Invited Speakers

IanApperly Professor Ian Apperly, School of Psychology, University of Birmingham

Topic: "Reasoning about mental states"

Reasoning about beliefs, knowledge, desires and intentions is central to humans’ ability to understand one another, and would surely be necessary for artificial agents to interact on equal terms with humans in a wide range of circumstances. However, ascribing mental states runs into classic difficulties that Artificial Intelligence encounters with unbounded information processing because, in any given situation, it is difficult to specify clearly what information is relevant for inferring what someone else believes, knows, or intends. Psychological research suggests that humans address this problem in two ways: by confronting it, with only partial success; and by side-stepping it with efficient but incomplete solutions.

Short biography:
Ian Apperly is an experimental psychologist, and his main research interest is in “mindreading” – the ability to take other people’s perspectives. He is the author of a book entitled “Mindreaders: The cognitive basis of theory of mind”, and over 80 papers on the development of these abilities, and their cognitive and neural basis. He is particularly interested in how mindreading can be simultaneously flexible and efficient, and with Stephen Butterfill he has proposed a “two systems” account of these abilities.
Professor Gordon Brown, Department of Psychology, University of Warwick

Topic: "Human memory and timing"

Human memory appears to be organised adaptively in that, at a given point in time and in a given context, the memories that are easiest for us to retrieve are the ones that are most likely to be needed. How does it achieve this? Human memories appear to be organized at least partly in terms of their temporal distances (i.e., how far in the past they occurred), and this organisation may be adaptive. In the talk I will discuss the time-scale invariant properties of human memory, the notion of temporal distinctiveness (memories that are temporally distinct are less confusable in memory), and related "ratio-rule" models of memory. I will also introduce the notion of contextual diversity as a principle underlying human memory.

Short biography:
Gordon Brown leads the Behavioural Science Group in the Department of Psychology at the University of Warwick, where he was appointed in 1994. He has held posts at the University of Wales, the University of Hong Kong, and the University of Essex and has over 150 academic publications. Much of his recent research has involved computational models of human timing and memory and, at the interface between economics and psychology, the psychology of judgement and decision-making as applied to consumer choice as well as agent-based models of political polarisation.
AlanBundy Professor Alan Bundy, School of Informatics, University of Edinburgh

Topic: "Representation change"

Human-like computing will entail the building of hybrid teams of persistant, autonomous agents: robots, softbots and humans. By 'persistant', we mean that they will have to deal with changes to both their goals and their environments, including changes to the agents with which they must interact. Such persistant agents must have internal representations of their environment, including of other agents. Autonomy wil entail that these representations must themselves change automatically as the agent's goals and environments evolve. The changes must be to the language of the representation as well as to the beliefs represented in this language. Despite its importance, automatic language change is a neglected research area. We will illustrate the need for such automated representational change, and describe some early experimental systems that implement it.

Short biography:
Alan Bundy is Professor of Automated Reasoning at the University of Edinburgh. He is a fellow of the Royal Society, the Royal Academy of Engineering and the Association for Computing Machinery. He was awarded the IJCAI Research Excellence Award (2007), the CADE Herbrand Award (2007) and a CBE (2012). He was Edinburgh's Head of Informatics (1998-2001) and a member of: the Hewlett-Packard Research Board (1989-91); both the 2001 and 2008 Computer Science RAE panels (1999-2001, 2005-2008). He was the founding Convener of UKCRC (2000-2005) and a Vice President of the BCS (2010-12). He is the author of over 290 publications.
Professor Nick Chater, Department of Psychology, University of Warwick

Topic: "Virtual bargaining as theory of social Interaction and communication"

Successful social interaction between agents (whether human or artificial) involves coordinating thoughts and behaviour. But how can such coordination achieved? If each agent attempts to second-guess the thoughts and behaviour of the other, there is a danger of an infinite regress. A tries to infer what B will do; and knows that B will try to infer what A will do; so A needs to figure out what B thinks that A will do; but what A will do in turn depends on what B thinks A thinks that B will do, and so on, forever. We introduce a different approach: agents should reason jointly about what they would agree to think or do, were they able to negotiate. That is, they reason not about “What will you do?” and “What should I do?, but rather “What should we agree to do?” Where it is “obvious” what a result of such negotiation would be, no actual communication is required: agents can coordinate their thoughts and actions through a simulation of the bargaining process. Virtual bargaining provides a new foundation for understanding the reasoning that underpins social behaviour, including communication itself.

