Dietmar Dahmen
Futurist & Innovation Expert | Former CCO at BBDO & ECD at Ogilvy | Founder, BRAINKICKS GmbH | Change & Digital Transformation
Senior Research Scientist at DeepMind and Founder of OpenMined
Speaker Andrew Trask is a Senior Research Scientist at DeepMind and the Founder of OpenMined, a global community advancing privacy-preserving AI. He is also the author of Grokking Deep Learning, an influential AI educator, and a member of the United Nations Privacy Task Force. With a career spanning research, industry, and policy, he champions the democratization of AI to drive innovation and accountability worldwide.
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Speaker Andrew Trask is a leading expert at the intersection of privacy and artificial intelligence. He works as a Senior Research Scientist at DeepMind, where he explores innovative ways to build AI systems that prioritize privacy and security. At the same time, he leads as Founder of OpenMined, a thriving open-source community of more than 12,000 researchers, engineers, and practitioners committed to advancing privacy-preserving AI and driving its mainstream adoption.
Throughout his career, Andrew has worked closely with hedge funds, investment banks, healthcare networks, and government agencies. In each of these roles, he has delivered AI solutions that not only solve complex problems but also create meaningful impact across industries. By combining technical depth with real-world applications, he consistently helps organizations unlock the true potential of AI.
Moreover, Andrew is deeply passionate about education. He strives to make complex ideas both clear and approachable. As the author of the bestselling book Grokking Deep Learning, an instructor in Udacity’s Deep Learning Nanodegree program, and the creator of the widely read blog i am trask, he has inspired thousands of learners worldwide. In addition, through his teaching, he empowers students and professionals to confidently engage with cutting-edge AI concepts.
Speaker Andrew Trask also plays an influential role in shaping technology policy. As a member of the United Nations Privacy Task Force, he raises awareness about privacy-preserving analytics while encouraging governments to lower barriers to adoption in the public sector. Looking ahead, he continues to champion the democratization of AI. Drawing parallels between the launch of ARPANET in the 1960s and today’s NAIRR initiative, he emphasizes that open access to advanced AI resources will fuel innovation and ensure a more inclusive future.
In this keynote, Andrew explores the most important advances in secure, privacy-preserving, and multi-owner governed artificial intelligence. He begins by offering a clear, up-to-date perspective on the state of AI safety, user privacy, and governance. From there, he introduces the essential tools shaping technical AI safety—homomorphic encryption, secure multiparty computation, federated learning, and differential privacy. To bring these concepts to life, he concludes with a compelling demonstration from the OpenMined project, showing how a deep neural network can be trained entirely in an encrypted and secure environment.
Andrew examines how privacy-preserving techniques are reshaping the future of artificial intelligence across industries bound by strict data regulations. He highlights three breakthrough approaches—federated learning, differential privacy, and encrypted computation—that enable powerful collaboration without exposing sensitive data. By weaving together concepts such as differential privacy and secure multi-party computation, Andrew shows how healthcare and other fields can train AI models on encrypted data from multiple sources, while still protecting individual privacy.
In this session, Andrew addresses one of the most pressing questions in the digital age: what does meaningful privacy look like in practice? He demonstrates how cutting-edge technologies, including privacy-preserving machine learning, can directly tackle global privacy challenges. Through practical insights and real-world examples, he illustrates how these innovations pave the way for more secure, transparent, and trustworthy AI systems.
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