CW TEC 2017 - Artificial Intelligence: Underlying technologies – how they work and how they are applied

Brought to you by CW (Cambridge Wireless)

We will be focusing on the technologies underlying the burgeoning field of Artificial Intelligence. As seemingly limitless applications are increasingly discussed in the press we will look past this hype.

About the event

We will be focusing on the technologies underlying the burgeoning field of Artificial Intelligence. As seemingly limitless applications are increasingly discussed in the press we will look past this hype, focusing on three key themes:

  • What are the key and emerging AI technologies and how are they combined to drive current and future applications?
  • What are the trade-offs between the increasing number of tools and frameworks and how far can they really take us?
  • What are the implications for hardware, network infrastructure and storage - what are the limiting challenges, and what is on the horizon to make this more tractable?

The 3rd CW TEC is aimed at technology leaders in industry, as well as young engineers and data scientists, to give them an overview of the subject and to critically examine the associated challenges. Speakers will include leaders in AI research and development from universities and industry.

You can follow @CambWireless on Twitter and tweet about this event using #CWTEC.

Hosted by Digital Technology Group, Computer Laboratory, University of Cambridge

We are one of the research groups within the University of Cambridge Computer Laboratory.

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Sponsored by: Magna International

Magna is a leading global automotive supplier with 312 manufacturing operations and 93 product development, engineering and sales centres in 29 countries. Magna has over 155,000 employees focused on delivering superior value to its customers through innovative processes and World Class Manufacturing. At Magna, we take great ideas and develop them from innovation to industry standard. We also know that great thinking happens outside our four walls, and that our ability to commercialise great ideas benefits inventors, entrepreneurs, customers, and ultimately all who share the road.

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Sponsored by: Innovate UK

Innovate UK is the UK’s innovation agency. Innovate UK works with people, companies and partner organisations to find and drive the science and technology innovations that will grow the UK economy - delivering productivity, new jobs and exports. Our aim at Innovate UK is to keep the UK globally competitive in the race for future prosperity.

Supported by Prowler.io

PROWLER.io is the Cambridge-based creator of the first principled A.I. decision-making platform. Its world-class team of experts in probabilistic modelling, machine learning and multi-agent systems is building the platform on a foundation of interpretable principles of mathematics and decision theory. PROWLER.io empowers customers to optimise the millions of micro-decisions that can occur in complex, dynamic systems such as online games, autonomous vehicles and smart cities.

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Supported by: Myrtle Software Ltd

Myrtle accelerates performance critical workloads on FPGAs: devices that are currently being deployed at scale in data centers around the world. Myrtle has realized multiple proprietary deep learning networks as silicon designs so that they execute at a latency and power point that makes them usable in real-world situations. Myrtle is currently targeting its technology at inference workloads in data centers and is involved in a major collaboration to address the safety and verification challenges that currently preventing sophisticated deep learning networks being used in road vehicles.

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Agenda

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The information supplied below may be subject to change before the event.

09:30

Registration and networking with refreshments

10:00

Welcome

Welcome from CW (Cambridge Wireless)
Robert Driver, CEO, CW
Welcome from our host The Computer Labs
Dr Ian Wassell, Senior Lecturer, University of Cambridge Computer Laboratory
A word from our headline sponsor Magna
David Paul, Director, Corporate Engineering and R&D, Magna International

10:15

Scene setting

The Landscape of AI
Phil Claridge, Founder, Mandrel Systems
Peter Whale, Founder, Peter Whale Consulting
An introduction from the AI SIG Champions to the conference day, the key topics to be covered, the structure of the day, and some of the engineering design questions to be covered.

10:30

Key and emerging AI technologies:The near-term impact of AI


Professor Steve Young, Professor of Information Engineering, University of Cambridge
A review of the major algorithmic approaches and technological advances that are driving the current uptake of AI.

10:55

Key and emerging AI technologies:Under the covers of Deep Learning

Theophane Weber, Senior Research Scientist, Google DeepMind
What is Deep Learning? How is it different from classic neural nets? How is this informed from our understanding of the human brain? What is Deep Learning is good for and not so good for.

11:20

Refreshments and networking

11:50

Under the covers of a range of other AI technologies

Professor Carl Edward Rasmussen, Professor of Machine Learning, University of Cambridge and Chairman, PROWLER.io
This is the counter point to the session on Deep Learning, where we explain some of the other promising areas of AI such as Probabilistic models, Reinforcement Learning (RL) and Multi-agent Systems (MAS).

