DL & RECOMMENDER SYSTEMS APPLIED
Kat James - Royal Mail
Offline applications of Recommender Systems at Royal Mail
Whilst recommender systems are regularly found in the context of digital marketing and campaign selections, these powerful algorithms have the flexibility to be applied to a wide range of business problems. At Royal Mail we have used a hybrid recommendation system to better target our B2B marketing communications and are currently exploring the possibility of using recommenders to aid decision making by a wide range of colleagues. In this talk I will cover the challenges of moving recommenders away from the traditional spaces they occupy and will discuss the power of combining business knowledge and recommendation algorithms to enable data driven decision making in an operational setting.
Kat James received her PhD from the University of Oxford in Statistical Genomics and completed a short Post Doc in Kumamoto, Japan working on the statistical challenges presented by whole blood RNASeq sampling in HIV-2 infected patients. Following on from roles at British Airways building solutions to destination recommendation problems and at Aviva, applying NLP techniques to customer complaints data, she is currently a Senior Data Scientist at Royal Mail, working on a variety of Data Science applications around optimisation, IoT devices, marketing and data-driven decision making. Current interests include recommender systems, AI, IoT and NLP.
Alessandro Magnani - Walmart Labs
Deep learning @walmartlabs
Machine learning finds many applications in e-commerce: search (ranking, query understanding, ...), pricing, entity resolution, product classification, recommendation and more. Deep learning has shown tremendous results in some of these applications. Walmart is actively using and exploring deep learning to improve our current machine learning based solutions and to solve problem that were previously beyond reach. In this talk I will offer an overview of some of the problems where deep learning is either used in production or under development at @walmartlabs.
Alessandro Magnani received his Ph.D. and M.S. from Stanford University in Electrical Engineering. He is currently a distinguished data scientist at @WalmartLabs, working on product classification, attribute extraction, and search. Prior to that, he was a research scientist at Adchemy where he worked on optimization algorithms for online advertising. His current interests are deep learning, machine learning, large scale computing and natural language processing.
Jian Li - Sky
Content Discovery with Semantic Flows
Recommendation system is a powerful tool to increase customer engagement and satisfaction for media industry. Existing approaches rely on measuring similarity between contents and similarity between customers. In this talk, I am introducing Sky’s patent-pending machine learning research by looking at the recommendation problem from a different angle: what is the benefit of learning dissimilarity? In particular, I will introduce semantic flow, a brand-new approach, to measure semantic distance between concepts and its capability on recommending exciting and surprising contents to customers.
Jian Li is principal data scientist and data science team manager in Sky. His team focuses on machine learning research for Sky’s content discovery products and services including search, recommendation, data supply and enrichment. His team’s research covers various topics including deep learning, natural language processing, ranking, semantic knowledge inference and supply chain optimisation. Before joining Sky, Jian was with Microsoft Research in Cambridge where he developed the personalised email classification product for Microsoft Exchange and Office 365 services. This product has been used by 50 million customers globally by the end of 2015. Jian holds a Ph.D in computer vision and experimental psychology from University of Bristol.
UTILISING NLP IN RETAIL & ADVERTISING
Yariv Adan - Google
Superhuman Conversational AI
AI has reached superhuman levels in various areas such as playing complex strategic and video games, calculating protein folding, and visual recognition. Are we close to superhuman levels in conversational AI as well? In his talk, Yariv will address this question, sharing some of the recent developments from Google Cloud AI, Google Brain Research, Deepmind, and Duplex - across speech recognition and generation, and natural language understanding.
For the past 10 years, Yariv has been leading products and product teams at Google Zurich. In his current role, he is leading the Zurich product team working on the Google Assistant. Prior to this role, Yariv worked on a wide range of products, including proprietary Google infrastructure, privacy and security, products designed for the Emerging Markets, and even the notorious non skippable ads on YouTube. Before joining Google, Yariv was an engineering manager in various Israeli start-ups and companies.
Dima Karamshuk - Skyscanner
Learning Cheap and Novel Flight Itineraries
As a leading travel meta search engine, Skyscanner is dedicated to provide the best flight deals available on the Internet. Towards this goal, we consider the problem of efficiently constructing cheap and novel flight itineraries resulting from combining legs from different ticket providers. We analyze the factors that contribute towards the competitiveness of such itineraries and formulate the problem of predicting competitive itinerary combinations. We consider a variety of supervised learning approaches to model the proposed prediction problem and put forward a number of practical considerations for implementing them in production.
