Claire Calmejane - Lloyds Banking Group
Claire Calmejane is a leading influencing figure in Financial Technology and one of the only females in the European Top 40 Fintech list. The French-born tech guru joined Lloyds Banking Group in 2012. Claire enjoys working on projects that make a difference and benefit people. She has supported the set-up of the Digital Academy at Lloyds Banking Group, which has helped 75, 000 of her colleagues to become digital leaders. She is a lecturer on Fintech and Digital Transformation at MIT, Oxford, UCL and HEC. Before joining the bank, she worked for Capgemini Consulting and has served as a visiting scientist at MIT, lecturing on how large organisations digitise.
DEEP LEARNING METHODS & TOOLS IN FINANCE
Soledad Galli - LV=
Interpreting Machine Learning Models
Big data and machine learning are becoming central parts of the business for many organisations in the public and private sectors. This is driven by the continuous growth and availability of data that empowers the organisations to make better data driven decisions. To support the business and the users of the machine learning models, it is key to explain why the algorithm made a certain decision. This way machine and people can work together to tackle important issues like Fraud. In addition, with the imminent coming of the new regulation on data protection, today model interpretability is becoming more important than ever. Many machine learning algorithms like gradient boosted trees and deep learning have been traditionally called ‘black-box’ algorithms, because it seems unclear the process they undertake to make a certain decision. In particular, deep learning involves feeding information through non-linear neural networks that classify data based on the outputs from previous layers, making it very difficult to understand the reasons for the decisions made. In this talk, I will discuss different implementations that allow us to understand why an algorithm makes a certain decision at an observation level. I will show how we can use these tools in public datasets and then describe how we use them in insurance.
Soledad is a Lead Data Scientist at LV=, with 2+ years of experience in data science and analytics in the financial sector, and 10+ years of experience in scientific research in academia. She is passionate about extracting meaningful information from data and supporting institutions make solid and reliable data driven decisions. At LV=, Soledad and the data science team are leading the implementation of machine learning across the multiple company business areas. Having transitioned from academia to data science, Soledad is passionate about enabling and facilitating data scientists and academics transition into the field, and helping data scientists increase their breath of knowledge. During the last year, Soledad shared insight in blogs and talks in the data science community. She also created 2 online courses on machine learning now live in Udemy, which have enrolled 350+ students from several parts of the world in just under 3 months.
Michael Natusch - Prudential
Horses for Courses: Deep Learning Beyond Niche Applications
Machine learning in general and deep learning in particular are driving major advances for a wide range of specific finance use cases. This talk will outline how enterprise-wide learning loops will extend these point success to a coherent AI strategy and also show what other elements are required for success, using real-world examples at Prudential plc.
Michael is the Global Head of AI in the Group Digital team of Prudential plc. He joined Prudential last year from Silicon Valley based Pivotal Labs where he led the Data Science team. His experience lies in the application of artificial intelligence methods to large-scale, multi-structured data sets, in particular neural network based deep learning techniques. Michael previously founded and sold a ‘Silicon Roundabout’ based startup and prior to that was a partner at a major consulting firm. Michael holds a PhD in theoretical physics from the University of Cambridge and is a Fellow of the Royal Statistical Society.
DEEP REPRESENTATION LEARNING
Huma Lodhi - Direct Line Group
Deep Representation Learning for Disparate Data
Deep leaning based Artificial Intelligence models are showing state-of-the-art results for complex problems in banking and insurance. This talk will focus on learning representations from disparate data, like free-form text and structured categorical and numeric data, in insurance. It will show how a deep network can be trained that represents the knowledge that is required to perform the task at hand and generalises well to novel unseen cases.
Huma is a principal data scientist at Direct Line Group. She has over 15 years of experience in Artificial Intelligence & Machine Learning across both industry and academia. She is an accomplished expert with hands on experience in development and application of Deep Learning, Kernel Methods, Relational Learning and Ensemble Methods for areas ranging from insurance to health care. She has a PhD in Machine Learning from university of London. She is a co-editor of two books and has published many research articles in leading AI & Machine Learning journals and conferences. She regularly speaks at industry events and is keen to transform industries by the use of Artificial Intelligence and Machine Learning.
