© Oxford University
This series is host to episodes created by the Department of Computer Science, University of Oxford, one of the longestestablished Computer Science departments in the country.
The series reflects this department's worldclass research and teaching by providing talks that encompass topics such as computational biology, quantum computing, computational linguistics, information systems, software verification, and software engineering.
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Fri, 17 Mar 2017 10:59:09 +0000
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Computer Science
Oxford University
Oxford University
podcasts@it.ox.ac.uk
no
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Computer Science
http://podcasts.ox.ac.uk

1
modelling
computer science
Bayesian
machine learning
Strachey Lectures
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.
http://rss.oucs.ox.ac.uk/tag:20170317:105909:000:file:299141:video
http://media.podcasts.ox.ac.uk/comlab/comsci/20170307_comsci_strachey2017ghahramani.mp4
Professor Zoubin Ghahramani gives a talk on probabilistic modelling from it's foundations to current areas of research at the frontiers of machine learning.
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.
modelling,computer science,Bayesian,machine learning,Strachey Lectures,20170307
Zoubin Ghahramani
3044
Wed, 15 Mar 2017 13:44:51 +0000

2
computing
students
computer science
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.
http://rss.oucs.ox.ac.uk/tag:20161108:152552:000:file:297811:video
http://media.podcasts.ox.ac.uk/comlab/comsci/20160511comscistudentdesignpracticals.mp4
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.
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.
computing,students,computer science
Computer Science Students
119
Tue, 08 Nov 2016 15:25:52 +0000

3
computer science
cryptography
universal machine
artificial intelligence
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 farreaching 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.
http://rss.oucs.ox.ac.uk/tag:20161102:125856:000:file:297754:video
http://media.podcasts.ox.ac.uk/comlab/comsci/20161031_comsci_hodges720p.mp4
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.
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 farreaching 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.
computer science,cryptography,universal machine,artificial intelligence,20161031
Andrew Hodges, Mike Wooldridge
4042
Wed, 02 Nov 2016 12:49:49 +0000

4
quantum physics
quantum computing
science
computing
technology
Dr Scott Aaronson (MIT, UT Austin) gives the 2016 Strachey lecture. In the near future, it will likely become possible to perform specialpurpose 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 milestonedecisively answering quantum computing skeptics, while casting doubt on one of the foundational tenets of computer science, the Extended ChurchTuring 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.
http://rss.oucs.ox.ac.uk/tag:20160614:111445:000:file:296065:video
http://media.podcasts.ox.ac.uk/comlab/comsci/20160524comsciaaronson720p.mp4
Dr Scott Aaronson (MIT, UT Austin) gives the 2016 Strachey lecture. Creative Commons AttributionNonCommercialShare Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/byncsa/2.0/uk/
http://creativecommons.org/licenses/byncsa/2.0/uk/
Dr Scott Aaronson (MIT, UT Austin) gives the 2016 Strachey lecture. In the near future, it will likely become possible to perform specialpurpose 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 milestonedecisively answering quantum computing skeptics, while casting doubt on one of the foundational tenets of computer science, the Extended ChurchTuring 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.
Creative Commons AttributionNonCommercialShare Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/byncsa/2.0/uk/
quantum physics,quantum computing,science,computing,technology
Scott Aaronson
4321
Tue, 14 Jun 2016 11:14:45 +0100

5
artificial intelligence
ai
Google
Deep Mind
computer science
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 CoFounder 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.
http://rss.oucs.ox.ac.uk/tag:20160302:110935:000:file:294813:video
http://media.podcasts.ox.ac.uk/comlab/comsci/20160224stracheylecturenoq+a720p.mp4
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.
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 CoFounder 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.
artificial intelligence,ai,Google,Deep Mind,computer science,20160224
Demis Hassabis
3308
Fri, 26 Feb 2016 14:37:13 +0000

6
computer science
BX
viewupdate
lens
monad
modeldriven development
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, nondeterminism, and exceptions. We briefly outline the construction.
http://rss.oucs.ox.ac.uk/tag:20151117:144252:000:file:291751:video
http://media.podcasts.ox.ac.uk/comlab/comsci/20151028_comscigibbons.mp4
A reconstruction (slides and voiceover) of a talk given at the Summit on Advances in Programming Languages (snapl.org/2015) in May 2015. Creative Commons AttributionNonCommercialShare Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/byncsa/2.0/uk/
http://creativecommons.org/licenses/byncsa/2.0/uk/
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, nondeterminism, and exceptions. We briefly outline the construction. Creative Commons AttributionNonCommercialShare Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/byncsa/2.0/uk/
computer science,BX,viewupdate,lens,monad,modeldriven development,20151028
Jeremy Gibbons
316
Tue, 17 Nov 2015 14:29:46 +0000