Dima (@karamshuk) is a Senior Data Scientist at Skyscanner where his focus is on applying data mining and machine learning techniques for optimizing content caching and distribution. Prior to Skyscanner, Dima was with King's College London where he worked on analysis of BBC iPlayer (a joint project with BBC) and various social media websites (Twitter, Pinterest, Foursquare, etc.). He is an active contributor to the computer networks (Infocom, ComMag, etc.) and data mining communities (KDD, WWW, etc.). Dima's work has been featured in New Scientist, BBC News and other media outlets. He also co-founded and was a former CEO of stanfy.com. More information can be found here - https://karamshuk.github.io/.
Nikolaos Lamprou - Vodafone
Deep Learning as a Catalyst for Decoding Customer Behaviour
The smartphone has become an extension of one’s self. It has become the focal point of any activity or interaction a user has with the world including communication, entertainment, commerce, banking and navigation. As a consequence, information regarding the utilisation of such devices can reveal useful insights about a user’s behaviour and needs. At Vodafone our mission is to use data to develop personalised products and services and build a personalised relationship with customers in order to offer them a unique customer experience. In my talk, I will describe how deep learning techniques are being used at Vodafone to better understand how our customers interact with our products and how that knowledge can help us to make appropriate, more relevant propositions.
Nikolaos Lamprou is a senior data scientist at Vodafone UK where he is a member of the Big Data and Advanced Analytics team. He has worked on various use cases within the telco industry for the last 4 years where he has championed the use of new techniques and technologies. Nik holds physics degrees from the universities of Oxford and Athens but his interests now lie in all things Deep Learning, Distributed Computing, Reinforcement Learning, and Natural Language Processing.
COMPUTER VISION & IMAGE RECOGNITION
Merve Alanyali - Warwick Business School & The Turing Institute
Using Deep Learning to Identify Human Behaviour with Flickr Photographs
A mammoth amount of data is being generated by our daily interactions with technological devices and online services. With the improved connectivity, online information has been shifted from being text based to visual media such as as photographs. Offering independent, cheap and rapidly available ways to quantify human behaviour. In my talk, I will focus on using deep learning to analyse photographs shared on Flickr to identify protest outbreaks around the world.
Merve Alanyali is a doctoral researcher at Warwick Business School and academic assistant at The Turing Institute. Her research focuses on analysing large open data sources with concepts from image processing and machine learning to understand and predict human behaviour at a global scale. Her work has been featured by television and press worldwide including coverage in the Financial Times and Bloomberg Business. Alanyali currently holds a Chancellor’s International Scholarship at the University of Warwick. She was awarded a double Masters degree in Complex Systems Science by the University of Warwick, UK and Chalmers University of Technology, Sweden and draws on an interdisciplinary background in computer science, complex systems and behavioural science.
Jenny Griffiths - Snap Tech
The impact of AI and visual search on the fashion and publishing industry
We’re all being told that AI and ML are going to impact the fashion and publishing industry - but in what ways? Jenny Griffiths from Snap Tech takes a deep dive into the different platforms and products that visual search can touch, with real world examples of everything from shoppable red carpets on the BAFTAs and celebrity editorial on the red carpet, to creating new shops powered by AI for brands in under a fortnight, and bringing the power of visual search to fitting rooms. She’ll focus on the impact from a consumer point of view, but also the impact on the bottom line of businesses, and the ROIs you should be expecting.
Jenny Griffiths is the founder of Snap Tech and Snap Fashion; the multi-award winning visual search engine for fashion. Jenny invented Snap Tech’s computer vision algorithms with the ambition of changing the way that people search and shop online. Jenny formally incorporated Snap Fashion aged 23, and has been scaling the company for over 6 years, with Snap HQ being based in London. She has received a number of awards for her work with Snap Fashion, from being named in Forbes’ 30Under30 list for technology, to receiving an MBE for her Services to Innovation in the Digital Fashion Industry.