DECISION MAKING & AI
David Hand - Senior Research Investigator & Emeritus Professor of Mathematics - Imperial College London
Evaluating Algorithms: Why Your Conclusions are Wrong if you use the F-Measure & What to do About it
David Hand - Imperial College London
Evaluating Algorithms: Why your conclusions are wrong if you use the F-measure and what to do about it
The choice of performance metric is critical in constructing and choosing between machine learning tools. Poor choice of metric can lead to poor choice of method, with serious adverse consequences. One popular and very widely used performance metric is the F-measure. However, this measure has fundamental conceptual weaknesses which render its straightforward (and standard) usage invalid. These conceptual contradictions are spelt out, and an adaptation is described which overcomes the problems and leads to valid results.
Professor David Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, where he formerly held the Chair in Statistics. He is also Chief Scientific Advisor to Winton Capital Management. He is a Fellow of the British Academy, and an Honorary Fellow of the Institute of Actuaries, and has served (twice) as President of the Royal Statistical Society. He is a non-executive director of the UK Statistics Authority, and is Chair of the Board of the UK Administrative Data Research Network. He has published 300 scientific papers and 28 books, including Principles of Data Mining, Information Generation, Measurement Theory and Practice, The Improbability Principle, and The Wellbeing of Nations. In 2002 he was awarded the Guy Medal of the Royal Statistical Society, and in 2012 he and his research group won the Credit Collections and Risk Award for Contributions to the Credit Industry. He was awarded the George Box Medal in 2016. In 2013 he was made OBE for services to research and innovation.
Horia Velicu - BRD - Groupe Societe Generale
Hybrid recommender system for banking products using deep learning techniques:
The task is to predict top 5 products to offer to each client. The model uses an ensemble of neural networks with various architectures. Due to the large target number of clients, around 2.5 milions, an autoencoder applied to similarity vectors was used for dimensionality reduction.
Horia Velicu is Head of Innovation Lab at BRD - Groupe Societe Generale and is therefore involved in products and ideas that can make life easier for clients or employees through technology. From chatbot to blockchain, Horia has over 18 years experience in banking and consulting with a focus on financial markets.
NLP & SENTIMENT ANALYSIS
Adam McMurchie - RBS
Dynamic Banking - A five-year roadmap to excellence in AI
Andrew Ng famously stated that “AI is the new electricity”, whilst the jury is still out on this one - it is important to note there has already been at least two seperate AI renaissances in the past that failed to reach escape velocity*. However it is without a doubt that the next AI revolution has begun, but this time far greater in scope and scale than ever before. That said learnings have been made, and it has become apparent that a robust framework is essential for successful adoption across the various industries - this is where AI meets DevOps.
In this presentation I showcase a one size fits all, five-year roadmap to guide businesses big and small into a successful AI transformation. I also explore Dynamic Banking - the first major wave of AI finance centric innovations from slim predictive fraud engines to frictionless banking IPB micro-apps. Finally I report on the progress across the globe towards the second major wave of AI, and what this means for finance as a whole.
- Significant investment from both the US, UK and the EU was funnelled into AI research and development in the 1960s and 1980s, in both cases funding and interest was abruptly withdrawn due to limitations of the technology. Adam McMurchie is leader in Devops and an A.I expert working in the banks SAO platform on the forefront of technology development in finance. With a broad exposure to a range of technologies, Adam drives an ethos of simplification, cloud agnosticism and specialises in spotting the next trends in fin tech. Additionally, Adam also has a background in science with a physics degree specialising in NeuroComputing and is a polyglot linguist & seasoned translator. Adam has pooled these skills to deliver full stack novel solutions from tensor flow driven mobile apps, to personalized banking chatbots. Adam also develops apps designed around the ethos of Social Utility, including Flood/Storm reporting, EV Vehicle bay monitoring and preservation of endangered languages.
Matteo Testi - Deep Learning Italia
Volatility: your Enemy or your Friend?
One of the most challenging tasks in Deep Learning is “Sentiment Analysis” (SA). SA is the process of automatically categorising opinions within a piece of text such as a tweet (i.e. positive, negative, neutral). For this session, we propose our Sentiment Analysis prototype which is standing out as an interesting tool able to merge advanced Deep Learning techniques, feasible Human-Machine interaction and easy Data Discovery. Specifically, we focused on the improvement of state of the art by integrating Recurrent Convolutional Neural Networks (RCNNs) with a novel training approach. Indeed, we designed a new training algorithm grounded on the well-known “False Positive Reduction” technique. Namely, we adopt a words-driven training of the tweets by searching these words within the training set of tweets. These “discriminative” words lead to the creation of words-centred sentences (i.e. the context of each word) introducing additional false positives within the training set. The main topic in find the emotional patterns in the financial market able to understand and predict the trends.
