Deep Learning Summit schedule

Iterative Approach To Improving Sample Generation

Generative modelling allows us to synthesise new data samples, whether the samples be used for imagining new concepts, augmenting datasets or better understanding our datasets. When synthesising samples, generative models don’t always get it right first time - the samples may not be sharp, may have artefacts or may be nonsensical. I will present recent research that shows how we can improve these samples, by applying an iterative procedure to get slightly better samples with each step.

Antonia is a PhD candidate at Imperial College London, in the Bio-Inspired Computer Vision Group. Her research focuses on unsupervised learning and generative models. She received her masters in Biomedical Engineering from Imperial College London with an exchange year at the University of California, Davis. Antonia has interned at DeepMind, Twitter (Magic Pony), Cortexica and UNMADE.

Shape Intelligence: The Missing Link in Spatial Computing

As spatial computing rapidly evolves, and as 3D data proliferates exponentially, a core missing component of the technology and application landscape has been intelligent understanding of physical shapes and objects. While reconstruction, visualisation and detection are advancing in leaps and bounds, semantic recognition, matching and analysis of shapes remains work-in-progress. This talk will share the vision of 3D Industries in this domain and how, alongside its industry partners, the company is developing proprietary technologies and techniques to deliver powerful solutions across various sectors.

Seena Rejal is Founder and CEO of 3D Industri.es (3DI). His work has been featured in Forbes, Inc.com and the BBC and he has presented internationally at SXSW, WebSummit, CES, DLD and the Hanover Messe, amongst others.

Prior to this, Seena was active in the cleantech space, working with groundbreaking and revolutionary firms in the carbon capture and solar energy fields. Preceding that, he was head of technology for the acclaimed Clinton Climate Initiative (CCI), President Clinton’s programme on mitigating climate change. He worked closely with the global research and technology communities, and coordinated with policy and finance leaders to support accelerated deployment of the most high-impact solutions.

Seena holds Master's and Doctoral degrees from the University of Cambridge where he was supervised by Prof. Sir Mike Gregory.

Semantic descriptors for 3D object understanding in AR

We present a method for jointly analyzing images, 3D objects and text, to generate a unified semantic descriptor that captures the shape and class of objects from color images. This creates an AI agent that predicts semantic and geometric scene data from the physical world as humans do. We show how such a system is used at Selerio to pull physical objects into the virtual world for Augmented Reality applications.

Flora is co-founder of the AR startup Selerio, and a recent Ph.D. graduate in Computer Vision at the University of Cambridge. Her publications in several top-tier venues cover topics at the intersection of Graphics, Vision, and NLP such as sketch-based modeling or joint analysis of images and text for 3D retrieval. She also holds the 2014 Google European Doctoral Fellowship in Computer Graphics for her work on retrieving 3D models using images and sketches. Selerio, a Cambridge spin-out, builds on this work to provide developers with live 3D reconstruction and editing of real scenes, for more engaging AR experiences. Selerio is backed by investors such as Entrepreneur First and Betaworks.

The Future of Audiobooks

DeepZen focuses on using neural networks to produce audiobooks and voiceovers with humanlike speech quality while reducing the cost and production time.

Kerem is the CTO and co-founder of DeepZen which specializes in building end to end AI solutions that generate emotional and expressive synthetic voice that’s indistinguishable from humans.

Kerem has been an entrepreneur for the majority of his career. He has extensive experience in machine learning and more recently, Artificial Intelligence. He also took management roles at corporation like Oracle and GE.

He has a BS in Information Technology from Rensselaer Polytechnic Institute in Troy, New York and currently resides in London.

Enhancing Cryptocurrency Forecasting by using Deep Learning Sentiment Analysis

Peculium is the first Crypto-Savings platform that combines traditional savings, blockchain technology, cryptocurrency, and artificial intelligence. Indeed, Peculium is a platform that makes use of the AIEVE Artificial Intelligence technology to forecast the market price of several cryptocurrencies and giving real-time saving-portfolios advices.

