43. Ian Scott - Data science at Deloitte
Towards Data Science
07/22/20
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Data science can look very different from one company to the next, and it’s generally difficult to get a consistent opinion on the question of what a data scientist really is.
That’s why it’s so important to speak with data scientists who apply their craft at different organizations — from startups to enterprises. Getting exposure to the full spectrum of roles and responsibilities that data scientists are called on to execute is the only way to distill data science down to its essence.
That’s why I wanted to chat with Ian Scott, Chief Science Officer at Deloitte Omnia, Deloitte’s AI practice. Ian was doing data science as far back as the late 1980s, when he was applying statistical modeling to data from experimental high energy physics as par of his PhD work at Harvard. Since then, he’s occupied strategic roles at a number of companies, most recently including Deloitte, where he leads significant machine learning and data science projects.
Previous Episode
Machine learning in grad school and machine learning in industry are very different beasts. In industry, deployment and data collection become key, and the only thing that matters is whether you can deliver a product that real customers want, fast enough to meet internal deadlines. In grad school, there’s a different kind of pressure, focused on algorithm development and novelty. It’s often difficult to know which path you might be best suited for, but that’s why it can be so useful to speak with people who’ve done both — and bonus points if their academic research experience comes from one of the top universities in the world.
For today’s episode of the Towards Data Science podcast, I sat down with Will Grathwohl, a PhD student at the University of Toronto, student researcher at Google AI, and alum of MIT and OpenAI. Will has seen cutting edge machine learning research in industry and academic settings, and has some great insights to share about the differences between the two environments. He’s also recently published an article on the fascinating topic of energy models in which he and his co-authors propose a unique way of thinking about generative models that achieves state-of-the-art performance in computer vision tasks.
Next Episode
Reinforcement learning has gotten a lot of attention recently, thanks in large part to systems like AlphaGo and AlphaZero, which have highlighted its immense potential in dramatic ways. And while the RL systems we’ve developed have accomplished some impressive feats, they’ve done so in a fairly naive way. Specifically, they haven’t tended to confront multi-agent problems, which require collaboration and competition. But even when multi-agent problems have been tackled, they’ve been addressed using agents that just assume other agents are an uncontrollable part of the environment, rather than entities with rich internal structures that can be reasoned and communicated with.
That’s all finally changing, with new research into the field of multi-agent RL, led in part by OpenAI, Oxford and Google alum, and current FAIR research scientist Jakob Foerster. Jakob’s research is aimed specifically at understanding how reinforcement learning agents can learn to collaborate better and navigate complex environments that include other agents, whose behavior they try to model. In essence, Jakob is working on giving RL agents a theory of mind.
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