Matteo is the CEO of Deep Learning Italia, the biggest Italian Community of Deep Learning. Deep Learning Italia, shares tutorials, articles and lessons online about Machine Learning and Deep Learning. Matteo also offers consultancy for Machine Learning and Deep Learning issues with a vast amount of experience with R and Python. During his most recent work, he has been developing using Cloudera's Ecosystem (Hive, Hadoop, Impala, Pig, Scala, Spark, Mahout, Sqoop, Flume, Kafka). Matteo is responsible for successfully directing the various teams at Deep Learning Italia and has previously been awarded a Bronze award for their work at the Kaggle Competition.
DEEP LEARNING & FRAUD DETECTION
Jordan Brandt - Inpher
Privacy-preserving Machine Learning in Finance
More (good) data yields better models, but increasing consumer awareness, privacy regulations and proprietary barriers mitigate access to valuable feature sets and our ability to leverage them. The conundrum of computing data without exposing it can be addressed with emerging cryptographic methods such as Secure Multiparty Computation and Fully Homomorphic Encryption. Furthermore, this opens the opportunity to monetize analytics while maintaining data privacy, security, and scarcity value. This talk will discuss the basics of the technologies and various real applications in financial institutions including fraud detection, credit analysis, customer discovery and more.
Dr. Jordan Brandt is the CEO and cofounder at Inpher, a data security company pioneering privacy-preserving machine learning. As a Technology Futurist, Jordan’s research and insight on cybersecurity, AI, robotics and 3D printing have been featured in print and live broadcast internationally on Bloomberg, CNBC, Forbes, Financial Times, Wired and other business and technology press. Jordan is the former CEO and cofounder of Horizontal Systems, acquired by Autodesk (Nasdaq: ADSK) in 2011. He went on to serve as the director of Autodesk’s investment fund, while also teaching and conducting research as a Consulting Professor of Engineering at Stanford University. Jordan completed his undergraduate work at the University of Kansas and his PhD in Building Technology at Harvard. In 2014 he was selected as one of Forbes ‘Next-Gen Innovators’.
Timothy Hospedales - University of Edinburgh / QMUL
Enforcing Sanity in a Black Box Models: Gated Neural Networks for Option Pricing
Neural Network methods often outperform econometric models in empirical evaluations of prediction accuracy, yet they are hard to trust in mission critical applications due to lack of guarantees about the sanity of their predictions for all inputs. We introduce a variety of tools and techniques for enforcing a variety of sanity conditions in neural network predictive models. Then we demonstrate a case study of gated neural networks for EU call option pricing that automatically learn to divide and conquer the problem space for robust and accurate pricing. We derive instantiations of these networks that are ‘rational by design’ in terms of naturally encoding a valid call option surface that enforces no arbitrage principles. This integration of human insight within data-driven learning provides significantly better generalisation in pricing performance due to the encoded inductive bias in the learning, guarantees sanity in the model’s predictions, and provides econometrically useful byproducts such as risk neutral density.
Timothy Hospedales is a Reader (Associate Professor) at the University of Edinburgh and Visiting Reader at Queen Mary University of London, where he founded the Applied Machine Learning Lab. His research focuses include deep learning, knowledge transfer for life-long machine learning, and reliable and explainable machine learning.
DEEP LEARNING FRAMEWORKS
Jim Dowling - KTH Royal Institute of Technology
Hyperscale Deep Learning for the Masses
State-of-the-Art Deep Learning systems at hyper-scale AI companies attack the toughest problems with distributed deep learning. Distributed Deep Learning systems enable both AI researchers and practioners to be more productive and the training of models that would be intractable on a single GPU server. In this talk, we will introduce the latest developments in distributed Deep Learning (synchronous stochastic gradient descent) and how distribution can both massively reduce training time and parallel experimentation, using large-scale hyperparameter optimization. We will introduce different distributed architectures, including the parameter server and Ring-AllReduce models. In particular, we will describe open-source TensorFlow frameworks that leverage Apache Spark to manage distributed training, such as Yahoo’s TensorflowOnSpark, Uber’s Horovod platform, and Hops’ TfSpark. We will introduce the different programming models supported and highlight the importance of cluster support for managing GPUs as a resource. To this end, we will also introduce Hops, an open-source distribution of Hadoop with support for GPUs as a resource, and show how TensorFlow/Spark applications can be easily run from a Jupyter Notebook. We will also show that on-premise distributed Deep Learning is gaining traction, as both enterprise and commodity GPUs can be integrated into a single platform.
