66. Owain Evans - Predicting the future of AI
Towards Data Science
01/13/21
•47m
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Most researchers agree we’ll eventually reach a point where our AI systems begin to exceed human performance at virtually every economically valuable task, including the ability to generalize from what they’ve learned to take on new tasks that they haven’t seen before. These artificial general intelligences (AGIs) would in all likelihood have transformative effects on our economies, our societies and even our species.
No one knows what these effects will be, or when AGI systems will be developed that can bring them about. But that doesn’t mean these things aren’t worth predicting or estimating. The more we know about the amount of time we have to develop robust solutions to important AI ethics, safety and policy problems, the more clearly we can think about what problems should be receiving our time and attention today.
That’s the thesis that motivates a lot of work on AI forecasting: the attempt to predict key milestones in AI development, on the path to AGI and super-human artificial intelligence. It’s still early days for this space, but it’s received attention from an increasing number of the AI safety and AI capabilities researchers. One of those researchers is Owain Evans, whose work at Oxford University’s Future of Humanity Institute is focused on techniques for learning about human beliefs, preferences and values from observing human behavior or interacting with humans. Owain joined me for this episode of the podcast to talk about AI forecasting, the problem of inferring human values, and the ecosystem of research organizations that support this type of research.
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65. Helen Toner - The strategic and security implications of AI
January 6, 2021
•43m
With every new technology comes the potential for abuse. And while AI is clearly starting to deliver an awful lot of value, it’s also creating new systemic vulnerabilities that governments now have to worry about and address. Self-driving cars can be hacked. Speech synthesis can make traditional ways of verifying someone’s identity less reliable. AI can be used to build weapons systems that are less predictable.
As AI technology continues to develop and become more powerful, we’ll have to worry more about safety and security. But competitive pressures risk encouraging companies and countries to focus on capabilities research rather than responsible AI development. Solving this problem will be a big challenge, and it’s probably going to require new national AI policies, and international norms and standards that don’t currently exist.
Helen Toner is Director of Strategy at the Center for Security and Emerging Technology (CSET), a US policy think tank that connects policymakers to experts on the security implications of new technologies like AI. Her work spans national security and technology policy, and international AI competition, and she’s become an expert on AI in China, in particular. Helen joined me for a special AI policy-themed episode of the podcast.
Next Episode
67. Joaquin Quiñonero-Candela - Responsible AI at Facebook
January 20, 2021
•50m
Facebook routinely deploys recommendation systems and predictive models that affect the lives of billions of people everyday. That kind of reach comes with great responsibility — among other things, the responsibility to develop AI tools that ethical, fair and well characterized.
This isn’t an easy task. Human beings have spent thousands of years arguing about what “fairness” and “ethics” mean, and haven’t come close to a consensus. Which is precisely why the responsible AI community has to involve as many disparate perspectives as possible in determining what policies to explore and recommend — a practice that Facebook’s Responsible AI team has applied itself.
For this episode of the podcast, I’m joined by Joaquin Quiñonero-Candela, the Distinguished Tech Lead for Responsible AI at Facebook. Joaquin has been at the forefront of the AI ethics and fairness movements for years, and has overseen the formation of Facebook’s responsible AI team. As a result, he’s one of relatively few people with hands-on experience making critical AI ethics decisions at scale, and seeing their effects.
Our conversation covered a lot of ground, from philosophical questions about the definition of fairness, to practical challenges that arise when implementing certain ethical AI frameworks.
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