Short biography:
Nick Chater joined WBS in 2010, after holding chairs in psychology at Warwick and UCL. He has over 200 publications, has won four national awards for psychological research, and has served as Associate Editor for the journals Cognitive Science, Psychological Review, and Psychological Science. He was elected a Fellow of the Cognitive Science Society in 2010 and a Fellow of the British Academy in 2012. Nick is co-founder of the research consultancy Decision Technology; and is on the advisory board of the Cabinet Office's Behavioral Insight Team (BIT), popularly known as the 'Nudge Unit'.
AnthonyCohn Professor Anthony Cohn, School of Computing University of Leeds

Topic: "Spatial reasoning"

Being able to represent space and time is fundamental to an agent’s ability to operate effectively in the world it inhabits, to process language, and to recognise activities which it observes.   In this talk I will present approaches to represent, reason about, and also to learn such spatio-temporal knowledge, focussing on qualitative representations. these have a number of advantages, especially in relation to human level computing, since much of human spatial knowledge is qualitative, certainly as it appears in language. I will also discuss the issue of grounding spatio-temporal language to the visual world.

Short biography:

My PhD was in the area of many sorted logic, a way of encoding taxonomic knowledge efficiently in a computational logic. After that I got interested in naďve physics and common sense knowledge and focussed in particular on spatial representation and reasoning, which is fundamental for any agent operating in a physical world. My particular interest is qualitative spatial representation and reasoning and I am known as one of the founders of this field. My present focus is mostly on *using* such calculi for activity modelling and as the representation language for machine learning of activity models, exploiting and developing a variety of machine learning techniques. I have also become interested in grounding language in vision, in particular relating to unsupervised learning of the perceptual semantics of spatial language and activity descriptions.
Professor Simon Colton, Department of Computing, Goldsmiths College, University of London

Topic: "Computational Creativity in Human Society "

Human-like Simulating creative behaviours and producing artefacts of real cultural value has long been a prized goal in AI research, and has been studied intensively in the sub-field of Computational Creativity. As this area begins to draw in researchers from broader AI fields, and begins to make  an impact outside of academic research, it is worth reflecting on some lessons learned with respect to the way creative software can become a part of human cultures. In many respects, a celebration of creativity is actually a celebration of humanity itself, and some art forms, for instance poetry, serve - at least in part - to help people make connections to other people, over and above the value of the art itself. This raises questions about the role of creative software in such a context, and in the talk, I’ll highlight and try to address some stakeholder issues we  have faced with projects such as The Painting Fool (thepaintingfool.com), The WhatIf Machine  (whim-project.eu) and our latest offering, Gamika Technologies (metamakersinstitute.com).

Short biography:
Simon Colton is a Professor of Digital Game Technologies at Falmouth University, and part-time Professor of Computational Creativity in the Department of Computing at Goldsmiths College, University of London. He holds an ERA Chair and an EPSRC leadership fellowship, and was previously a Reader in Computational Creativity in the Department of Computing at Imperial College, London. He is an Artificial  Intelligence researcher, specialising in questions of Computational Creativity by developing and investigating  novel AI techniques and applying them to creative tasks in domains such as pure mathematics, graphic design, video game design, creative language and the visual arts. By taking an overview of creativity in such domains, he has added to the philosophical discussion of creativity, by addressing issues raised by the idea of autonomously creative software. This has enabled the driving forward of various formalisms aimed at bringing more rigour to the assessment of creativity in software. Prof. Colton has also advanced public engagement around issues of Computational Creativity through the development and public-deployment of creative software, which has led to the study of stakeholder issues in the field, and offers prospects for commercialisation projects.
Professor Ulrike Hahn, Department of Psychological Sciences, Birkbeck University of London

Topic: "Lessons for Human Like Computing from Cognitive Modelling "

The talk gives a brief overview of approaches to computational modelling within Cognitive Psychology and Cognitive Science, seeking to highlight relevant criteria of 'success'. The implications of this for human like computing are then discussed.