In this context, AIEVE is based on cutting-edge NLP techniques to extract semantic meaning and sentiment from large volumes of unstructured text from multiple sources such as social media or RSS feeds. In this presentation, we will present a Twitter sentiment analysis pipeline based on CNNs and LSTMs networks using fine-tuned word embeddings. We will show how these techniques help AIEVE to predict the cryptocurrency market with a higher level of accuracy to increase users savings.

Prior to joining AIEVE, Kamel was working as a Senior NLP Data Scientist at First Utility developing AI technology to improve the customer experience. Kamel has also worked at Oxford University Press as a Lead Language Technologist for the Oxford English Dictionary project. During his Ph.D at the University of Geneva in Switzerland, he has been working on several topics such as NLP, Machine Learning and Semantic Web.

End-to-End Continuous Machine Learning in Production with PipelineAI, Spark ML, TensorFlow AI, PyTorch, Kafka, TPUs, and GPUs

Traditional machine learning pipelines end with life-less models sitting on disk in the research lab. These traditional models are typically trained on stale, offline, historical batch data. Static models and stale data are not sufficient to power today's modern, AI-first Enterprises that require continuous model training, continuous model optimizations, and lightning-fast model experiments directly in production. Through a series of open source, hands-on demos and exercises, we will use PipelineAI to breathe life into these models using 4 new techniques that we’ve pioneered: * Continuous Validation (V) * Continuous Optimizing (O) * Continuous Training (T) * Continuous Explainability (E).

The Continuous "VOTE" techniques has proven to maximize pipeline efficiency, minimize pipeline costs, and increase pipeline insight at every stage from continuous model training (offline) to live model serving (online.) Attendees will learn to create continuous machine learning pipelines in production with PipelineAI, TensorFlow, and Kafka.

Chris Fregly is Founder at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs." Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.

Exploring galaxy evolution with deep learning

We are currently living an exciting epoch. Thanks to rapidly improving technology, astronomy is entering the big data era. New NASA/ESA surveys that will be available in 2-5 years will contain multi-wavelength images of billions of galaxies and spectra for many tens of millions (e.g. EUCLID, WFIRST). The increase of computing power has also enabled us to run hydrodynamic numerical simulations that incorporate our knowledge of physics in a cosmological context and produce large amount of simulated data, spanning most of the Universe’s life. Deep learning appears as an unavoidable solution to analyze the huge volume of data available to the community. But not only that, it also brings new opportunities to find new observables and tighten the link between theory and observations. In the last years our group has pioneered the use of deep learning techniques in astronomy. I will review some of our key results and their impact in our understanding of how galaxies form and evolve.

I am an associate professor at the Observatoire de Paris and Université Paris Diderot. I am a recognized expert in the fields of galaxy morphology and massive galaxy formation I defended my PhD in 2009 and after a short postdoctoral experience of less than a year I obtained a permanent position at the Paris Observatory, being the youngest scientist hired at the institution in the last 10 years. I was one of the first researchers in applying classical machine learning techniques to the classification of galaxy morphologies during my PhD back in 2008. When I started working on this topic, the presence of such techniques in astronomy was marginal. Since 2013, I have been pioneering the use of deep learning techniques to improve our understanding of galaxy evolution.

Data Technologies & Applied ML - ML Pipeline

Rob’s career history to date has been within the Investment Banking Technology sector. He has held various senior positions working either for centralised IT organizations or directly for key business lines. He has held fellowship titles at his last two employers, Credit Suisse and Goldman Sachs. During his career he has acquired deep technical knowledge and expertise on areas ranging from performance development methods in software or hardware, more recently his focus has turned to big data technologies and their application within the Investment Banking arena, specifically artificial intelligence and scientific computing

Rob is currently a technical Managing Director at Barclays International where he holds the position of Global Head of the Data Technologies and Applied Machine Learning Team.

