humour chip” in Star Trek, or is it an emergent phenomenon that arises from complex interactions among all our other faculties? Might humour emerge naturally within complex AI systems without explicitly being programmed to do so, as in the mischievous supercomputer Mike in Robert Heinlein’s The Moon is a Harsh Mistress or in the sarcastic droid K2SO in Rogue One: A Star Wars story?
This course will survey and critique the competing humour theories that scholars have championed through the ages, enlarging on recurring themes (incongruity, relief, superiority) while considering the amenability of each to computational modelling. What is it that these theories are really explaining, and which comes closest to capturing the elusive essence of humour?
The centrality of incongruity in modern theories demands that this concept be given a special focus. So we will unpack its many meanings to show how our understanding of incongruity can be as multifaceted as the idea of humour itself. Popular myths about the brittleness of machines in the face of the incongruous and the unexpected will be unpicked and debunked as we explore how machines might deliberately seek out and invent incongruities of their own.
But computational humour is still in its infancy, and it is no coincidence that the mode of humour for which machines show the greatest aptitude is that which humans embrace at a very early age, puns. Puns vary in wit and sophistication, but the simplest require only an ear for sound similarity and a disregard for the consequences of replacing a word with its phonetic doppelgänger. The challenge for AI systems is to progress, as children do, from these simple beginnings to modes of ever greater conceptual sophistication.
To do so, is it possible to capture the essence of jokes in a mathematical formula, much as physicists have done for electromagnetism and gravity? Do jokes have quantifiable features that we and our machines can intuitively appreciate? Can we build statistical models to characterize the signature qualities of a humorous artefact, so that machines can learn to tell funny from unfunny for themselves? And what do these measurable qualities say about humour and about us?
Finally, however we slice it, conflict sits at the heart of humour, whether it is a conflict in meaning, attitude, expectation or perspective. Double acts personalize this conflict by recognizing the different roles a comic can play. Computers can likewise play multiple rules in the creation of humour, from rigid “straight man” to absurdist provocateur, in double-acts with humans and with other machines. So we will explore the ways in which smart machines can contribute to the social emergence of humour, either as embodied robots or disembodied software.
- Computational humour studies is an established field that has produced a range of academic books, from Victor Raskin’s Semantic Mechanisms of Humour (1985, one of the first) to the more recent Primer of Humour Research (with chapters from computational humorists). Non-computational humour researchers, such as Elliott Oring, have also written accessible books on humour, such as Engaging Humour, while the computer scientist Graeme Ritchie has written a pair of well-received academic books on humour. Comedians and comedy professionals have also written some noteworthy books on humour, with individual chapters that focus on computational humour or that offer algorithmic insights into the author’s own comedy production strategies. Toplyn’s Comedy Writing for Late-Night TV offers a beginner’s guide to humour production that is frequently schematic in style. The Naked Jape, by Jimmy Carr and Lucy Greeves, considers humour more broadly, but also offers a chapter on computational models and the people who build them. I will quote from each of the sources as needed.
Tony Veale is an associate professor in the School of Computer Science at UCD (University College Dublin), Ireland. He has worked in AI research for 25 years, in academia and in industry, with a special emphasis on humour and linguistic creativity. He is the author of the 2012 monograph Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity (from Bloomsbury), co-author of the 2016 textbook, Metaphor: A Computational Perspective (Morgan Claypool), and co-author of 2018’s Twitterbots: Making Machines That Make Meaning (MIT Press). He led the European Commission’s coordination action on Computational Creativity (named PROSECCO), and collaborated on international research projects with an emphasis on computational humour and imagination, such as the EC’s What-If Machine (WHIM) project. He runs a web-site dedicated to explaining AI with humour at RobotComix.com. He is active in the field of Computational Creativity, and is currently the elected chair of the international Association for Computational Creativity (ACC).
Affiliation: University College Dublin