Shuai Yuan - MediaGamma
Enabling Deep Learning in Real-Time Bidding
Machine learning and AI have been applied extensively in many tasks of computational advertising, like user profiling and yield management. Models are getting more and more complex for these tasks in recent years. However, for bidding optimisation in Real-Time Bidding, it is still hard to apply complex models because a bidder is required to return a prediction in a few ms with thousands of QPS workload. In this talk, I briefly introduce MediaGamma’s end-to-end implementation of the bidding engine. Besides loading a Neural Network model, the process includes a few more challenges like guaranteeing the feature consistency, augmenting bid requests with additional features, and keeping tracking performance of each iterations.
Dr. Shuai Yuan is the VP Data Science of MediaGamma. He supervises and works with the team to build end-to-end data model pipelines to solve challenging problems. He joined the company in 2014 after gaining his Ph.D. in University College London. He has worked with a number of companies such as AppNexus and Bright (acquired by Linkedin) on various topics in computational advertising, including bidding algorithms in RTB and floor price optimisation. He has published several papers and co-hosted the tutorials on RTB in top-tier venues including CIKM, SIGKDD, WSDM, ECIR, and ADKDD. He also published the first empirical study on RTB auctions.
PERSONALISATION & CUSTOMER BEHAVIOUR
PANEL: How Can You Best Enrich the Customer Experience Using AI?
Jonna Fassbender - StyleScript
StyleScript enables every eCommerce platform to deliver their consumers personal style advice by harnessing big data, business intelligence solutions and predictive analyses. It saves time and enriches customer experience. Lifestyle and consumer focused organization with a large assortment (big data) especially benefit from easy to plug-in API solution.
Adi Chhabra - Vodafone
Evolution of AI & Machine Learning in Customer Experience - Beyond Interfaces
The evolution of Machine learning is directly proportional to the customer believing in the non-existence of a machine in between. With deep learning and neural network implementation, the traditional ML models are becoming dated. Often when a new technology has its breakthrough; it’s impact is only felt in hindsight. But it's different with Artificial Intelligence and Machine Learning. Let’s talk about how ML powered Chatbots add value to the customer experience. From demand generation to fulfilment, all behind a seamless customer experience.
Adi is a Product Manager with over 11 years of experience across Fashion, E-commerce and Technology Industries. He is currently leading Product Management and Innovations for Vodafone UK in the space of Artificial Intelligence and Machine Learning. He is passionate about solving problems through design thinking, big data and automation. An avid reader of consumer technology and applied mathematics, he often spends his time thinking about how to merge the two to make consumer experiences worth their while. Adi holds an MBA degree from Lancaster University Management School.
Adam McMurchie - NatWest
Five years ago Adam talked here at ReWork on the future of AI in banking, he laid out a roadmap of things to come - previewing China’s Smile to Pay (which shortly after ballooned to 400 million users), smart contracts, personalised banking and other initiatives that have come to fruition. That said, Adam had also warned on the grave consequences of failure and significant challenges to arise both practical and regulatory which closely describes the state of quagmire we now find ourselves in.
From Alexa telling a 10-year old girl to touch a live plug with a penny, to regulatory breaches such as Uber test driving autonomous cars without state permission, running 6 red lights as a result.
Finance hasn’t been spared the controversy either, with multiple institutions losing significant wealth from poorly deployed AI, to colossal investment wastage by backing the wrong AI sectors.
In this talk Adam will share key insights & learnings on how organisations can better traverse these pitfalls by designing, building and deploying finance driven AI that is both sustainable and cost effective. He will also layout key milestones to enable future proofing imminent threats of supply chain failures and global talent shortages so that we can navigate the next five years of AI in finance with confidence.
Adam McMurchie is the Lead Cloud Data Engineer at NatWest. He was previously the 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.
Julia Morrongiello - Global Founders Capital
Julia Morrongiello is an investor at Global Founders Capital (GFC), a stage agnostic venture fund. The fund has invested in the likes of Facebook, Linkedin, Slack, Funding Circle and Kreditech. Since joining GFC, Julia has worked on numerous AI deals including DigitalGenius and has also led all of the fund’s blockchain and crypto initiatives. Previously, she helped set up Innovate Finance, the UK’s fintech industry body. During her time there, she worked with numerous fintechs and large financial institutions, helping to grow the organisation from 10 to over 250 member companies.
Nick Holzherr - Whisk.com
Data-Driven Food Recommendations
A behind-the-scenes glimpse into The Food Genome™ and how Whisk maps food relationships with data on nutrition, price, flavour, and other properties. With a team highly advanced in natural language processing, Whisk uses NLP and ontologies of food to understand recipes and then machine learning to personalise recommendations based upon an individual’s food preferences, recipe content they save, and groceries they buy.