Short biography:
Professor of Psychology in the Department of Psychological Sciences, has been awarded the Alexander von Humboldt Foundation Anneliese Maier Research Award. This award is presented to world class researchers in the humanities and social sciences with the aim of encouraging collaboration between international researchers in Germany. Winners work on research projects funded for up to five years. Professor Hahn’s research investigates aspects of human cognition including argumentation, decision-making, concept acquisition, and language learning. Her work involves both experimentation and modelling. She is Director of the Centre for Cognition, Computation and Modelling which was launched earlier in 2013.
Dr Caroline Jay, School of School of Computer Science, University of Manchester

Topic: "Human-like software engineering"

Engineering software is a challenging endeavour. Development processes are incrementally improving, allowing us to construct increasingly complex artefacts, yet software continues to contain errors, or behave in unforeseen ways. This is partly to due to the 'unknown unknowns' introduced by a changing external environment, but it is also because algorithms often fail to work as expected: the formal representations underlying machine computation are frequently at odds with the heuristics used by the human brain. Observation of the programming process has resulted in huge technological advances. A notable example of this is locality of reference, a principle uncovered when trying to ascertain how to page data in and out of memory, which has gone on to touch virtually every aspect of modern systems. What we understand about human-machine interaction in software engineering remains limited, however, and progress in development has occurred primarily through craft-based iteration, rather than rigorous empirical study. Advances in hardware, such as parallel processing, have yet to achieve their full potential, as we struggle to translate serial human-written programs onto distributed architectures. Automated programming offer a means to reduce and repair errors, but even with machine-written programs, human input to a system means a bottleneck will always remain. As we move into the era of quantum computing and beyond, a true understanding of how our minds map themselves onto the machines we create is a vital component of achieving a step change in the creation and performance of software.

Short biography:
Caroline Jay is a Senior Lecturer in Empirically Sound Software Engineering in the School of Computer Science at the University of Manchester. She is qualified as both a Psychologist (BA, CPsychol) and Computer Scientist (MSc, PhD), and undertakes research crossing these domains. She is a Fellow of the Software Sustainability Institute, and an advocate for open and reproducible science. She is currently leading the 'Data Science Meets Creative Media’ project between the University of Manchester and BBC Research and Development.
Professor Pat Langley, Institute for the Study of Learning and Expertise (ISLE), Palo Alto, California

Topic: "Intelligent Behavior in Humans and Machines"

In this talk, I review the role of cognitive psychology in the origins
of artificial intelligence and in our pursuit of AI's initial objectives.
I examine how many key ideas about representation, performance, and learning had their inception in computational models of human cognition, and I argue that this approach to developing intelligent systems, although no longer common, has an important place in the field. Not only will research in this paradigm help us better understand human mental abilities, but findings from psychology can serve as useful heuristics to guide our search for intelligent artifacts. I also claim that another psychological notion - cognitive architecture - is especially relevant to developing unified theories of the mind and integrated intelligent systems.

Short biography:
Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise and as Honorary Professor of Computer Science at the University of Auckland. He has contributed actively to artificial intelligence and cognitive science for over 35 years, he was founding Executive Editor of Machine Learning, and he is currently Editor for Advances in Cognitive Systems. His ongoing research focuses on induction of explanatory scientific models and on architectures for intelligent agents.
DenisMareschal Professor Denis Mareschal, Centre for Brain and Cognitive Development, School of Psychology, Birkbeck College

Topic: "Constraints on Children’s Learning Across Development"

Since the seminal work of Piaget we have understood that children differ in the way they approach learning and problem solving, depending on their age. Debates have focussed on whether learning was qualitatively different across ages, or rather, whether the same basic mechanisms operated at different ages, but with ever-greater world knowledge with increasing age. With this context in mind, I will discuss two major factors impacting on the very impressive early human learning: (1) one-shot learning (or fast mapping), whereby children appear to learn words or concepts robustly from a single exposure, (2) socially guided learning, whereby children learn best from trustworthy conspecific agents. In each case, I will illustrate the impressive power of these two inductive constraints, but also give examples of where they fall down and of the impressive limitations of children’s learning. I will argue that these two forms of inductive biases arise from general learning and orienting mechanisms rather than specialised, human-specific, modules.