Learning Representations for Hyperparameter Transfer Learning

Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization, such as hyperparameter optimization, critical in deep learning. Typically, BO relies on conventional Gaussian process regression, whose algorithmic complexity is cubic in the number of evaluations. As a result, Gaussian process-based BO cannot leverage large numbers of past function evaluations, for example, to warm-start related BO runs. After a brief intro to BO and an overview of several use cases at Amazon, I will discuss a multi-task adaptive Bayesian linear regression model, whose computational complexity is attractive (linear) in the number of function evaluations and able to leverage information of related black-box functions through a shared deep neural net. Experimental results show that the neural net learns a representation suitable for warm-starting related BO runs and that they can be accelerated when the target black-box function (e.g., validation loss) is learned together with other related signals (e.g., training loss). The proposed method was found to be at least one order of magnitude faster than competing neural net-based methods recently published in the literature.

Cedric is the science lead of Amazon Core AI, with teams in Berlin, Barcelona, Tuebingen, and Seattle. His work on democratizing machine learning enables teams at Amazon deliver a wide range of machine learning-based products, including customer facing services such as Amazon SageMaker (aws.amazon.com/sagemaker). Currently, he is interested in algorithms that learn representations, algorithms that learn to learn, and algorithms that avoid catastrophic forgetting (in deep learning). Prior to joining Amazon, he led the Machine Learning group at Xerox Research Centre Europe (now Naver Labs Europe). His team conducted applied research in machine learning, computational statistics and mechanism design, with applications in customer care, transportation and governmental services. He joined Amazon, Berlin, as an Applied Science Manager in October 2013, where he was in charge of delivering zero-parameter machine learning algorithms.

End-to-End Deep Learning for Detection, Prevention, and Classification of Cyber Attacks

With more than a million new malicious files created every single day, it is becoming exceedingly difficult for currently existing malware detection methods to detect most of these new sophisticated attacks. In this talk, we describe how Deep Instinct uses an end-to-end deep learning based approach to effectively train its brain on hundreds of millions of files, and thus providing by far the highest detection and prevention rates in the cybersecurity industry today. We will additionally explain how deep learning is employed for malware classification and attribution of attacks to specific entities.

Dr. Eli David is a leading expert in the field of computational intelligence, specializing in deep learning (neural networks) and evolutionary computation. He has published more than thirty papers in leading artificial intelligence journals and conferences, mostly focusing on applications of deep learning and genetic algorithms in various real-world domains. For the past ten years, he has been teaching courses on deep learning and evolutionary computation, in addition to supervising the research of graduate students in these fields. He has also served in numerous capacities successfully designing, implementing, and leading deep learning based projects in real-world environments. Dr. David is the developer of Falcon, a grandmaster-level chess playing program based on genetic algorithms and deep learning. The program reached the second place in World Computer Speed Chess Championship. He received the Best Paper Award in 2008 Genetic and Evolutionary Computation Conference, the Gold Award in the prestigious "Humies" Awards for Human-Competitive Results in 2014, and the Best Paper Award in 2016 International Conference on Artificial Neural Networks. Currently Dr. David is the co-founder and CTO of Deep Instinct, the first company to apply deep learning to cybersecurity. Recently Deep Instinct was recognized by Nvidia as the "most disruptive AI startup".

Jade is a researcher with the Governance of Artificial Intelligence Program (GovAI) at the Future of Humanity Institute (University of Oxford). Her research focuses on the governance of emerging dual-use technologies, with a specific focus on firm-government relations in the US and China with respect to advanced artificial intelligence. Jade has a background in engineering, international law, and policy design and evaluation.

Alison leads the Humanities work at the PHG Foundation, a health policy think tank which is part of University of Cambridge. Her research focuses on the regulation and governance of genomic data for clinical care and research, the impact of automated processing and artificial intelligence on existing legal and ethical frameworks, and the challenges and opportunities associated with delivering personalised healthcare. Alison has professional qualifications in law and nursing and a masters qualification in healthcare ethics.

