In this terms Strachey lecture, Professor Monika Henzinger gives an introduction to differential privacy with an emphasis on differential private algorithms that can handle changing input data. Decisions are increasingly automated using rules that were learnt from personal data. Thus, it is important to guarantee that the privacy of the data is protected during the learning process. To formalize the notion of an algorithm that protects the privacy of its data, differential privacy was introduced. It is a rigorous mathematical definition to analyze the privacy properties of an algorithm – or the lack thereof. In this talk I will give an introduction to differential privacy with an emphasis on differential private algorithms that can handle changing input data. Monika Henzinger is a professor of Computer Science at the Institute of Science and Technology Austria (ISTA). She holds a PhD in computer science from Princeton University (New Jersey, USA), and has been the head of research at Google and a professor of computer science at EPFL and the University of Vienna. Monika Henzinger is an ACM and EATCS Fellow and a member of the Austrian Academy of Sciences and the German National Academy of Sciences Leopoldina. She has received several awards, including an honorary doctorate from TU Dortmund University, Two ERC Advanced Grant, the Leopoldina Carus Medal, and the Wittgensteinpreis, the highest science award of Austria. The Strachey Lectures are generously supported by OxFORD Asset Management

It’s said that Henry Ford’s customers wanted a “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

It’s said that Henry Ford’s customers wanted a “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

Stroustrup discusses the development and evolution of the C++, one of the most widely used programming languages ever. The development of C++ started in 1979. Since then, it has grown to be one of the most widely used programming languages ever, with an emphasis on demanding industrial uses. It was released commercially in 1985 and evolved through one informal standard (“the ARM”) and several ISO standards: C++98, C++11, C++14, and C++17. How could an underfinanced language without a corporate owner succeed like that? What are the key ideas and design principles? How did the original ideas survive almost 40 years of development and 30 years of attention from a 100+ member standards committee? What is the current state of C++ and what is likely to happen over the next few years? What are the problems we are trying to address through language evolution?

Professor Andrew Hodges author of 'Alan Turing: The Enigma' talks about Turing's work and ideas from the definition of computability, the universal machine to the prospect of Artificial Intelligence. In 1951, Christopher Strachey began his career in computing. He did so as a colleague of Alan Turing, who had inspired him with a 'Utopian' prospectus for programming. By that time, Turing had already made far-reaching and futuristic innovations, from the definition of computability and the universal machine to the prospect of Artificial Intelligence. This talk will describe the origins and impacts of these ideas, and how wartime codebreaking allowed theory to turn into practice. After 1951, Turing was no less innovative, applying computational techniques to mathematical biology. His sudden death in 1954 meant the loss of most of this work, and its rediscovery in modern times has only added to Turing's iconic status as a scientific visionary seeing far beyond his short life. Andrew Hodges is the author of Alan Turing: The Enigma (1983), which inspired the 2014 film The Imitation Game. The Strachey Lectures are generously supported by OxFORD Asset Management.

Dr Scott Aaronson (MIT, UT Austin) gives the 2016 Strachey lecture. In the near future, it will likely become possible to perform special-purpose quantum computations that, while not immediately useful for anything, are plausibly hard to simulate using a classical computer. These "quantum supremacy experiments" would be a scientific milestone---decisively answering quantum computing skeptics, while casting doubt on one of the foundational tenets of computer science, the Extended Church-Turing Thesis. At the same time, these experiments also raise fascinating questions for computational complexity theorists: for example, on what grounds should we believe that a given quantum system really is hard to simulate classically? Does classical simulation become easier as a quantum system becomes noisier? and how do we verify the results of such an experiment? In this lecture, I'll discuss recent results and open problems about these questions, using three proposed "quantum supremacy experiments" as examples: BosonSampling, IQP / commuting Hamiltonians, and random quantum circuits. Based partly on joint work with Alex Arkhipov and with Lijie Chen. The Strachey Lectures are generously supported by OxFORD Asset Management.

In this talk Demis Hassabis discuss's what is happening at the cutting edge of AI research, its future impact on fields such as science and healthcare, and how developing AI may help us better understand the human mind. Strachey Lecture 2016, generously supported by Oxford Asset Management. Dr. Demis Hassabis is the Co-Founder and CEO of DeepMind, the world’s leading General Artificial Intelligence (AI) company, which was acquired by Google in 2014 in their largest ever European acquisition. Demis will draw on his eclectic experiences as an AI researcher, neuroscientist and videogames designer to discuss what is happening at the cutting edge of AI research, its future impact on fields such as science and healthcare, and how developing AI may help us better understand the human mind.

A reconstruction (slides and voiceover) of a talk given at the Summit on Advances in Programming Languages (snapl.org/2015) in May 2015. Bidirectional transformations inherently involve state effects. Modelling them that way allows the incorporation of other effects too, such as I/O, non-determinism, and exceptions. We briefly outline the construction.