12:15

The revolution of speech recognition technology

Dr Tony Robinson, Founder & CTO, Speechmatics
Speech recognition technology is revolutionising the industry – but how do you make speech recognition work for you?

12:35

Panel Session with audience Q&A

12:55

A word from our sponsor PROWLER.io Vishal Chatrath, CEO and Co-founder, PROWLER.io

13:00

Lunch and networking

13:55

Tools, Frameworks and AI Systems Engineering: Where next for AI?

Professor Neil Lawrence, Professor of Machine Learning, University of Sheffield
Our current generation of artificial intelligence techniques are driven by data. But also we expect to be able to deploy artificial intelligence techniques on data. What does that mean, is it a contradiction? How will this effect the wider technology landscape? Is it simply a matter of refining deep neural nets? Or are more disruptive technologies needed? What will be the challenges of deploying AI systems?

14:20

Tools, Frameworks and AI Systems Engineering: Survey of Tools and Frameworks

Dan Neil, Lead Machine Learning Researcher, Benevolent AI
This talk will look at the capabilities of the increasing number of tools and frameworks for AI, and how far they can take us currently. We will then reflect on Benevolent's experience of area where we have needed to augment these with our own in-house tools.

14:40

Tools, Frameworks and AI Systems Engineering:NVIDIA’s Artificial Intelligence Platform

Alison B Lowndes, Artificial Intelligence Developer Relations, EMEA, NVIDIA
This talk will combine knowledge of world-wide state-of-the-art research, with NVIDIA’s ecosystem of software, research, training, support and of course hardware. Chips are one part of enabling AI, a field moving faster than chips can be produced. That momentum drives efficiency and forces agility, allowing us to enable AI across the world’s data centres, clouds & ‘at the edge’. Discussion will include the tools we opensource and optimize with partners across academia & Enterprise as well as hints along the path to AGI through neuroscience, and what that means for the real world.

15:00

Tools, Frameworks and AI Systems Engineering: Why do we need another processor (solution?) for AI

Simon Knowles, Co-founder & CTO, Graphcore Ltd.
This talk will cover how to design a processor for Machine Intelligence. It will describe the underlying compute workload in today's and future Machine Intelligence applications. It will show how current CPU and GPU processors are limited in their ability to support this new workload and how a new type of intelligence processing unit can be developed which is much more efficient for this fundamental new era of computers.

15:20

Processors for real-world AI

Dr Peter Baldwin, Founder, Myrtle Software; Dr David Page, Chief Scientist, Myrtle Software
A number of different processor architectures have been proposed for deployment in AI applications. In this talk, we discuss trade-offs of the alternative approaches for current and future workload

15:40

AI: open for all?

Panel session
Chaired by Sobia Hamid, Founder, Cambridge Data Insights
AI will have an increasing impact on business and wider society but can seem like it is driven by a very select set of people. This panel discussion explores the issues we need to resolve to have AI be open to all.

16:00

Refreshments and networking


16:30

Tools, Frameworks and AI Systems Engineering: System Architectures for AI

Anton Lokhmotov, CEO, dividiti
Keeping up with the fast pace of AI innovation calls for an agile system approach that engages the community in a virtuous co-design and optimisation cycle, where the design of AI applications is informed by the capabilities of computer systems and the design of computer systems is informed by AI applications.

16:50

Tools, Frameworks and AI Systems Engineering: System Architectures for AI

Jem Davies, Fellow and General Manager, Machine Learning, Arm Ltd
AI and Machine Learning are currently generating a huge number of headlines. As the near ubiquitous computing platform on devices, Arm has a unique view on the technology and the different implementation approaches needed to make it a success. As AI/ML workloads increase in number and complexity, Jem will discuss how Arm views the challenges, options and opportunities presented, and what is being done to address these new workloads.

17:30

Panel Session with audience Q&A

Chaired by James Chapman, VP Product Management, Qualcomm

17:30

AI on the Edge

Cyrus Vahid, Principal Solutions Architect, Amazon (AWS)
What drives the partitioning of edge v. cloud? How intelligent can the edge be and how intelligent do we want it to be? Does the edge device really learn?