Andrea Renda is an Italian social scientist, whose research lies at the crossroads between economics, law, technology and public policy. He is Senior Research Fellow and Head of Global Governance, Regulation, Innovation and the Digital Economy at the Centre for European Policy Studies (CEPS). From September 2017, he holds the Chair for Digital Innovation at the College of Europe in Bruges (Belgium) where he has also leading the course “Regulatory Impact Assessment for Business” since 2007. He is also a non-resident fellow at Duke University's Kenan Institute for Ethics. Over the past two decades, he has provided academic advice to several institutions, including the European Commission, the European Parliament, the OECD, the World Bank and several national governments around the world. An expert in technology policy and better regulation, he is a member of the ESIR (Economic and Social Impacts of Research) expert group of the European Commission; and a member of the EU Blockchain Observatory and Forum. He is also a member of the Editorial Board of the international peer-reviewed journals “Telecommunication Policy” (Elsevier) and of the European Journal of Risk Regulation (Lexxion); a member of the Scientific Board of the International Telecommunications Society (ITS) and Chair of the Scientific Board of European Communications Policy Research (EuroCPR). He holds a Ph.D. degree in Law and Economics awarded by the Erasmus University of Rotterdam.

Dr Matthew Fenech is an artificial intelligence policy consultant, with expertise in developing and advocating for policies that maximise the opportunities and minimise the risks of these technologies. His main interest is in the ethics and practicalities of the use of AI in healthcare, a field to which he brings his 10 years of experience working as a hospital doctor and clinical academic. He has also authored reports about AI and other emerging technologies in low- & middle-income countries, and on the impact of automation on the future of work. He regularly speaks about these topics in lectures and in the media.

Loubna Bouarfa is a machine learning scientist turned entrepreneur.

In 2016, after several years in academia, Loubna founded her own artificial intelligence company: OKRA Technologies. OKRA is a data analysis platform, using deep machine learning algorithms to transform complex datasets into evidence-based predictions, in real time. The platform was designed to equip Healthcare and Life Sciences professionals with the foresight to improve patient outcomes.

Before OKRA, Loubna spent over 10 years validating and implementing machine learning (ML) solutions for real-world applications, such as an autonomous ML system that tracks surgeons’ operating movements and prevents error in real time.

Loubna has won several awards and was recognised as a leading innovator by the MIT Technology Review in 2017.

Beyond her business, Loubna has recently been appointed by the European Commission as a High-Level Expert on Artificial Intelligence. She will support the EU by developing recommendations on ethical, legal and societal issues related to AI, impacting the health, safety and freedom of the wider society.

On a personal level, Loubna is a strong advocate for diversity and challenging the status quo. Having lived in Morocco, moving to the Netherlands at the age of 17, and later to the UK with a young family, she realised the power of remaining outside her comfort zone.

Alice reviews Hazy's product features for AI ethics, data privacy and compliance and helps define Hazy's core message to the world. Prior to joining Hazy Alice coordinated the cross-party parliamentary group on AI (APPG AI), helping the UK Government to address ethical implications and design new standards for applying machine learning in commercial, political and social areas.

Alice has over 10 years of experience in policy, research, product management and marketing, and a particular focus on such fields as artificial intelligence, big data and tech for good. Having worked in national and international public and private sector organisations, social enterprises and NGOs, Alice has a proven track record in delivering the strategic vision and showcasing impact to a wide range of stakeholders.

Aimee van Wynsberghe has been working in ICT and robotics since 2004. She began her career as part of a research team working with surgical robots in Canada at CSTAR (Canadian Surgical Technologies and Advance Robotics). She is Assistant Professor in Ethics and Technology at TU Delft in the Netherlands. She is co-founder and co-director of the Foundation for Responsible Robotics, on the board of the Institute for Accountability in a Digital Age, and an advisory board member for the AI & Intelligent Automation Network. Aimee also serves as a member of the European Commission's High-Level Expert Group on AI and is a founding board member of the Netherlands AI Alliance. Aimee has been named one of the Netherlands top 400 influential women under 38 by VIVA and was named one of the 25 ‘women in robotics you need to know about’. She is author of the book Healthcare Robots: Ethics, Design, and Implementation and has been awarded an NWO personal research grant to study how we can responsibly design service robots. She has been interviewed by BBC, Quartz, Financial Times, and other International news media on the topic of ethics and robots, and is often invited to speak at International conferences and summits.