Short biography:
Denis Mareschal is Professor of Psychology and Director of the Centre for Brain and Cognitive Development, at Birkbeck University of London. His first degree was in Natural Sciences (Physics and Theoretical Physics) from Cambridge University, after which he obtained an MA in psychology from McGill University, followed by a DPhil in Psychology from Oxford University.  His research centers on developing mechanistic models of perceptual and cognitive development in infancy and childhood. His work combines computational modelling, behavioural experiments and neuroimaging to elucidate the mechanisms underlining human learning as it unfolds across child development. He has published over 80 refereed journal article and 4 monographs, including most recently Educational Neuroscience published by OUP. He has received the Marr prize from the Cognitive Science Society (USA), the Young Investigator Award from the International Society on Infant Studies (USA), and the Margaret Donaldson Prize from the British Psychological Society, as well as the Queen's Anniversary Prize for Higher and Further Education, and a Royal Society-Wolfson research merit award. He is a fellow of the British Psychological Society and the American Association of Psychological Sciences, and served for 8 years as Editor-in-Chief of Developmental Science, the leading journal of scientific developmental psychology.
Professor Stephen Muggleton, Department of Computing, Imperial college London

Topic: "Human-machine learning"

Traditionally Machine Learning has been seen as an area in which computer programs are used automatically to devise a prediction function on the basis of large quantities of data. In this talk we will argue that the properties of such computational systems differ radically from those of human learning, which, unlike Machine Learning, progresses incrementally over a lifetime and involve building structured modules which allow multi-modal integration of sensors, motor actions and high-level plans. Consequently there has been little research to date on the topic of how to effectively integrate human and machine learning for tasks which involve effective collaboration between computers and machine agents which learn symmetrically from each other. In this talk we will explore the requirements in this case for what we will call Human-Machine Learning, and some of the ongoing research relevant to this topic.

Short biography:
Stephen Muggleton is Professor of Machine Learning in the Department of Computing at Imperial College London and is internationally recognised as the founder of the field of Inductive Logic Programming. SM’s career has concentrated on the development of theory, implementations and applications of Machine Learning, particularly in the field of Inductive Logic Programming (ILP) and Probabilistic ILP (PILP). Over the last decade he has collaborated with biological colleagues, such as Prof Mike Sternberg, on applications of Machine Learning to Biological prediction tasks. SM’s group is situated within the Department of Computing and specialises in the development of novel general-purpose machine learning algorithms, and their application to biological prediction tasks. Widely applied software developed by the group includes the ILP system Progol (publication has over 1600 citations on Google Scholar) as well as a family of related systems including ASE-Progol (used in the Robot Scientist project), Metagol and Golem.
Professor Stephen Payne, Department of Computer Science , University of Bath

Topic: "Sensemaking"

Sensemaking  refers to the behavioural and cognitive processes required to find, collect and understand wide-ranging information about a complex multi-faceted topic. In library and information studies, the term Sensemaking has been used to broaden the conception of human knowledge, so as to incorporate dynamic and collaborative processes (Dervin, 1998). In cognitive science and human-computer interaction, Sensemaking has been used to label a process of schema-formation that is distributed in time and across people and devices (Russell et al, 1993). In this talk I will draw closer links between Sensemaking and the psychology of comprehension, and review some pertinent laboratory studies, so as to ask finer-grained questions about the cognitive capabilities that allow sensemaking in a world where there is typically more information available than can be read and understood.

Short biography:
Professor Stephen Payne is an academic cognitive scientist with a particular interest in human-computer interaction, currently Professor of Human-Centric Systems in the Department of Computer Science at the University of Bath. Research interests in cognitive science and human-computer interaction. Currently researching how individuals allocate time and effort across multiple tasks; how technology supports and shapes collaborative problem solving and the formation and maintenance of friendships; emotional and motivational constraints on the exploration of novel interactive services.
Alex Polozov,  Computer Science & Engineering Department, University of Washington, Seattle

Topic: "Automated Program Synthesis"

