57. Dylan Hadfield-Menell - Humans in the loop
Towards Data Science
11/11/20
•64m
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Human beings are collaborating with artificial intelligences on an increasing number of high-stakes tasks. I’m not just talking about robot-assisted surgery or self-driving cars here — every day, social media apps recommend content to us that quite literally shapes our worldviews and our cultures. And very few of us even have a basic idea of how these all-important recommendations are generated.
As time goes on, we’re likely going to become increasingly dependent on our machines, outsourcing more and more of our thinking to them. If we aren’t thoughtful about the way we do this, we risk creating a world that doesn’t reflect our current values or objectives. That’s why the domain of human/AI collaboration and interaction is so important — and it’s the reason I wanted to speak to Berkeley AI researcher Dylan Hadfield-Menell for this episode of the Towards Data Science podcast. Dylan’s work is focused on designing algorithms that could allow humans and robots to collaborate more constructively, and he’s one of a small but growing cohort of AI researchers focused on the area of AI ethics and AI alignment.
Previous Episode
56. Annette Zimmermann - The ethics of AI
November 4, 2020
•56m
As AI systems have become more powerful, they’ve been deployed to tackle an increasing number of problems.
Take computer vision. Less than a decade ago, one of the most advanced applications of computer vision algorithms was to classify hand-written digits on mail. And yet today, computer vision is being applied to everything from self-driving cars to facial recognition and cancer diagnostics.
Practically useful AI systems have now firmly moved from “what if?” territory to “what now?” territory. And as more and more of our lives are run by algorithms, an increasing number of researchers from domains outside computer science and engineering are starting to take notice. Most notably among these are philosophers, many of whom are concerned about the ethical implications of outsourcing our decision-making to machines whose reasoning we often can’t understand or even interpret.
One of the most important voices in the world of AI ethics has been that of Dr Annette Zimmermann, a Technology & Human Rights Fellow at the Carr Center for Human Rights Policy at Harvard University, and a Lecturer in Philosophy at the University of York. Annette is has focused a lot of her work on exploring the overlap between algorithms, society and governance, and I had the chance to sit down with her to discuss her views on bias in machine learning, algorithmic fairness, and the big picture of AI ethics.
Next 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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