60. Rob Miles - Why should I care about AI safety?
Towards Data Science
12/02/20
•45m
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Progress in AI capabilities has consistently surprised just about everyone, including the very developers and engineers who build today’s most advanced AI systems. AI can now match or exceed human performance in everything from speech recognition to driving, and one question that’s increasingly on people’s minds is: when will AI systems be better than humans at AI research itself?
The short answer, of course, is that no one knows for sure — but some have taken some educated guesses, including Nick Bostrom and Stuart Russell. One common hypothesis is that once an AI systems are better than a human at improving their own performance, we can expect at least some of them to do so. In the process, these self-improving systems would become an even more powerful system that they were previously—and therefore, even more capable of further self-improvement. With each additional self-improvement step, improvements in a system’s performance would compound. Where this all ultimately leads, no one really has a clue, but it’s safe to say that if there’s a good chance that we’re going to be creating systems that are capable of this kind of stunt, we ought to think hard about how we should be building them.
This concern among many others has led to the development of the rich field of AI safety, and my guest for this episode, Robert Miles, has been involved in popularizing AI safety research for more than half a decade through two very successful YouTube channels, Robert Miles and Computerphile. He joined me on the podcast to discuss how he’s thinking about AI safety, what AI means for the course of human evolution, and what our biggest challenges will be in taming advanced AI.
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59. Matthew Stewart - Tiny ML and the future of on-device AI
November 25, 2020
•43m
When it comes to machine learning, we’re often led to believe that bigger is better. It’s now pretty clear that all else being equal, more data, more compute, and larger models add up to give more performance and more generalization power. And cutting edge language models have been growing at an alarming rate — by up to 10X each year.
But size isn’t everything. While larger models are certainly more capable, they can’t be used in all contexts: take, for example, the case of a cell phone or a small drone, where on-device memory and processing power just isn’t enough to accommodate giant neural networks or huge amounts of data. The art of doing machine learning on small devices with significant power and memory constraints is pretty new, and it’s now known as “tiny ML”. Tiny ML unlocks an awful lot of exciting applications, but also raises a number of safety and ethical questions.
And that’s why I wanted to sit down with Matthew Stewart, a Harvard PhD researcher focused on applying tiny ML to environmental monitoring. Matthew has worked with many of the world’s top tiny ML researchers, and our conversation focused on the possibilities and potential risks associated with this promising new field.
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61. Ben Goertzel - The unorthodox path to AGI
December 9, 2020
•81m
No one knows for sure what it’s going to take to make artificial general intelligence work. But that doesn’t mean that there aren’t prominent research teams placing big bets on different theories: DeepMind seems to be hoping that a brain emulation strategy will pay off, whereas OpenAI is focused on achieving AGI by scaling up existing deep learning and reinforcement learning systems with more data, more compute.
Ben Goertzel —a pioneering AGI researcher, and the guy who literally coined the term “AGI” — doesn’t think either of these approaches is quite right. His alternative approach is the strategy currently being used by OpenCog, an open-source AGI project he first released in 2008. Ben is also a proponent of decentralized AI development, due to his concerns about centralization of power through AI, as the technology improves. For that reason, he’s currently working on building a decentralized network of AIs through SingularityNET, a blockchain-powered AI marketplace that he founded in 2017.
Ben has some interesting and contrarian views on AGI, AI safety, and consciousness, and he was kind enough to explore them with me on this episode of the podcast.
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