Program synthesis is the task of automatically finding a program in the underlying programming language that accomplishes the user's intent, given in a form of some specification. Despite being a golden dream of software engineering for decades, it gained significant traction only in the last 15 years, when novel search algorithms, achievements in SAT solving, and Moore's law made many non-trivial synthesis problems tractable. Since then, program synthesis has been successfully applied to numerous domains, including tutoring systems, data cleaning, task automation, robotics, and even discovering biological phenomena. Methods of program synthesis are traditionally categorized by (a) the form of intent specification that they accept, and (b) the underlying search algorithm. The challenge of the former lies in ambiguity: the specification often communicates only partial intent, and synthesizers need intuitive user interaction models to arrive at the correct program. The challenge of the later lies in navigating an enormous space of possible candidate programs in the language. In this talk, I will give a high-level overview of the field of program synthesis, its most prominent challenges, the most popular techniques, and some influential applications.

Short biography:
Alex Polozov is a graduate student at University of Washington in Seattle, USA, and a founding member of the Microsoft Program Synthesis by Examples (PROSE) group. His work includes inductive program synthesis, its applications to data wrangling, intelligent tutoring systems, and software engineering, as well as investigating human-computer interaction in the context of automatic learning systems. He is interested in combining symbolic and stochastic approaches to fundamental AI, and integrating domain-specific reasoning into machine learning algorithms. Together with the PROSE group, Alex is building an algorithmic framework that powers numerous programming-by-example features in Microsoft products, including Excel, Cortana, and Azure services.
Professor Yvonne Rogers, Department of Computer Science, University College London

Topic: "Human-Centred Data: Beyond AI "

Artificial intelligence (AI) is back in ascendancy. Without question it is an exciting time to be working in AI. Deep learning is very much at the heart of this renaissance; enabling core AI research areas, such as natural language processing and computer vision, to make significant strides, developing more accurate classification and recognition techniques. A diversity of areas, including advertising, search, security, media filtering, social media profiling, logistics, and content curation are benefiting from the application of the new generation of algorithms. So far, much of the focus in AI has been on the artificial - making machines smarter – with some adverse publicity arising as a result. For example, modeling interaction with users in order to optimize presentation of content has treated the users as passive subjects that should be persuaded to click through to the proposed pages. Humans are more often left out of the loop in the push for ever more optimization and efficiency.  But the HCI community argues the opposite: they should be viewed as central to tech development. A core concern is how best to optimize synergy in our interactions not efficiency. In my talk, I will introduce the research we have been doing on data: shifting from an automated data science perspective to a human-centered one.

Short biography:
Yvonne Rogers is a Professor of Interaction Design, the director of UCLIC and a deputy head of the Computer Science department at UCL. Her research interests are in the areas of ubiquitous computing, interaction design and human-computer interaction. A central theme is how to design interactive technologies that can enhance life by augmenting and extending everyday, learning and work activities. This involves informing, building and evaluating novel user experiences through creating and assembling a diversity of pervasive technologies. Yvonne is the PI at UCL for the Intel Collaborative Research Institute on Sustainable Connected Cities which was launched in October 2012 as a joint collaboration with Imperial College. She was awarded a prestigious EPSRC dream fellowship rethinking the relationship between ageing, computing and creativity. Food for Thought: Thought for Food is the result of a workshop arising from it, comprising a number of resources, including a short documentary and the participant's reflections on dining, design and novel technology. She is a visiting professor in the Psychology Department at Sussex University and an honorary professor in the Computer Science department at the University of Cape Town.
ClaudeSammut Professor Claude Sammut, Computer Science and Engineering at the University of New South Wales

Topic: "Logic-based robotics"

Robot software architectures are often characterised as hierarchical systems where the lower layers handle motor control and feature extraction from sensors, and the higher layers deal with problem solving and planning. The lower layers usually deal with continuous, noisy data at short time scales, whereas the upper layers work on longest time scales and treat the world as being more discrete and predictable. Early attempts at building integrated robot systems focussed more on the higher levels but often failed because they were unable to handle the  uncertainty inherent in the physical world. Recent progress in robotics owes much to the development of probabilistic and behaviour based methods that overcome some of the shortcomings of the early  approaches. However, high level symbolic reasoning and learning still have important roles to play. We describe our work on hierarchical robot software architectures that combine symbolic and sub-symbolic methods for learning complex behaviours. Relational learning is used to acquire an abstract model of robot actions that is then used to constrain sub-symbolic learning for low-level control. Models can be variously expressed in the classical STRIPS representation, as qualitative models or as teleo-reactive programs. The talk will give examples of  each in the context of the RoboCup Rescue and the RoboCup Standard Platform competitions.