Nick Holzherr is founder and CEO of Whisk.com, a path-to-purchase technology startup that makes all online content -- recipe pages, video, display, search or social campaigns -- shoppable on any platform. Since Whisk’s launch in 2012, the startup has partnered with many of the UK's biggest retailers, FMCG brands and digital food publishers. Nick raised venture capital to fund Whisk after pitching the initial idea to Lord Sugar at the final of BBC The Apprentice 2012. Nick has been awarded Business Insider’s Emerging Entrepreneur of the Year, PPA Digital’s “Newcomer of the Year,” The Drum’s "One to Watch,” and The Grocer’s "Top New Talent". He is a frequent industry speaker and has been featured in The Guardian, The Telegraph, BBC, Internet Retailing, Management Today, Food Manufacturer and The Next Web. Prior to Whisk.com, Nick founded two successful start-ups after graduating with a 1st in International Business from Aston University.
Adam Hornsby - dunnhumby
Where Deep Learning Fails
Recent progress in deep learning has garnered considerable excitement throughout the data science community. Many now believe that to improve a model's performance, you just need to "throw more deep learning at it". In this talk, I'll explain why such thinking can be costly. I'll highlight some notable cases where deep learning has failed to improve performance compared to simpler approaches. In particular, I'll deep-dive into the problem of modelling structured data, which is typical across retail, marketing and more. I will try to provide some intuition about why deep learning struggles in these domains and suggest some alternative approaches to try first.
Adam is a Senior Data Scientist at dunnhumby, where he builds and deploys machine learning algorithms at scale for some of the world's largest retailers. He is also a part-time Experimental Psychology PhD student at UCL, funded by dunnhumby. His research aims to better understand customer purchase behaviour through a combination of data science, machine learning and cognitive modelling.
Conversation & Drinks
Denis Troyanov - Lalafo
How to Solve Product Classification Challenge for Marketplaces in the Wild
In the pursuit of improved user experience, marketplaces are trying to solve open issues using modern approaches. Today, it seems that AI can easily solve a lot of narrow problems but when it faces real-world questions, it turns out that those questions are much deeper than they seem at first glance. Since we are following current industry dynamics in AI, we have started with image classification. We’ll tell you about our experience with real-world problems associated with this task. How we went from a flat-model to one that is ‘super-complex’ as well as what was successful and what wasn’t.
Denis Troyanov is a young researcher at Lalafo, a mobile C2C marketplace powered by artificial intelligence. After earning a degree in computer science at South-Ural State University, Denis moved to Kyiv, Ukraine to work as a research engineer with an emphasis on deep learning. He has completed several projects in computer vision and deep learning and worked with classical computer vision for a 'face' and gesture processing company with DL models for the fashion and entertainment industry. Denis is currently working on very deep classification for 10k+ classes for e-commerce under product constraints such as unbalanced classes, weak labelling, constantly evolving catalog of classes etc. Denis is passionate about continuing research in machine learning and topics such as psychology, game theory, reinforcement learning, how memory works and decision making concepts.
Chlump Chatkupt - PlaceMake.io
AI for Investment Management and Retail Optimization
Location industries—industries, such as real estate and retail, with salient location components and interests—stand to be transformed by and to benefit profoundly from the proliferation of data and AI, which can be expected to drive everything from capital allocation and the development and deployment of investment strategies and vehicles to placemaking and the valuation and positioning of assets. We explore how we at PLACEMAKE.IO leverage AI to drive investment management and the optimization of large-scale retail portfolios.
Tech founder. Mathematician. AI scientist. Economist. Engineer. Writer. Artist. Musician. Swimmer. Boundlessly curious person. Whether as a researcher at LSE, a strategic advisor to Abbott Laboratories, or the mathematical mind behind a boutique consulting firm, Chlump has made a career of developing novel strategies and building mathematical models, technologies, and algorithms. He has written on a variety of subjects ranging from mathematics to multilateral refugee and asylum policy. The goal of his PhD was to revolutionize the field of game theory. His current goal is to revolutionize the world of location and mobility. He holds a BA in Economics from Northwestern University, an MA in Philosophy from NYU, and a PhD in Mathematics from LSE.