Jim Dowling is the CEO of Logical Clocks AB, as well as an Associate Professor at KTH Royal Institute of Technology in Stockholm, and a Senior Researcher at SICS RISE. His research concentrates on building systems support for machine learning at scale. He is the lead architect of Hops Hadoop, the world's most fastest and most scalable Hadoop distribution and only Hadoop platform with support for GPUs as a resource. He is a regular speaker at Big Data and AI industry conferences, and blogs at O'Reilly on AI.
PANEL: What are the Key Challenges & Risks of Implementing AI Into the Financial Sector?
Karthik Ramakrishnan - Element AI
Karthik Ramakrishnan holds an Master Degree from Waterloo University in Applied Science, Electrical and Computer Engineering and MBA from Ivey Business School, Canada. As a Director of Industry Solutions at Element AI, he is dedicated to engage the clients to develop an AI-first strategy and vision, prioritize ideas and opportunities to utilize Element AI’s research and development capability and achieve clients’ AI ambitions. He also manages a team of ~40 in both Toronto and Montreal. He is a serial entrepreneur with broad international experiences. He founded a series of technology start-ups, including Gallop Labs, Blu Trumpet and Gazoo Mobile. He also serves as advisor for multiple start-ups, including the most recent Perpetua Labs, Beam Propulsion Lab, and Hubbl, MatchFuel, etc. Before joining Element AI, he was a Senior Manager at Deloitte. In the early years of his career, he also serves as Director of Product Innovation at Xtreme Lab, GM of Product Development at Hatch Labs in New York.
AJ Hashim - Barclays
The Use of Chatbots and Natural Language Processing in Financial Services Organisations to Improve Client Experience and Reduce Operational Costs
This presentation with explore the latest trends in Natural Language Processing and how this technology is being used by Financial Institutions today to deliver real benefits for banks and for clients. It will also explore the approach taken to testing these technologies and how best to experiment and deploy within a large corporate environment.
AJ Hashim has lead the Innovation and Emerging Technology team for the past 2 years, developing future technology strategies and executing through proof of concepts in the Corporate Bank. He has delivered a number of high profile initiatives for the bank, pioneering the use of AI and Blockchain across the technology estate. He previously did his Masters research into the optimization of making financial decisions using artificial neural networks, and has a passion for the commercial applications of new technologies.
Elena Chatzimichali - HSBC
Elena is a Data Scientist working for HSBC Global Banking and Markets. With a strong academic background in Computer Science, Elena holds a PhD in Machine Learning and an MSc in Bioinformatics. Over the past ten years she has also actively been involved in lecturing, supervising and developing a wide range of specialised courses for both academic and professional audiences. Her research work focuses on the development, implementation and optimisation of complex statistical models and novel computational tools for the financial services and healthcare sectors.
CONVERSATION & DRINKS
Marco Javarone - nChain
Implementing Deep Learning Models: a quick overview
During this talk I will provide a quick introduction to Deep Learning, starting from its theoretical foundations to simple implementations. Then, I will focus on some languages and frameworks for developing commercial projects, with a particular emphasis on Tensorflow and Keras. In particular, I will explain how these frameworks work by simple examples and fragments of codes. To conclude, some tips for feeding deep networks, and evaluate its performance are presented. Therefore, beyond to present a general overview on Deep Learning and its mathematical structure, this talk aims to indicate the first steps for build up a (simple) model in Python.
Marco Alberto Javarone is a Senior Researcher at nChain LTD and Associate Researcher at the University of Kent. He holds two PhDs, one in Computer Engineering and one in Applied Mathematics. His research activity is focused on Statistical Physics and Information Theory with applications to Machine Learning (and Deep Learning), Blockchain Technologies, Evolutionary Game Theory and Complex Networks. Recently, he published a book on computational models for Evolutionary Game Theory (Springer). Passionate for Startups, he works as consultant in the field of Machine Learning and Data Science.
