Nigel Nicholson
Emeritus Professor, London Business School | Pioneer of Evolutionary Psychology in Business | Bestselling Author, Unique You & Family Wars
Founder & Leader of OpenMined | Senior Research Scientist, Google DeepMind | Author of 'Grokking Deep Learning' | Pioneer of Privacy-Preserving AI
Andrew Trask is one of the world's leading voices on privacy-preserving AI. A Senior Research Scientist at Google DeepMind, he founded and leads OpenMined, a global community of more than 18,000 people advancing privacy-preserving AI, and created the widely used PySyft library. A bestselling author and gifted teacher, on stage Trask makes the most technical questions in AI clear, showing leaders how privacy and AI can advance together.
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AI speaker Andrew Trask is one of the world’s leading voices on privacy-preserving artificial intelligence. A Senior Research Scientist at Google DeepMind, where he studies the intersection of privacy and AI, he has spent his career working to make powerful AI both responsible and widely accessible. His work spans research, industry, and public policy, with a consistent goal: ensuring that the benefits of AI reach far beyond a handful of large organizations.
Trask is the founder and leader of OpenMined, a global open-source community of more than 18,000 researchers, engineers, and practitioners building the tools that make privacy-preserving AI possible. Through OpenMined he created PySyft, a widely used library for secure and private machine learning, and helped popularize techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation. He has applied these methods with hedge funds, investment banks, healthcare networks, and government agencies to solve problems that were once off-limits because the data was too sensitive to share.
Andrew is also a gifted educator with a talent for making hard ideas feel approachable. He is the author of the bestselling book Grokking Deep Learning, an instructor in Udacity’s Deep Learning Nanodegree program, and the writer behind the widely read blog “i am trask.” He is also pursuing a PhD at the University of Oxford, where his research focuses on privacy-preserving machine learning. Through his teaching and writing, he has helped thousands of people around the world learn to build neural networks from the ground up.
Trask also helps shape technology policy as a member of the United Nations Privacy Task Force, where he works to raise awareness of privacy-preserving analytics and lower the barriers to adopting them in the public sector. He is a passionate advocate for the democratization of AI, often comparing today’s push to widen access to AI supercomputing, through initiatives like the US National AI Research Resource, to the way ARPANET once opened up access to computing in the 1960s.
As a speaker, Andrew Trask makes some of the most technical and important questions in AI clear, engaging, and relevant to decision-makers. He helps audiences understand how privacy and AI can advance together rather than at each other’s expense, and what it will take to build systems people can trust. Leaders leave with a sharper sense of where AI is heading and how to adopt it responsibly, securely, and for the benefit of many rather than a few.
Andrew Trask explores the most important advances in secure, privacy-preserving, and multi-owner governed AI. He opens with a clear, current picture of where AI safety, user privacy, and governance stand today, then introduces the core building blocks of technical AI safety: homomorphic encryption, secure multi-party computation, federated learning, and differential privacy. He closes with a striking demonstration from OpenMined, showing how a deep neural network can be trained entirely on encrypted data, proving that powerful AI and strong privacy are not mutually exclusive.
Trask examines how privacy-preserving techniques are reshaping AI in industries bound by strict data rules. He focuses on three breakthrough approaches, federated learning, differential privacy, and encrypted computation, that allow powerful collaboration without exposing sensitive information. Using examples from healthcare and beyond, he shows how organizations can train AI models on encrypted data from many sources at once while still protecting every individual's privacy, unlocking insights that were previously impossible to reach.
What does real privacy look like in a world run on data? In this session, Trask tackles one of the defining questions of the digital age. He shows how cutting-edge tools, including privacy-preserving machine learning, can address global privacy challenges in practice rather than in theory. Through clear explanations and real-world examples, he illustrates how these innovations point the way toward AI systems that are more secure, more transparent, and more worthy of public trust.
Andrew Trask argues that the long-term benefits of AI will only be realized if access to data, tools, and computing power is shared far more broadly. Drawing a parallel between the way ARPANET opened up computing in the 1960s and today's efforts to widen access to AI resources, he makes the case that open infrastructure fuels innovation and a more inclusive future. He offers leaders and policymakers a clear view of why democratizing AI matters, and what it will take to get there responsibly.
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