Short biography:
Claude Sammut is a Professor of Computer Science and Engineering at the University of New South Wales. His early work on relational learning helped to the lay the foundations for the field of Inductive Logic Programming (ILP). With Donald Michie, he also did pioneering work in Behavioural Cloning. His current work is focussed on learning in robotics. He is a mentor for the UNSW teams that have been RoboCup champions five times in the Standard Platform league and the teams that won the award for best autonomous robot at RoboCup Rescue three times. In 2012, he was elected to the board of trustees of the RoboCup Federation and is the general chair of RoboCup 2019, to be held in Sydney.  He is also is co-editor-in-chief of Springer's Encyclopaedia of Machine Learning and Data Mining.
Amanda Seed
Dr Amanda Seed, School of Psychology and Neuroscience, University of St Andrews

Topic: "What cognitive mechanisms underpin social and physical problem-solving in non-verbal creatures? searching for the conceptual middle-ground"

Recent work in comparative psychology has revealed problem-solving abilities in some large-brained species of animal such as apes, monkeys, corvids and elephants that seem to defy explanation from traditional models of associative learning.  I will provide some examples from studies of theory-of mind and physical problem-solving.  The difficulty in interpreting these findings lies with the fact that often subjects fall short of the kinds of solutions adult humans would be expected to find.  Applying labels from human cognitive psychology to explain the performance of animals (such as causal reasoning, or intention understanding) has therefore met with reasonable resistance.  Explanations of the third kind (Call & Tomasello, 2005) try to find a middle ground between these extremes, but lack theoretical models that specify the cognitive mechanisms involved. I will describe two lines of research aimed at addressing this problem: one an AHRC-funded research project on ‘re-thinking mind and meaning’ that is trying to grapple with conceptual issues such as the distinction between implicit vs. explicit thinking; and another an ERC-funded project trying to apply a Bayesian modelling approach to move beyond null-hypothesis testing in comparative psychology.

Short biography:
Amanda Seed is a comparative and developmental psychologist studying the evolution of cognition, in particular causal reasoning, episodic thinking and executive function in primates and children. She was recently awarded an ERC Starting Grant to explore the relationship between some of these different cognitive skills and how they combine to affect performance on problem-solving tasks.  The motivation for this research is to shed light on the evolutionary changes in representational, mnemonic and executive processes that marked the origins of uniquely human thinking. Amanda is a Senior Lecturer at the School of Psychology and Neuroscience at the University of St Andrews where she is a member of the Centre for Social Learning and Cognitive Evolution, and the Scottish Primate Research Group.  She is the Director of the ‘Living Links to Human Evolution’ Centre at Edinburgh Zoo, where capuchin and squirrel monkeys take part in cognitive experiments in full view of the visiting public, with accompanying displays for public engagement with science.
Professor Mark Steedman, School of Informatics, University of Edinburgh

Topic: "Computational linguistics and artificial intelligence"

There is a long tradition associating language and other serial cognitive behavior with an underlying motor planning mechanism (Piaget 1936, Lashley 1951, Miller et al. 1960, passim). The evidence is evolutionary, neurophysiological, and developmental. It suggests that language is much more closely related to embodied cognition than current linguistic theories of grammar suggest. The talk argues that practically every aspect of language reflects this connection transparently. Building on planning formalisms developed in Robotics and AI, with some attention to applicable machine learning techniques, two basic operation corresponding to seriation and affordance will be shown to provide the basis for both plan-composition in animals, and long-range dependency in human language, of the kind found in constructions like relative clauses and coordination. A connection this direct raises a further obvious question: If language is so closely related to animal planning, why don't any other animals have language? The talk will further argue that the specific requirements of human collaborative planning, involving actions like helping and promising that depend on an understanding of other minds that has been found to be lacking in other animals, provides a distinctively semantic precursor for recursive aspects distinguishing human language from animal communication. It will show that the automaton that is minimally necessary to conduct search for collaborative plans, which is of only slightly greater generality than the push-down automaton, is exactly the automaton that also appears to characterize the parsing problem for natural languages.