Filippo Scopel - Merantix
Learning to Trade
There are known applications of Deep Learning in financial time series predictions, such as sentiment analysis based on microblogging messages or commodity supply prediction based on satellite imagery. Surprisingly, there has been little research on using deep learning to predict market movements based on pure financial microstructure. We argue that there are distinct advantages of choosing machine learning models over classical econometric approaches in algorithmic trading. This talk gives a short introduction to financial microstructure data, general problems of algorithmic trading and the opportunity of deep (reinforcement) learning to solve them.
Filippo is a Machine Intelligence Engineer at Merantix in Berlin. During his master studies, Filippo researched sentiment analysis to forecast financial time series. Before joining Merantix he worked for Julius Baer automatising portfolio risk management and at Konux, a Munich based industrial IoT company, training deep models based on time series data from industrial sensors. In the past year, Filippo has worked at Merantix training deep models for time series prediction on financial microstructure data. Merantix is currently engaged in algorithmic trading on the Russian FX-Market and on several cryptocurrency exchanges.
Rajeev Dutt - DimensionalMechanics
How Can Adoption of Machine Learning Be Accelerated in the Financial Sector?
Volumes of data are generated and consumed by the financial sector daily. Analyzing this data depends on a small pool of data scientists and an even smaller pool of machine learning experts. NeoPulse™ is a new platform that substantially reduces the skill level needed to build machine learning solutions at a fraction of the cost. It puts machine learning into the hands of engineers without experience in the area. By empowering more engineers to build ML solutions, NeoPulse™ can drive broader adoption of ML at a faster rate.
Rajeev is a veteran of the high-tech industry, with prominent roles at Hewlett Packard, Compaq, Microsoft, Intel and the BBC. He pioneered work at the BBC leading to the BBC America's Shop; at HP in problem detection and self-healing; and at Microsoft and Intel in Windows technologies and automated hardware validation. At the age of 26, Rajeev launched a startup, NeoProxima PLC in London UK to build and deliver adaptive multimedia solutions for enterprises. Rajeev has expertise in cloud computing, artificial intelligence, OS kernel design, and hardware. He holds a degree in theoretical physics from Trinity College, University of Toronto.
Blaise Ngonmang - Anorak Technologies
Data for Smart Advice in Life Insurance
Life insurance is broken. Smart data will fix it. In this talk, I will present how Anorak Technologies takes advantage of smart data and machine learning techniques to help our customers get the life insurance they really need. Through advanced data science and machine learning, we’re giving everyone access to tailored advice about their life and their risks. Not only that, but we’re looking at how to integrate it seamlessly into people’s digital lives. For example, by partnering with existing services, like online banking and e-commerce. Anorak is backed by Kamet, the startup studio of AXA.
Dr. Blaise Ngonmang holds a PhD in machine learning and as the Chief Data Officer (CDO) of the Anorak Technologies group he is responsible of the data strategy and lead the Data science team. Prior to Anorak Blaise was a lead Data scientist at AXA with a main focus on fraud detection in insurance claims and a R&D scientist working on the monetization of online social networks.
DEEP LEARNING APPLICATIONS IN FINANCE
Javier Campos - Experian
Using Deep Learning to Address Transactional Credit Card Fraud: Using advanced machine-learning mathematical formulas to outperform the bank’s existing system in rooting out credit card fraud.
As Head of the Datalabs, Javier oversees innovation with emphasis on development of new products and services across all business units in UK&I and EMEA. Pioneering application of artificial intelligence in mobile, voice, fraud, credit, marketing, social media, digital advertising and healthcare. He came from WPP, where he spent 7 years: two as Kantar ( WPP) Global CTO, leading the strategy of the global technology function and focusing on the new generation of market research platforms.He also spent 5 years in GroupM (WPP), as the EMEA Chief Information Officer (CIO). He was part of the Xaxis Global Technology Board. Javier has more than 26 years’ experience globally within the Finance, AI, Market Research, Media and Technology fields including serving as Global Chief Technology Officer at Havas Media. Prior to joining HavasMedia, Javier was an Executive consultant for the largest Media companies in the world within Accenture’s London Media & Entertainment practice for nearly 10 years.