Roman Steinberg - uKit Group
Using Deep Learning in Website Building
Integrated recommendation systems & support chat-bots can be found web-wide. But AI, Machine Learning, Deep Learning & Generative Design strengthen their positions in website building and designing industry — branding, front-end coding and even working with layouts. In this speech, we will discuss today's achievements and future possibilities of website building: from a simple ability of AI to evaluate how people will react to your website to creating a personalized version of this website for a particular visitor exploring their data and online behavior.
Roman works as Leading Data Scientist at uKit Group, SaaS for websites developing company. He leads the neural networks training team at uKit AI, automated websites design and personalization project. Being a Ph.D. in Applied Mathematics, Roman has 3 years of experience in commercial projects in the field of machine learning, computer vision and data mining.
Francois Chaubard - Focal Systems
Focal Systems Will Help Retailers Beat Amazon Go
Can a small inexpensive camera system on a cart beat Amazon Go? At Focal Systems, we have taken a completely different approach to solving automated checkout. We use Deep Learning Computer Vision and sensor fusion to accurately track every item that enters or exits the shopping trolley. The result is a much more scalable solution that allows large format stores to adopt auto checkout, delivering positive ROI to the store and a frictionless experience for shoppers.
Francois was an EE masters, and then CS masters from Stanford University. He researched Deep Learning and Computer Vision under Fei Fei Li in the Computer Vision and Geometry Lab. Francois worked as a Deep Learning Researcher at Apple working on secret projects. Before that, he was a Missile Guidance Algorithm Engineer at Lockheed Martin working on Kalman filter/Information theory. In his current role, in addition to leading the company, Francois heads up Deep Learning Research at Focal Systems.
Tom Charman - KOMPAS
How are recommendations changing? Using machine learning to disrupt retail and advertising.
Is machine learning in forms such as k-means clustering changing the way that businesses make recommendations to people? How can we use these technologies to understand people better than previously thought, and make more specific, and tailored suggestions to customers? This presentation focuses on the importance of leveraging data, with the intention of training machines to learn about behavioural patterns, and make recommendations. We look at the accuracy of these recommendations, and how we can test the success of such machines and algorithms.
Tom aims to disrupt the consumer space of travel by harnessing machine learning and applying algorithms to the problem of personalisation. He gave a TEDx talk on the ‘Future of Technology’, and is a regularly hosted speaker by blue-chip companies, talking about AI and its impact on corporates. After being featured in international publications including the TATA Consultancy report and Success magazine, he’s focused on creating a seamless travel experience. He’s advised the UK government, reflected on proposed regulations on deep technology with the European Parliament and was recently named as one of the '20 young entrepreneurs to watch in 2017' by startups.co.uk, and the ‘Future Face of Innovation and Technology’ by the Chamber of Commerce.
Fabio Daolio - ASOS.com
A Multi-Task Multi-Modal approach to Product Classification
For any retail business, having a consistent understanding of its products is the first step towards being able to understand and forecast customers’ behaviour. In this talk, we discuss the approach that we have taken at ASOS, a global fashion retailer with an ever-evolving catalogue. In particular, we show how Deep Learning can leverage unstructured and partially-labelled data in order to consolidate product attributes.
Fabio is a computer scientist with 8 years of research experience in Evolutionary Computation, Complex Networks and Combinatorial Optimisation. Following his interests in Statistics and Machine Learning, he is currently working as a Data Scientist at ASOS.
Xavier Giro-i-Nieto - Universitat Politècnica de Catalunya
Cross-Modal Machine Translation
The advances on neural machine translation across natural language have opened new venues in the field of cross-modal analysis. Given the unified the machine learning framework broadly adopted by the language and vision communities, novel opportunities have arisen by using deep learning framework to transform across modalities. This talk will provide an overview of the state of the art on cross-modal translation and (eg. lipreading, facial animation, sign language) and present our work in speaker visualization from speech.
Xavier Giro-i-Nieto is a learning enthusiast working as an associate professor at the Universitat Politecnica de Catalunya (UPC), in Barcelona, and a certified instructor at the NVIDIA Deep Learning Institute. He has been a visiting scholar at Columbia University and works regularly with Dublin City University, the Barcelona Supercomputing Center and Vilynx. His research interests focus on deep learning for computer vision, speech and natural language processing. His current service includes associate editor of the IEEE Transactions in Multimedia. Xavier Giro-i-Nieto is a learning enthusiast working as an associate professor at the Universitat Politecnica de Catalunya (UPC), in Barcelona, and a certified instructor at the NVIDIA Deep Learning Institute. He has been a visiting scholar at Columbia University and works regularly with Dublin City University, the Barcelona Supercomputing Center and Vilynx. His research interests focus on deep learning for computer vision and natural language processing applied to large scale image retrieval, affective computing, lifelogging from wearables and visual saliency prediction. His current service includes associate editor of the IEEE Transactions in Multimedia and ACM SIGMM Records.