Short biography:
A computational linguist and cognitive scientist. Professor Steedman graduated from the University of Sussex in 1968, with a B.Sc. in Experimental Psychology, and from the University of Edinburgh in 1973, with a Ph.D. in Artificial Intelligence.
He has held posts as Lecturer in Psychology, University of Warwick (1977–83); Lecturer and Reader in Computational Linguistics, University of Edinburgh (1983-8); Associate and full Professor in Computer and Information Sciences, University of Pennsylvania (1988–98). He has held visiting positions at the University of Texas at Austin, the Max Planck Institute for Psycholinguistics, Nijmegen, and the University of Pennsylvania, Philadelphia. Professor Steedman currently holds the Chair of Cognitive Science in the School of Informatics at the University of Edinburgh (1998- ). He works in computational linguistics, artificial intelligence, and cognitive science, on Generation of Meaningful Intonation for Speech by Artificial Agents, Animated Conversation, The Communicative Use of Gesture, Tense and Aspect, and Combinatory Categorial Grammar (CCG). He is also interested in Computational Musical Analysis and Combinatory Logic.
Professor Josh Tenenbaum, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology

Topic: "Building machines that see, learn and think like people: Probabilistic programs and program induction"

Many recent successes in computer vision, machine learning and other areas of artificial intelligence have been driven by methods for sophisticated pattern recognition, such as deep neural networks.  But human intelligence is more than just pattern recognition.  In particular, it depends on a suite of cognitive capacities for modeling the world: for explaining and understanding what we see, imagining things we could see but haven’t yet, solving problems and planning actions to make these things real, and building new models as we learn more about the world. I will talk about how we are beginning to capture these distinctively human capacities in computational models using the tools of probabilistic programs and program induction, embedded in a Bayesian framework for inference from data. These models help to explain how humans can perceive rich three-dimensional structure in visual scenes and objects, perceive and predict objects' motion based on their intrinsic physical characteristics, and learn new visual object concepts from just one or a few examples.  

Short biography:
Professor Josh Tenenbaum studies learning, reasoning and perception in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. His current work focuses on building probabilistic models to explain how people come to be able to learn new concepts from very sparse data, how we learn to learn, and the nature and origins of people's intuitive theories about the physical and social worlds. Professor of Computational Cognitive Science in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology and is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his Ph.D. from MIT in 1999, and was a member of the Stanford University faculty in Psychology and (by courtesy) Computer Science from 1999 to 2002. His papers have received awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, IJCAI and Cognitive Science Society conferences. He is the recipient of early career awards from the Society for Mathematical Psychology (2005), the Society of Experimental Psychologists, and the American Psychological Association (2008), and the Troland Research Award from the National Academy of Sciences (2011)
ManosTsakiris Professor Manos Tsakiris, Department of Psychology, Royal Holloway University of London

Topic: "The Multisensory Basis of the Self"

By grounding the self in the body, experimental psychology has taken the body as the starting point for a science of the self. One fundamental dimension of the bodily self is the sense of body ownership that refers to the special perceptual status of one's own body, the feeling that "my body" belongs to me. The primary aim of this talk is to highlight recent advances in the study of body ownership and our understanding of the underlying neurocognitive processes in three ways. I first consider how the sense of body ownership has been investigated and elucidated in the context of multisensory integration. Beyond exteroception, recent studies have considered how this exteroceptively driven sense of body ownership can be linked to the other side of embodiment, that of the unobservable, yet felt, interoceptive body, suggesting that these two sides of embodiment interact to provide a unifying bodily self. Lastly, the multisensorial understanding of the self has been shown to have implications for our understanding of social relationships, especially in the context of self-other boundaries. Taken together, these three research strands motivate a unified model of the self inspired by current predictive coding models.

Short biography:
Manos Tsakiris studied psychology and philosophy before completing his PhD in psychology and cognitive neurosciences at the Institute of Cognitive Neuroscience, UCL. He is currently Professor of Psychology at the Department of Psychology, Royal Holloway, University of London where he investigates the neurocognitive mechanisms that shape the experience of embodiment and self-identity. He is the recipient of the 2014 Young Mind and Brain Prize  and of the 22nd Experimental Psychology Society Prize.