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Real Artificial Intelligence - Speakers

Details of the speakers and their presentations are given below.

Dr. Andrew Fitzgibbon (Microsoft Research Cambridge)

Particle Swarms and Slippery Slopes

Casting hard problems as optimization (e.g. “energy minimization”) is powerful in so many ways: it may be the natural Bayesian thing to do, it may mirror reality, it may allow our ability to generate (e.g. graphics) to be used to solve harder inverse problems (e.g. vision). It allows us to use black boxes at two levels. At the practical level, there’s a whole internet full of PSOs, BFGSs, SGDs that we can use. Black boxes are also great at a theoretical level: we can assume the perfect black box and analyse the performance of the algorithm by examining the optimization objective, which is typically a much simpler object (piece of code, set of equations) than the optimization algorithm (in terms of lines of code, or in terms of bifurcation paths, potential execution traces etc). And indeed we already have the perfect black box: simply try all values which will give statistically indistinguishable results, this latter proviso making this a finite set. The code is simple: a few hundred nested “for” loops. It is slow however. Oh, you wanted it fast? Really? OK, now we’re talking. Let’s see exactly what’s fast and what isn’t. In the red corner: genetic-derived algorithms, in the blue corner: gradient-based algorithms. Let’s see who wins on which problems, both in theory and in practice.

Andrew Fitzgibbon is a principal researcher in the Machine Intelligence and Perception group at Microsoft Research Cambridge. He is best known for his work on 3D vision, having been a core contributor to the Emmy-award-winning 3D camera tracker "boujou" (www.boujou.com) and Kinect for Xbox 360, but his interests are broad, spanning computer vision, graphics, machine learning, and even a little neuroscience. He has published numerous highly-cited papers, and received many awards for his work, including nine "best paper" prizes, the Silver medal of the Royal Academy of Engineering, and the BCS Roger Needham award. He is a fellow of the Royal Academy of Engineering, the British Computer Society, and the International Association for Pattern Recognition. Before joining Microsoft in 2005, he was a Royal Society University Research Fellow at Oxford University, having previously studied at Edinburgh University, Heriot-Watt University, and University College, Cork.

Professor Susan Craw (Robert Gordon University, Aberdeen)

Robust Intelligence from Case-Based Systems

A Case-based System solves problems by retrieving similar, previously solved problems from its memory, and reusing their solutions. The fundamental assumption is that ‘similar problems have similar solutions’. Robust Intelligence should be able to cope with ill-defined domains and complex, changing realistic contexts, in contrast to the brittleness of many model-based systems. This talk will look at a number of case-based applications, will investigate the contribution they make towards robust intelligence, and will explore the opportunities they offer to learn and adapt to a changing problem-solving environment.

Susan Craw is a Professor of Computing at Robert Gordon University in Aberdeen where she has been Director of the IDEAS Research Institute, Head of Research for Design & Technology, and Head of Computing. She graduated with BSc Honours and MSc by Research in Pure Mathematics and a PhD in Computing Science from Aberdeen University. Her research developing AI technologies for building smart systems is established over 30 years. Current research interests are Smart Information Systems, Case Based Reasoning, and Recommender. In 2015 she was elected a AAAI senior member in recognition of significant contribution to the field of AI.

Dr. Andy Pardoe (Credit Suisse)

Innovation Labs: the Journey from Big Data Lakes to Real AI Systems

Many corporates have invested in big data solutions over the past few years, however, focus is now shifting to data science analytics and machine learning systems. While large technology firms are already invested in exploring the application of machine learning techniques, we will explore the role of innovation labs within non technology corporations to demonstrate the benefits of advanced analytics and machine learning applications to their business users and executives.

Andy Pardoe has worked in both commercial and research environments developing artificial intelligence applications. Currently working for Credit Suisse as a technical architect designing their machine learning and big data analytics platforms.


Professor Ben Azvine (BT)

Applications of Intelligent Systems in BT

Over the last 30 years, Intelligent Systems have grown significantly in importance and applicability across industries. This has been fuelled by advances in computing power and significant reduction in the cost of hardware as well excellent progress in computational models of systems that resemble intelligent behaviour. In this talk I’ll share my experience of developing industrial application of intelligent systems within BT over the last 20 years is a wide variety of areas such as workforce management, customer service, business intelligence and Cyber security.