17:55

Closing remarks

18:00

Event Close

Speakers

Ian Wassell - Senior Lecturer, Digital Technology Group, Computer Laboratory, University of Cambridge

Dr Ian Wassell joined the University of Cambridge Computer Laboratory as a Senior Lecturer in January 2006. Prior to this, he was with the Department of Engineering for six years. He received the PhD degree from the University of Southampton in 1990 and the BSc., BEng. (Honours) Degrees (First Class) from the University of Loughborough in 1983. He has in excess of 25 years experience in radio communication systems gained via positions in industry and academia and has published more than 200 papers. His research interests include broadband wireless networks, wireless sensor networks, radio propagation, coding, communication signal processing, compressive sampling, and image processing and classification.

David Paul - Director for Business Development, Magna International

David Paul has served as Director, Corporate Engineering and R&D since 2014. In this role David is responsible for identifying innovation opportunities for Magna from startups, SME’s, Universities, etc.

He has worked in the automotive industry for 38 years, beginning his career with an Engineering Apprenticeship then studied Mechanical Engineering at the University of Loughborough (sponsored by Jaguar Cars Ltd) and later became a core member of Jaguar’s design team for their first V8 engine. Upon leaving Jaguar in 1995, David joined Magna International’s Powertrain division and in 2007 set up his own Consultancy to support SME’s and Innovate UK in the low carbon vehicle sector, before rejoining Magna in 2014.

David Page - Chief Scientist, Myrtle Software Ltd

David has a background in mathematics and theoretical physics. He completed a PhD at Durham University and postdoctoral research at the University of Toronto. After a long period in industry managing Quantitative Analysis teams, he returned to research in the areas of machine learning and theoretical neuroscience. His interests are in understanding the type of algorithms that can allow intelligent agents to operate safely and effectively in complex, dynamic environments. This will require a deeper understanding of the statistical properties of learning algorithms, in order to guarantee robustness and safety, and also algorithmic advances to handle the rich problem solving capabilities needed for this kind of behaviour.

Sobia Hamid - Founder, Cambridge Data Insights

Sobia is founder of Cambridge Data Insights, helping organisations to improve performance through the application of AI and machine learning. Also founder of CancerPDX; a ‘Genetic Risk and Refer’ platform for the early identification and management of inherited cancers, Sobia previously worked as a Senior Investment Associate at Invoke Capital. She holds a PhD in Epigenetics from the University of Cambridge, and an MSc in Cognitive Neuroscience from Imperial College London.

Vishal Chatrath - Co-Founder, CEO, Prowler.io

Vishal Chatrath is CEO and co-founder of PROWLER.io, a Cambridge-based developer of principled AI decision-making technologies that help customers understand, guide and optimise the millions of micro-decisions that occur in complex, dynamic environments. Its world-class researchers and engineers are transforming fields like systems engineering, autonomous vehicles, game design, and smart city planning. Vishal was previously head of automotive at Nokia, founder of Chleon Automotive and Chief Business Officer of VocalIQ, which was acquired by Apple in 2015. He focuses on turning ground-breaking research into workable solutions.

Peter Whale - Founder, Vision Formers

Peter is founder of Vision Formers, a specialist consultancy that helps visionary technology businesses get product to market and turn their ideas into reality. Peter has a long track record of conceiving, developing and marketing successful technology-based solutions, deployed at scale, globally. Innovative products Peter has brought to market in digital, cloud, AI, consumer electronics and telecommunications have been used by countless millions of people on a daily basis globally, badged by the world’s leading digital and technology brands. Peter is a board member of CW (Cambridge Wireless), and co-leads its Artificial Intelligence special interest group.

Phil Claridge - Founder, Mandrel Systems

Phil Claridge is a ‘virtual CTO’ for hire within Mandrel Systems covering end-to-end systems. Currently having fun and helping others with large-scale AI systems integration, country-wide large scale big-data processing, hands-on IoT technology (from sensor hardware design, through LoRa integration to back end systems), and advanced city information modelling. Supporting companies with M&A ‘exit readiness’, due-diligence and on advisory boards. Past roles include: CTO, Chief Architect, Labs Director, and Technical Evangelist for Geneva/Convergys (telco), Arieso/Viavi (geolocation), and Madge (networking). Phil’s early career was in electronics, and still finds it irresistible to swap from Powerpoint to a soldering iron and a compiler to produce proof-of-concepts when required.

