10 talks on building AI-first products and new AI research

These talks were recorded live at RAAIS 2020.

and

Oct 26, 2020

Introducing RAAIS

We founded The Research and Applied AI Summit (RAAIS) in 2015 to bring together entrepreneurs and researchers who accelerate the science and applications of AI technology for the common good. The key goal of the Summit is to catalyze best practice sharing, new relationships across disciplines, and help the most ambitious AI practitioners showcase their work to their peers.

The frontiers of AI compute: Cerebras Systems

Andy Hock is Head of Product at Cerebras Systems, a computer systems company focused on AI. He is responsible for the requirements and product strategy for Cerebras’ AI hardware, software, ML research, marketing.

Decoding the biology of human nutrition with PREDICT: ZOE

Sarah Berry is a Senior Lecturer in the Department of Nutritional Sciences at King’s College London and Head of Nutrition Science at ZOE, the London-based AI-first nutritional science startup behind the PREDICT study, which is the world’s largest ongoing nutritional research project of its kind.

A deep learning approach to anbitiotics discovery: Broad Institute

Jonathan Stokes is a Banting Fellow under the supervision of James Collins at MIT. His research applies a combination of chemical biology and machine learning to develop novel antibacterial therapies with expanded capabilities over conventional antibiotics.

Transforming healthcare with AI: Moorfields Eye Hospital

Pearse Keane is a consultant ophthalmologist at Moorfields Eye Hospital and an associate professor at UCL Institute of Ophthalmology.

Compute-designed organisms: University of Vermont

Josh Bongard is the Veinott Professor of Computer Science at the University of Vermont and the director of the Morphology, Evolution & Cognition Laboratory.

Insights from deep representations for ML systems: Google Brain

Maithra Raghu is a Research Scientist at Google Brain. Her research centers on developing quantitative techniques to gain insights into deep learning representations and using these insights to inform AI system design and collaboration with human experts in medicine.

Robustness certification for deep learning: DeepMind

Dj Dvijotham is a senior research scientist at DeepMind who works on these problems. His research focus is on building robust and verifiable AI systems that can be trusted to behave reasonably even under adversarial circumstances.

Why AI fairness must be automated: Oxford University

Sandra Wachter is an Associate Professor and Senior Research Fellow in Law and Ethics of AI, Big Data, and robotics as well as Internet Regulation at the Oxford Internet Institute at the University of Oxford and a Fellow at the Alan Turing Institute in London. Chris Russell is a Group Leader in Safe and Ethical AI at the Alan Turing Institute, and a Reader in Computer Vision and Machine Learning at the University of Surrey. Brent Mittelstadt is a Senior Research Fellow and British Academy Postdoctoral Fellow in data ethics at the Oxford Internet Institute, a Turing Fellow at the Alan Turing Institute, and a member of the UK National Statistician’s Data Ethics Advisory Committee.

Autonomous driving: Lyft Level 5

Sacha Arnoud is the Senior Director of Engineering at Lyft Level 5, the division of Lyft that is responsible for developing consumer-facing self-driving vehicles for the Lyft ride-hailing service.

Homomorphic encryption for deep learning: Zama

Pascal Paillier is a homomorphic encryption expert and the CTO of Zama, a Paris-based startup that builds open-source software tools enabling developers to easily deploy secure deep learning applications powered with homomorphic encryption.