Caryn is an Analytics Strategist operating at the intersection of applied analytics and law/ethics.

She advises senior decision-makers on analytics strategy, target operating model and analytics business case and manages technical teams to operationalise and realise these strategies. She also manages Accenture’s Responsible AI practice in the UK where she helps clients confidently deploy responsible AI models with technical, organisational, governance and brand considerations. This involves working with multidisciplinary teams, industry experts and academic institutes.

Caryn graduated from London Business School and holds a law degree from BPP University, both as a merit scholar.

This is a 2-hour workshop with a maximum of 20 places, and registration is required. To register your interest, please complete the registration form here. You must already be registered for the summit in order to attend. An email will be sent to confirm your place.

When searching for “professor” or “CEO” on Google images, the results show overwhelmingly white male pictures. While these jobs are held by white male professionals more often, the image search results present an extreme bias against representing women and people of color. This has been pointed out as an ethical problem in various outlets; however the problem persists.

In this workshop, we use this case as an example on how to structure the ethical problem at hand and its underlying principles before moving on to try solving it. Through the game-like structure of the Mapping method, the workshop will engage participants and help them develop essential tools to decide on ethical solutions that are technically feasible. Collaborating with each other, participants test the strength of their ideas and progress gradually towards creating solutions to this real-life problem as well as analyzing how their solution would hold up in other relevant cases such as voice assistant responses and other search result categories. The Mapping helps bring abstract ethical arguments to the ground—in a very literal sense, since the Mapping takes the form of a physical ground game.

Limited spaces are available for this session. Sit down with experts in AI who are using deep learning and AI techniques to solve challenges in the healthcare sector. Offer them your thoughts on the problems they're facing in their work, get feedback on your challenges, and ask those burning questions in this in-depth networking session.

Confirmed experts include:

• Danielle Belgrave, Machine Learning Researcher, Microsoft Research

• Sarah Culkin, Strategic Data Lead, NHS England

• Daniel Leightley, Post-Doctoral Research Associate, King's Centre for Military Health Research

Questions and challenges our experts are keen to explore with you include: - How do we incorporate expert knowledge into Deep Learning? - Who is responsible when the ‘machine’ makes an incorrect decision that costs lives? - How do we build and develop trust in AI technology, especially within the healthcare sector? - Machine learning for Healthcare in Data Sparse Contexts - What advanced technologies do you think will emerge for the Health and Care system over the next decade?

Graphs are a natural way to model many real-world complex objects. In this talk, after a brief review of recent advances in using deep learning for graphs, we will present our approach to create inference models based on novel attention mechanisms for graph convolutional neural networks making them robust to noise with added interpretability. We will show their applications to very large semi-automatically generated biological networks. In addition, we will discuss our recent approaches to model and generate graphs with optimal properties using RL as well as an architecture for conditional generative graph models which are applied to create novel chemical compounds.

How to Design AI with Human Centricity

Whether we like it or not, we are inevitably moving into a world of more autonomous decisions by AI. In fact, done right it could lead to a really exciting future.

Done right is the key.

How do we ensure that we build AI that are not marginalising groups within society?

How do we prevent negative unintended consequences such as creating larger social disparity?

This workshop will get execs and data scientists thinking about how to ensure their AI is designed with human centricity in mind. Opened to everyone, both technical and non technical.

Parker Moss, Entrepreneur-in-Residence at F-Prime Capital will be chairing this exciting session exploring investing in startups. We will start off the session with a Founder's Story interview with Eduardo Jorgensen, Founder of MedicSen; a startup dedicated to healthcare who have seen significant growth over the past year since launching GlycSen for intelligent treatment of insulin dependent diabetes. Following this, leading investors interested in deep learning across all industries will be sharing industry insights and tips on a panel discussion.

The investors are:

Dmitry Kaminskiy, Managing Partner, Deep Knowledge Ventures

John Spindler, General Partner, AI Seed

Frederic Lardieg, Partner, Octopus Ventures

Attendees will be invited to meet the investors for the remainder of the session to ask those burning questions.