59. Matthew Stewart - Tiny ML and the future of on-device AI
Towards Data Science
11/25/20
•43m
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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.
Previous Episode
58. David Duvenaud - Using generative models for explainable AI
November 18, 2020
•36m
In the early 1900s, all of our predictions were the direct product of human brains. Scientists, analysts, climatologists, mathematicians, bankers, lawyers and politicians did their best to anticipate future events, and plan accordingly.
Take physics, for example, where every task we think of as part of the learning process, from data collection to cleaning to feature selection to modeling, all had to happen inside a physicist’s head. When Einstein introduced gravitational fields, what he was really doing was proposing a new feature to be added to our model of the universe. And the gravitational field equations that he put forward at the same time were an update to that very model.
Einstein didn’t come up with his new model (or “theory” as physicists call it) of gravity by running model.fit() in a jupyter notebook. In fact, he never outsourced any of the computations that were needed to develop it to machines.
Today, that’s somewhat unusual, and most of the predictions that the world runs on are generated in part by computers. But only in part — until we have fully general artificial intelligence, machine learning will always be a mix of two things: first, the constraints that human developers impose on their models, and second, the calculations that go into optimizing those models, which we outsource to machines.
The human touch is still a necessary and ubiquitous component of every machine learning pipeline, but it’s ultimately limiting: the more of the learning pipeline that can be outsourced to machines, the more we can take advantage of computers’ ability to learn faster and from far more data than human beings. But designing algorithms that are flexible enough to do that requires serious outside-of-the-box thinking — exactly the kind of thinking that University of Toronto professor and researcher David Duvenaud specializes in. I asked David to join me for the latest episode of the podcast to talk about his research on more flexible and robust machine learning strategies.
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60. Rob Miles - Why should I care about AI safety?
December 2, 2020
•45m
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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