Andreas Hoepner - University College Dublin
Risk Analysis With & of AI: How Deeply Do We Learn With Which Statistical Approach
AI is one of the most addictive trends over the last years but how useful is which type of AI in a finance context? Does narrow or maybe even general Artificial Intelligence present the Holy Grail? Or will Augmented Intelligence be the winning formula for most use cases? Is AI a game of Man vs. Machine (e.g. AlphaGo) or is Kasparov right and AI is all about harmonizing teams of men & machine? Prof. Hoepner does not claim that there is one definite answer to the above questions. Instead, he argues that the nature of the use case is key. Repeated, real-time recognition tasks are highly suited for narrow Artificial intelligence solutions. However, the less repetitive, the more dynamic and the more predictive a use case, the more Augmented Intelligence becomes the viable business solution. In any case, AI will empower humans! It's just not clear which humans will empowered and which role these human will play in the emerging Data Factories of the 21st century.
Dr. Andreas G. F. Hoepner is Professor of Operational Risk, Banking & Finance at the Michael Smurfit Graduate Business School and the Lochlann Quinn School of Business of University College Dublin (UCD). Andreas is also heading the Practical Tools research group of the Mistra Financial Systems (MFS) research consortium, which aims to support Scandinavian and global asset owners with evidence-based tools for investment decision making. Prior to commencing his MISTRA role in March 2016, Andreas served over six years as lead academic advisor to the United Nations supported Principles for Responsible Investment and consulted organisations including the European Commission, the International Finance Corporation (IFC), and the University of Cambridge's Institute for Sustainability Leadership (CISL).
Luigi Troiano - University of Sannio
Supporting Trading in Financial Markets by Means of DL Tools
Deep Learning (DL) is disclosing new possibilities to automate complex decision making, and Finance is one the field that can benefit more from that. The need for investment decisions to look at a wider range of information has driven the interest towards the application of DL in Finance, due to the capability of the new architectures to explore complex relationships within groups of information sources or between sources and the quality of decisions. In this presentation, we will focus on some aspects regarding the use of DL in trading systems. In particular we will report some research findings from our group regarding the use of non-linear encoders and embedders in order to distort the data space, long-short term memory for multivariate volatility prediction and for learning an algorithmic trading strategy, convolutional neural networks in price series analysis.
Luigi Troiano is professor of Artificial Intelligence, Data Science and Machine Learning at University of Sannio, Department of Engineering, Italy. His research is devoted to mathematical modelling and algorithm development with applications to Finance and other industries. His expertise is designing, experimenting and validating algorithms, along their implementation in software systems for industrial environments, including some large international companies. He is coordinator of Computational and Intelligent Systems Engineering Laboratory (CISELab) at University of Sannio, aimed at developing research in Big Data and Deep Learning.
Arjun Bhandari - Family Office
Learning and Improving Factor Models
Factor selection and combination have been researched extensively in the world of statistical finance. Increase in number of data sources and frequency of data collection have opened new avenues in this pattern recognition problem. In this presentation I show conceptually how deep learning is applied in the first step of the investment management process - factor identification for diverse asset classes in a unified framework. I also present some of challenges proposed solutions to construct an alpha model and briefly share our research agenda. The current investment process employs a detailed model built on this conceptual framework.
Arjun Bhandari is a seasoned investment professional with over two decades of experience with Abu Dhabi Investment Authority and APG Investments (Holland), As head of quantitative strategies group he was managing multi billion dollar portfolios with a strong focus on application of advanced quantitative techniques. Currently he is applying his expertise and leveraging AI techniques - deep learning and machine learning, coupled with heavy tailed statistical methods as CIO at a single family office in the UAE. An Engineer by training he holds an MBA and is a CFA Charterholder.
PRIVACY, SECURITY & ETHICS - Plenary Session with Retail & AI Assistant Attendees
Aditya Kaul - Tractica
Aditya Kaul is a research director at Tractica, with a primary focus on artificial intelligence and robotics. He also covers blockchain and wearables as part of his research. Kaul has more than 12 years of experience in technology market research and consulting. He is based in London.
Prior to Tractica, Kaul was a practice director at ABI Research, where he led the firm’s Mobile Networks research group. Kaul has also worked as an analyst and team leader at firms including Pioneer Consulting and Evalueserve, and has provided independent consulting services in the areas of Internet of Things, wearables, and smart cities. Kaul started his career as an electrical engineer designing chipsets and wireless networks with stints at Qualcomm and Siemens. Kaul has been a prolific speaker, moderator, and panelist at industry conferences and events, and has appeared frequently in the media including The Wall Street Journal, The Financial Times, Forbes, CNBC, The Motley Fool, VentureBeat, Unstrung, ZDNet, Wireless Week, EE Times, and CommsDesign, among others.