Honglei Li - Northumbria University
Improving Online Customer Shopping Experience with Computer Vision
Computer vision and pattern recognition has achieved great developments in last decade, especially the feature categorizing and detection. How to exploit the new techniques in this research area has rarely discussed in the information systems field. This study aims at exploring the opportunities from the most recent development from computer vision area from the online shopping experience perspective. We discussed the possibility of extracting meaningful information from images and apply this to the online recommendation system to improve online customer shopping experience. Most existing online recommendation systems are making recommendations based text tags leaving large information from images un-used. Machine learning algorithm can use used to extract meaningful information such as texture, colour, and style information and classify them into different categories. This research will benefit those websites especially for that pure e-commerce website relying on online recommendation systems.
Dr. Honglei Li is currently a senior lecturer in Enterprise Information Systems at Department of Computing & Information Sciences, Northumbria University. Holding a PhD and M.Phil in Management Information Systems and a BSC in computational mathematics, she is enthusiastic about transforming our life through new technologies. She has been working on several research projects including Improving Shopping Experience with Image Processing Technology, Creating Digital Government Platform for Better Public Engagement, Smart City, and Virtual Community Participation through Interpersonal Relationships. She is an active researcher publishing papers in top journals such as Information & Management and Internet Research.
Sam Lloyd - Travis Perkins
Sam Lloyd is the Group Analytics Manager for Travis Perkins plc, the UK’s leading builders' merchant and home improvement retailer. Working with over 21 businesses across both the B2B and B2C sectors, Travis Perkins plc have an annualised turnover of over £6bn which creates plenty of exciting opportunity for the value-adding deployment of advanced analytics. Sam joined TP plc in 2016, having achieving an MBA with distinction from Warwick Business School. Prior to this he worked for a global leader in offshore engineering and consultancy, where he was technical manager for some of the highest profile offshore energy projects in recent years. Sam has over 10 years experience in overseeing teams in the collection, analysis and reporting of complex data sets with the specific aim of creating value from data-driven decision making. He is interested in predictive/prescriptive analytics and machine learning applications, architected through Google Cloud Platform, for large scale deployment.
PLENARY SESSION: PRIVACY, SECURITY & ETHICS
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?
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.
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.
Ansgar Koene - University of Nottingham
Given the rapid growth of AI deployment, successful implementation of risk-based AI regulations will require AI risk assessments to be conducted at a scale that will be difficult to achieve without some level of automation. The need for automated AI risk assessments is further emphasised by the need to perform post-deployment monitoring.
In his talk I will present the findings of a survey on AI risk assessment methodologies, outlining commonly identified assessment factors. Based on these survey results I will discuss key challenges and potential approaches towards automation of the AI risk assessments that will be required by risk-based AI regulations.
risk-based AI regulations will require large scale risk assessments of AI application; AI risk assessment involves evaluation of multiple technical and non-technical risk factors; AI can play an important role in automation of AI risk monitoring.
Dr. Ansgar Koene is an AI Regulatory Advisor at the EY Global where he supports the AI Lab’s Policy activities on Trusted AI. He is also a Senior Research Fellow at the Horizon Institute for Digital Economy Research (University of Nottingham). Ansgar chairs the IEEE P7003 Standard for Algorithmic Bias Considerations working group, is the Bias Focus Group leader for the IEEE Ethics Certification Program for Autonomous and Intelligent Systems (ECPAIS), and a trustee for the 5Rgiths foundation for the Rights of Young People Online. Ansgar has a multi-disciplinary research background, having worked and published on topics ranging from Policy and Governance of Algorithmic Systems (AI), data-privacy, AI Ethics, AI Standards, bio-inspired Robotics, AI and Computational Neuroscience to experimental Human Behaviour/Perception studies. He holds an MSc in Electrical Engineering and a PhD in Computational Neuroscience.
END OF SUMMIT