Category: Data-Labor-Technology Videos

Prof. Ajay Agrawal, founder of the Creative Destruction Lab and co-founder of the AI/robotics company Kindred, explored the economics behind the creation of artificial intelligence.
April 18th, 2018
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Nick Bostrom is a Swedish philosopher at the University of Oxford known for his work on existential risk, the anthropic principle, human enhancement ethics, superintelligence risks, and the reversal test.
https://nickbostrom.com/fable/dragon.html
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Recorded May 1st, 2018 ICLR2018

Augmenting Clinical Intelligence with Machine Intelligence

“Healthcare is rapidly becoming a data-intensive discipline, driven by increasing digitization of health data, novel measurement technologies, and new policy-based incentives. Critical decisions about ​whom​ and h​ ow​ to treat can be made more precisely by layering an individual’s data over that from a population. In this talk, I will begin by introducing the types of health data currently being collected and the challenges associated with learning models from these data. Next, I will describe new techniques that leverage probabilistic methods and counterfactual reasoning for tackling the aforementioned challenges. Finally, I will introduce areas where ​statistical machine-learning techniques are leading to new classes of computational diagnostic and treatment planning tools—tools that tease out subtle information from “messy” observational datasets, and provide reliable inferences given detailed context about the individual patient.” – Suchi Saria
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Recorded May 3rd, 2018
“In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning. However reproducing results for state-of-the-art deep RL methods is seldom straightforward. High variance of some methods can make learning particularly difficult when environments or rewards are strongly stochastic. Furthermore, results can be brittle to even minor perturbations in the domain or experimental procedure. In this talk, I will discuss challenges that arise in experimental techniques and reporting procedures in deep RL, and will suggest methods and guidelines to make future results more reproducible, reusable and robust. I will also report on findings from the ICLR 2018 reproducibility challenge.” – Prof. Joelle Pineau
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