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Real Artificial Intelligence - Speakers
the speakers and their presentations are given below.
Dr. Andrew Fitzgibbon (Microsoft Research Cambridge)
Particle Swarms and Slippery
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
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
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
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
Ben Azvine (BT)
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.
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.
Thomas Roth-Berghofer (University of West London)
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
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,
Find out more about him: https://about.me/thomasrothberghofer.