Steve Young - Professor of Information Engineering, University of Cambridge

Steve Young is Professor of Information Engineering at Cambridge University where he has previously served terms as Head of the School of Technology and Senior Pro-Vice Chancellor.  His main research interests lie in the area of statistical spoken language systems including speech recognition, speech synthesis and dialogue management. He is the recipient of a number of awards including an IEEE Signal Processing Society Technical Achievement Award, an ISCA Medal for Scientific Achievement and an IEEE James L Flanagan Speech and Audio Processing Award. He is a Fellow of the Royal Academy of Engineering and the Institute of Electrical and Electronics Engineers (IEEE).

In addition to his academic career, he has also founded three successful startups in the Speech Technology area. Entropic Inc was acquired by Microsoft in 1999, Phonetic Arts was acquired by Google in 2010 and VocalIQ was acquired by Apple in 2015. He is now a Senior Member of Technical Staff in the Apple Siri Development team based in Cambridge, UK, a post held jointly with his University professorship.

Théophane Weber - Senior Research Scientist, Google DeepMind

Theo is a senior research scientist at DeepMind. His research interests span probabilistic modeling, deep learning and deep reinforcement learning, and fundamentals of imagination and intuitive reasoning in artificial intelligence. Previously, he worked at Lyrics Labs (later acquired by Analog Devices), applying machine learning techniques to physical world problems. He holds an M.S. and Ph.D from MIT in Operations Research and an M.S. from Ecole Centrale Paris in Applied Mathematics.

Carl Edward Rasmussen - Chairman, Prowler.io, Professor of Machine Learning, University of Cambridge

Prof. Carl Edward Rasmussen is a professor of Machine Learning and head of the Computational and Biological Learning Lab at the Department of Engineering of the University of Cambridge. He is also the Chairman at PROWLER.io. He has extensive interests in probabilistic inference in machine learning, covering unsupervised, supervised and reinforcement learning. He is particularly interested in design and evaluation of nonparametric methods such as Gaussian processes (GPs) and Dirichlet processes. Exact inference in these models is often intractable, so one needs to resort to approximation methods, such as variational techniques or Markov chain Monte Carlo. He has co-authored a book with Chris Williams, entitled "Gaussian Processes for Machine Learning", MIT Press, 2006. Gaussian processes are a principled, practical, probabilistic approach to learning in kernel machines. This standard reference for GPs is accompanied by open source software tools.

Daniel Neil - Lead Machine Learning Researcher, BenevolentAI

Daniel Neil is a lead machine learning researcher at BenevolentAI which has key research areas around NLP and machine reading, generative models, and human-centric AI development for drug discovery and chemistry applications. Formerly, Daniel was a research assistant in Kwabena Boahen’s Brains in Silicon Laboratory at Stanford, helping to build the lowest-power neuron supercomputer Neurogrid, and worked as a technical strategy consultant in the San Francisco Bay Area. He was also a co-founder of Ponder, a site to discover intellectual events. Daniel received his B.S. degree in Biomedical Computation from Stanford with a thesis on protein simulation. He subsequently completed his master’s degree in Neural Systems and Computation at ETH Zurich, and received his doctorate there codeveloping hardware and optimized machine learning algorithms. His highest-impact publications have combined neuroscience, hardware, and algorithms to produce novel recurrent neural network algorithms in biologically plausible computing substrates. Currently, his research interests focus on applying deep models to hard problems in generative modelling for chemistry and biology. Specifically, he focuses on analyzing and building deep neural networks designed to cope with small or imbalanced data, combining semi-supervised and generative techniques, and exploring discrete and continuous spaces.

Alison Lowndes - Artificial Intelligence Developer Relations, Nvidia

After spending her first 18 months with NVIDIA as a Deep Learning Solutions Architect, Alison is now responsible for NVIDIA's Artificial Intelligence Developer Relations across the EMEA region. She is a mature graduate in Artificial Intelligence combining technical and theoretical computer science with a physics background & over 20 years of experience in international project management, entrepreneurial activities and the internet. She consults on a wide range of AI applications, including planetary defence with NASA, ESA & the SETI Institute and continues to manage the community of AI & Machine Learning researchers around the world, remaining knowledgeable in state of the art across all areas of research. She also travels, advises on & teaches NVIDIA’s GPU Computing platform, around the globe.

