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Computer Science

Author: Oxford University

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This series is host to episodes created by the Department of Computer Science, University of Oxford, one of the longest-established Computer Science departments in the country.

The series reflects this department's world-class research and teaching by providing talks that encompass topics such as computational biology, quantum computing, computational linguistics, information systems, software verification, and software engineering.


12 Episodes
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Professor Leslie Kaelbling (MIT) gives the 2019 Stachey lecture. The Strachey Lectures are generously supported by OxFORD Asset Management. We, as robot engineers, have to think hard about our role in the design of robots and how it interacts with learning, both in 'the factory' (that is, at engineering time) and in 'the wild' (that is, when the robot is delivered to a customer). I will share some general thoughts about the strategies for robot design and then talk in detail about some work I have been involved in, both in the design of an overall architecture for an intelligent robot and in strategies for learning to integrate new skills into the repertoire of an already competent robot.
Why has AI been so hard and what are the problems that we might work on in order to make real progress to human level intelligence, or even the super intelligence that many pundits believe is just around the corner? In his 1950 paper "Computing Machinery and Intelligence" Alan Turing estimated that sixty people working for fifty years should be able to program a computer (running at 1950 speed) to have human level intelligence. AI researchers have spent orders of magnitude more effort than that and are still not close. Why has AI been so hard and what are the problems that we might work on in order to make real progress to human level intelligence, or even the super intelligence that many pundits believe is just around the corner? This talk will discuss those steps we can take, what aspects we really still do not have much of a clue about, what we might be currently getting completely wrong, and why it all could be centuries away. Importantly the talk will make distinctions between research questions and barriers to technology adoption from research results, with a little speculation on things that might go wrong (spoiler alert: it is the mundane that will have the big consequences, not the Hollywood scenarios that the press and some academics love to talk about).
This talk is about the experience of providing privacy when running analytics on users’ personal data. The two-sided market of Cloud Analytics emerged almost accidentally, initially from click-through associated with user's response to search results, and then adopted by many other services, whether web mail or social media. The business model seen by the user is of a free service (storage and tools for photos, video, social media etc). The value to the provider is untrammeled access to the user's data over space and time, allowing upfront income from the ability to run recommenders and targeted adverts, to background market research about who is interested in what information, goods and services, when and where. The value to the user is increased personalisation. This all comes at a cost, both of privacy (and the risk of loss of reputation or even money) for the user, and at the price of running highly expensive data centers for the providers, and increased cost in bandwidth and energy consumption (mobile network costs & device battery life). The attack surface of our lives expands to cover just about everything. This talk will examine several alternative directions that this will evolve in the future. Firstly, we look at a toolchain for traditional cloud processing which offers privacy through careful control of the lifecycle of access to data, processing, and production of results by combining several relatively new techniques. Secondly, we present a fully decentralized approach, on low cost home devices, which can potentially lead to large reduction in risks of loss of confidentiality.
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?
Éva Tardos, Department of Computer Science, Cornell University, gives the 2017 Ada Lovelace Lecture on 6th June 2017. Selfish behaviour can often lead to suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade we developed good understanding on how to quantify the impact of strategic user behaviour on the overall performance in many games (including traffic routing as well as online auctions). In this talk we will focus on games where players use a form of learning that helps themadapt to the environment, and consider two closely related questions: What are broad classes of learning behaviours that guarantee that game outcomes converge to the quality guaranteed by the price of anarchy, and how fast is this convergence. Or asking these questions more broadly: what learning guarantees high social welfare in games, when the game or the population of players is dynamically changing.
Professor Kraus will show how combining machine learning techniques for human modelling, human behavioural models, formal decision-making and game theory approaches enables agents to interact well with people. Automated agents that interact proficiently with people can be useful in supporting, training or replacing people in complex tasks. The inclusion of people presents novel problems for the design of automated agents’ strategies. People do not necessarily adhere to the optimal, monolithic strategies that can be derived analytically. Their behaviour is affected by a multitude of social and psychological factors.  In this talk I will show how combining machine learning techniques for human modelling, human behavioural models, formal decision-making and game theory approaches enables agents to interact well with people. Applications include intelligent agents. The Strachey Lectures are generously supported by OxFORD Asset Management.
Professor Zoubin Ghahramani gives a talk on probabilistic modelling from it's foundations to current areas of research at the frontiers of machine learning. Probabilistic modelling provides a mathematical framework for understanding what learning is, and has therefore emerged as one of the principal approaches for designing computer algorithms that learn from data acquired through experience. Professor Ghahramani will review the foundations of this field, from basics to Bayesian nonparametric models and scalable inference. He will then highlight some current areas of research at the frontiers of machine learning, leading up to topics such as probabilistic programming, Bayesian optimisation, the rational allocation of computational resources, and the Automatic Statistician.The Strachey lectures are generously supported by OxFORD Asset Management.
Students undertaking undergraduate (first) degrees in Computer Science, Computer Science & Philosophy and Maths & Computer Science undertake a Group Design Practical as a compulsory part of the course. The Group Design Practical, which runs from January, sees teams of four to six undergraduate students battling it out with their chosen project. Many of the challenges having been set, or sponsored by industry partners, which in 2016 included Research, Oxford Asset Management, Bloomberg and Metaswitch. The students’ work culminated in an exhibition and formal presentation, held in the Department on 9 May. In the video current students discuss their experiences of the Group Design Practical.
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.
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