Ben Azvine is the global head of security research and innovation at BT. He has 25 years’ experience in both academia and industry. His previous roles included leading the IT research centre and head of business intelligence & customer analytics research at BT Group Chief Technology Office. He holds a BSc in Mechanical Engineering, an MSc in Control Engineering, a PhD in Intelligent Control Systems from Manchester University and an MBA from Imperial College, London. Having held research fellowship and lectureship posts in several universities, he joined BT in 1995 to lead a research programme to develop and exploit novel Artificial Intelligence technology to support next generation IT systems for BT. Since then he has held senior, principal, chief research scientist posts at Adastral Park in Ipswich where he is currently based.

He has edited two books and published more than 100 scientific articles on novel application of intelligent systems. He is an inventor of 50 patent applications, has won 4 BCS and an IET gold medals for IT innovation, holds visiting professorship positions at the Universities of Bristol, Cranfield and Bournemouth. He acted as the chairman of the European network of excellence for Uncertainty management techniques from 1998 to 2001. His current research interests include the application of intelligent systems to security, protection of critical national infrastructure, data analysis and information & knowledge management. His current projects include Cyber, cloud & internet security, intelligent assessment of security events, future compliance and risk management and future identity and access management.

Dr. Miguel Martinez-Alvarez (Signal)

Signal: Analyzing and Understanding the World’s News

The overload of textual information is an ever-growing problem to be addressed by modern information filtering systems, not least because strategic decisions are heavily influenced by the news of the world. In particular, business opportunities as well as threats can arise by using up-to-date information coming from disparate sources such as articles published by global news providers but equally those found in local newspapers or relevant blogposts. Common media monitoring approaches tend to rely on large-scale, manually created boolean queries. However, in order to be effective and flexible in a business environment, user information needs require complex, adaptive representations that go beyond simple keywords. Signal uses a cloud-based architecture that processes and analyses, in real-time, millions of articles from news and blog and allows its users to specify complex information requirements based on entities, topics, industry-specific terminology and keywords.

Miguel Martinez-Alvarez is a researcher and a developer, mainly in the fields of Text Analytics and Information Retrieval. He is also the co-founder and Head of Research of Signal, a media monitoring and insight platform which analyses millions of news articles in real-time in order to improve business intelligence. His main role is to investigate and analyse the best possible algorithms and methods from different academic fields and apply them in a large-scale, commercially viable product. During his time at Signal he has been awarded the Business Leader of Tomorrow award 2014 by Innovate UK and was included in the list of UK Business Innovators in 2016 by Bloomberg.

Professor Thomas Roth-Berghofer (University of West London)

Explanation-aware Thinking, Design and Computing

Explanation, trust, and transparency are concepts that are strongly associated with information systems. The ability to explain reasoning processes and results can substantially affect the usability and acceptance of a software system. Within the field of knowledge-based systems, explanations are an important link between humans and machines. There, their main purpose is to increase confidence of the user in the system’s result (persuasion) or the system as a whole (satisfaction), by providing evidence of how the solution was derived (transparency). For example, in recommender systems good explanations can help to inspire user trust and loyalty, and make it quicker and easier (efficiency) for users to find what they want (effectiveness). This talk presents important concepts for analysing and developing software systems with explanation capabilities and illustrates them with example implementations.

Thomas Roth-Berghofer's research focuses on aspects of smarter communication with personalised computing systems. He specialises in artificial intelligence and experience reuse with the help of case-based reasoning, and explanation-aware computing. Thomas is Professor of Artificial Intelligence and leads the research group Intelligent Computing Systems. He is Head of Research and Enterprise in the School of Computing and Engineering.

Thomas studied computer science with a minor in economics at the University of Kaiserslautern, Germany, where he gained his MSc (‘Dipl.-Inform.') and PhD (‘Dr. rer. nat.’), both focussing on artificial intelligence. He has worked in software industry as developer, technical consultant, and manager quality and customer support for several years, before he joined the German Research Centre for Artificial Intelligence DFKI GmbH as Senior Researcher. Since 2011, he is Professor in Artificial Intelligence at the University of West London.

Thomas has more than 100 refereed publications. He organised many workshops and conferences on such topics as case-based reasoning, context, explanation, and knowledge management. He is co-organiser of the 24th International Conference on Case-Based Reasoning (ICCBR 2016) in Atlanta, Georgia, USA.

Find out more about him: https://about.me/thomasrothberghofer.

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