Simon Knowles - Co-founder & CTO, Graphcore

Simon is co-founder & CTO of Graphcore. Simon has a strong track record as both engineer and entrepreneur, having co-founded and exited two highly successful fabless semiconductor companies, Element14 and Icera, for a combined value of over $1billion. Before that he headed the microprocessor development group at ST Micro. He is well known as a leading microprocessor designer, responsible for ground-breaking designs at STMicro, Element14, and Icera. He holds an MA in Electrical Science from Cambridge University and is the author of 14 issued patents.

Peter Baldwin - Founder & Director, Myrtle Software Ltd

Peter has a pure mathematics PhD from Cambridge University and has a special interest in the mathematical foundations of deep learning. Myrtle accelerates performance critical workloads on FPGAs devices that are currently being deployed at scale in data centres around the world. Myrtle has realized multiple proprietary deep learning networks as silicon designs so that they execute at a latency and power point that makes them usable in real-world situations. Myrtle is currently targeting its technology at inference workloads in data centres and is involved in a major collaboration to address the safety and verification challenges that currently preventing sophisticated deep learning networks being used in road vehicles.

Anton Lokhmotov - Founder & CEO, Dividiti

Anton Lokhmotov is CEO and founder of dividiti. The main of focus of dividiti is on Collective Knowledge (CK), an open technology, platform and initiative for accelerating AI R&D by crowdsourcing interdisciplinary design and optimisation knowledge. CK is contributed to by engineers and researchers working on AI applications, software and hardware, across industry and academia in 2010-2015, Anton led development of GPU Compute programming technologies for the ARM Mali GPUs, including production and research compilers, libraries and performance analysis tools. In 2008-2009, Anton was a post-doctoral research associate at Imperial College London. Anton obtained a PhD in Computer Science from the University of Cambridge Computer Laboratory in 2007, and an MSc in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology in 2004.

Jem Davies - VP, GM and Fellow, Media Processing Group, Arm

Jem is an Arm Fellow and the General Manager for Arm’s recently formed Machine Learning business, focusing on Machine Learning and Artificial Intelligence solutions. Jem was previously GM and vice-president of technology for the Media Processing Group and Imaging and Vision Groups. In addition to setting the future technology roadmaps for graphics, video, display and imaging, he was also responsible for technological investigations for several acquisitions leading to formation of the Media Processing Group, and most recently Apical, forming the Imaging and Vision Group. Based in Cambridge, Jem has previously been a member of ARM’s Architecture Review Board and he holds four patents in the fields of CPU and GPU design. He has a degree from the University of Cambridge.

Cyrus Vahid - Principal Solutions Architect, Amazon (AWS)

Cyrus Vahid is a Principal Solutions Architect at AWS Deep Learning. He has a background in Machine Learning and Artificial Intelligence with a focus on Neural Networks. Before joining AWS Cyrus was Principal Domain Architect at Redhat. He is a software specialist with focus on integration and analytics. Over the past 20 years he has delivering innovative solutions with a track-record of success stories mostly in entrepreneurial environments. For the past 3 years he has been dedicated to Big Data initiatives and solutions.

Tony Robinson - Founder & CTO, Speechmatics

Tony pioneered recurrent neural networks in speech recognition and has built and scaled multiple speech groups and companies. Tony’s passion is the development and application of machine learning to tasks that traditionally had been considered impossible for computers to solve.

Neil Lawrence - Professor of Machine Learning, Sheffield University

Neil Lawrence leads Amazon Research Cambridge where he is a Director of Machine Learning. He is on leave of absence from the University of Sheffield where he was a Professor in Computational Biology and Machine Learning jointly appointed across the Departments of Neuroscience and Computer Science. Neil’s main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular focus on applications in personalized health and computational biology, but happily dabbles in other areas such as speech, vision and graphics. Neil was Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (2011-2013) and is an Action Editor for the Journal of Machine Learning Research. He was the founding editor of the Proceedings of Machine Learning Research (2006) and is currently series editor. He was an area chair for the NIPS conference in 2005, 2006, 2012 and 2013, Workshops Chair in 2010 and Tutorials Chair in 2013. He was General Chair of AISTATS in 2010 and AISTATS Programme Chair in 2012. He was Program Chair of NIPS in 2014 and was General Chair for 2015. He is one of the founders of the DALI Meeting and Data Science Africa.

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Computer Laboratory, University of Cambridge, William Gates Building, 15 J J Thomson Ave, Cambridge, CB3 0FD

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