Kaul holds two master’s degrees in engineering, from Colorado State University and Pennsylvania State University, as well as a bachelor’s degree in electrical engineering from National Institute of Technology, Surat in India.
Panel Discussion - Security & Privacy - Plenary Session
AI holds great promise but also significant threat. As AI capabilities continue to advance at a rapid pace, so does the risk to both company and consumer. In this panel discussion we will bring together experts in AI security as well as those facing giant risks when advancing AI in their business. How do we keep AI safe from adversaries? How can we guard against mistakes? How do we protect against unintended consequences?
Catherine Flick - De Montfort University
Dr. Catherine Flick is a Senior Lecturer in Computing and Social Responsibility at the Centre for Computing and Social Responsibility at De Montfort University.
Areas of research have involved responsible research and innovation in health and ageing, online child protection, trusted computing, ethics and video games, ethics in AI, anonymous technologies and the darknet, and informed consent in ICT. She holds a number of EU grants on responsible research and innovation and is a member of the ACM’s Committee on Professional Ethics, and is a steering committee member of the ACM’s Code of Ethics refresh team. She teaches computer ethics and research methods to business computing and computer science students.
Panel Discussion - Ethics - Plenary Session
After looking at technical advancements in AI in retail, finance and AI Assistants over the course of the summit, we will join together to discuss the broader questions arising within AI ethics. Top academics and experts in industry will discuss topics around employment, skill shifts, behaviour change, AI bias and humanity. What should a regulatory framework for AI include? How do we eliminate AI bias? How do we define the humane treatment of AI? How do machines affect our behaviour and interaction?
Lucy Yu - FiveAI
Lucy leads public policy at FiveAI, a British technology company building fully self-driving vehicles to deliver safe and convenient shared mobility services for cities, starting in London in 2019. Lucy’s background combines startup business with technology policy and regulation. She has held roles at the UK’s globally renowned Centre for Connected and Autonomous Vehicles (CCAV), Cabinet Office, the Department for Transport and the UN, along with award-winning British technology startups SwiftKey (AI software), Reconfigure.io, and GeoSpock (data analytics). She has been on the boards of TravelSpirit Foundation (mobility innovation), HackTrain, and Ada, the National College for Digital Skills.
Ansgar Koene - Horizon Digital Economy Research Institute & University of Nottingham
Dr. Ansgar Koene is a Senior Research Fellow at Horizon Digital Economy Research institute, University of Nottingham and chairs the IEEE P7003 Standard for Algorithm Bias Considerations working group. As part of his work at Horizon Ansgar is the lead researcher in charge of Policy Impact; leads the stakeholder engagement activities of the EPSRC (UK research council) funded UnBias project to develop regulation-, design- and education-recommendations for minimizing unintended, unjustified and inappropriate bias in algorithmic systems; and frequently contributes evidence to UK parliamentary inquiries related to ICT and digital technologies.
Ansgar has a multi-disciplinary research background, having previously worked and published on topics ranging from AI, bio-inspired Robotics and Computational Neuroscience to experimental Human Behaviour/Perception studies.
Yasemin J. Erden - St Mary's University
Yasemin is Senior Lecturer in Philosophy at St Mary’s University, and her research interests range from interdisciplinary (with science and technology) to philosophy of language, aesthetics, and ethics. Her most recent publications include topics in dialogue and education; thinking, agency and recognition, and; neural implants and human identity. She is Vice Chair of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB) and on the Council of the Royal Institute of Philosophy.
END OF SUMMIT
Vasco Pedro - Unbabel
Transforming Customer Service with Multilingual Conversations
How AI is redefining omni-channel customer service for the better? In a global market, where customers expect to be spoken to in their native languages, balancing exceptional customer experience across all markets, with ever increasing cost pressures and operational realities is tough. This interactive workshop will explore how AI plus humans can deliver exceptional customer experience, in 28 languages, at scale, with multilingual live chats.
Vasco Pedro is the co-founder and CEO of Unbabel, with a mission of enabling everyone in the world to understand and be understood in any language. He has a Ph.D. in Language Technologies from Carnegie Mellon and has served as a mentor at The Founder Institute and Beta-i, where he helps startups navigate the early stages of development, focusing on team building, technology challenges and building testable MVPs.
Startup Mentoring Session - BREAKOUT SESSION
The opportunity for startups to meet with leading AI investors, VCs and industry experts to pitch ideas, and gain advice on their business ideas and strategies. View the VC information here.