It’s said that Henry Ford’s customers wanted “a faster horse”. If Henry Ford was selling us artificial intelligence today, what would the customer call for, “a smarter human”? That’s certainly the picture of machine intelligence we find in science fiction narratives, but the reality of what we’ve developed is far more mundane.

Car engines produce prodigious power from petrol. Machine intelligences deliver decisions derived from data. In both cases the scale of consumption enables a speed of operation that is far beyond the capabilities of their natural counterparts. Unfettered energy consumption has consequences in the form of climate change. Does unbridled data consumption also have consequences for us?

If we devolve decision making to machines, we depend on those machines to accommodate our needs. If we don’t understand how those machines operate, we lose control over our destiny. Much of the debate around AI makes the mistake of seeing machine intelligence as a reflection of our intelligence. In this talk we argue that to control the machine we need to understand the machine, but to understand the machine we first need to understand ourselves.

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge where he leads the University’s flagship mission on AI, AI@Cam. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the physical world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. He is interim chair of the advisory board of the UK’s Centre for Data Ethics and Innovation and a member of the UK’s AI Council. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.

THE STRACHEY LECTURES ARE GENEROUSLY SUPPORTED BY OxFORD ASSET MANAGEMENT

An introduction to algorithmic aspects of symmetry and similarity, ranging from the fundamental complexity theoretic "Graph Isomorphism Problem" to applications in optimisation and machine learning Symmetry is a fundamental concept in mathematics, science and engineering, and beyond. Understanding symmetries is often crucial for understanding structures. In computer science, we are mainly interested in the symmetries of combinatorial structures. Computing the symmetries of such a structure is essentially the same as deciding whether two structures are the same ("isomorphic"). Algorithmically, this is a difficult task that has received a lot of attention since the early days of computing. It is a major open problem in theoretical computer science to determine the precise computational complexity of this "Graph Isomorphism Problem".

An overview of work on probabilistic soft logic (PSL), an SRL framework for large-scale collective, probabilistic reasoning in relational domains and a description of recent work which integrates neural and symbolic (NeSy) reasoning. Our ability to collect, manipulate, analyze, and act on vast amounts of data is having a profound impact on all aspects of society. Much of this data is heterogeneous in nature and interlinked in a myriad of complex ways. From information integration to scientific discovery to computational social science, we need machine learning methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. Statistical relational learning (SRL) is a subfield that builds on principles from probability theory and statistics to address uncertainty while incorporating tools from knowledge representation and logic to represent structure. In this talk, I’ll overview our work on probabilistic soft logic (PSL), an SRL framework for large-scale collective, probabilistic reasoning in relational domains. I’ll also describe recent work which integrates neural and symbolic (NeSy) reasoning. I’ll close by highlighting emerging opportunities (and challenges!) in realizing the effectiveness of data and structure for knowledge discovery.

Bio:

Lise Getoor is a Distinguished Professor in the Computer Science & Engineering Department at UC Santa Cruz, where she holds the Jack Baskin Endowed Chair in Computer Engineering. She is founding Director of the UC Santa Cruz Data Science Research Center and is a Fellow of ACM, AAAI, and IEEE. Her research areas include machine learning and reasoning under uncertainty and she has extensive experience with machine learning and probabilistic modeling methods for graph and network data. She has over 250 publications including 13 best paper awards. She has served as an elected board member of the International Machine Learning Society, on the Computing Research Association (CRA) Board, as Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, JAIR Associate Editor, and on the AAAI Executive Council.. She is a Distinguished Alumna of the UC Santa Barbara Computer Science Department and received the UC Santa Cruz Women in Science & Engineering (WISE) award. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a professor at the University of Maryland, College Park from 2001-2013.

THE STRACHEY LECTURES ARE GENEROUSLY SUPPORTED BY OxFORD ASSET MANAGEMENT

There has been a proliferation of technological developments in the last few years that are beginning to improve how we perceive, attend to, notice, analyse and remember events, people, data and other information. There has been a proliferation of technological developments in the last few years that are beginning to improve how we perceive, attend to, notice, analyse and remember events, people, data and other information. These include machine learning, computer vision, advanced user interfaces (e.g. augmented reality) and sensor technologies. A goal of being augmented with ever more computational capabilities is to enable us to see more and, in doing so, make more intelligent decisions. But to what extent are the new interfaces enabling us to become more super-human? What is gained and lost through our reliance on ever pervasive computational technology? In my lecture, I will cover latest developments in technological advances, such as conversational interfaces, data visualisation, and augmented reality. I will then draw upon relevant recent findings in the HCI and cognitive science literature that demonstrate how our human capabilities are being extended but also struggling to adapt to the new demands on our attention. Finally, I will show their relevance to investigating the physical and digital worlds when trying to discover or uncover new information.

Mixed Signals: audio and wearable data analysis for health diagnostics Wearable and mobile devices are very good proxies for human behaviour. Yet, making the inference from the raw sensor data to individuals’ behaviour remains difficult. The list of challenges is very long: from collecting the right data and using the right sensor, respecting resource constraints, identifying the right analysis techniques, labelling the data, limiting privacy invasion, to dealing with heterogeneous data sources and adapting to changes in behaviour.

The advantages of computing for society are tremendous. But while new technological developments emerge, we also witness a number disadvantages and unwanted side-effects. The advantages of computing for society are tremendous. But while new technological developments emerge, we also witness a number disadvantages and unwanted side-effects: from the speed with which fake news spreads to the formation of new echo-chambers and the enhancement of polarization in society. It is time to reflect upon the successes and failures of collective rationality, particularly as embodied in modern mechanisms for mass information-aggregation and information-exchange. What can the study of the social and epistemic benefits and costs, posed by various contemporary mechanisms for information exchange and belief aggregation, tell us? I will use Logic and Philosophy to shed some light on this topic. Ultimately we look for an answer to the question of how we can ensure that truth survives the information age?

As AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. As AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. This requires AI systems to exhibit behavior that is explainable to humans. Synthesizing such behavior requires AI systems to reason not only with their own models of the task at hand, but also about the mental models of the human collaborators. At a minimum, AI agents need approximations of human’s task and goal models, as well as the human’s model of the AI agent’s task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), and be ready to provide customized explanations when needed. Using several case-studies from our ongoing research, I will discuss how such multi-model reasoning forms the basis for explainable behavior in human-aware AI systems.

Innovation is the main event of the modern age, the reason we experience both dramatic improvements in our living standards and unsettling changes in our society. Innovation is the main event of the modern age, the reason we experience both dramatic improvements in our living standards and unsettling changes in our society. Forget short-term symptoms like Donald Trump and Brexit, it is innovation itself that explains them and that will itself shape the 21st century for good and ill. Yet innovation remains a mysterious process, poorly understood by policy makers and businessmen, hard to summon into existence to order, yet inevitable and inexorable when it does happen.

Medicine and Physiology in the Age of Dynamics: Newton Abraham Lecture 2020 Lecture by Professor Alan Garfinkel (2019-2020 Newton Abraham Visiting Professor, University of Oxford, Professor of Medicine (Cardiology) and Integrative Biology and Physiology, University of California, Los Angeles)

Medicine and Physiology in the Age of Dynamics: Newton Abraham Lecture 2020 Lecture by Professor Alan Garfinkel (2019-2020 Newton Abraham Visiting Professor, University of Oxford, Professor of Medicine (Cardiology) and Integrative Biology and Physiology, University of California, Los Angeles)