Rebooting AI: What's Missing, What's Next with Gary Marcus - TWIML Talk #298
Today we're joined by Gary Marcus, CEO and Founder at Robust.AI, well-known scientist, bestselling author, professor and entrepreneur. Hear Gary discuss his latest book, ‘Rebooting AI: Building Artificial Intelligence We Can Trust’, an extensive look into the current gaps, pitfalls and areas for improvement in the field of machine learning and AI. In this episode, Gary provides insight into what we should be talking and thinking about to make even greater (and safer) strides in AI.
10 Sep 201947min
DeepQB: Deep Learning to Quantify Quarterback Decision-Making with Brian Burke - TWIML Talk #297
Today we're joined by Brian Burke, Analytics Specialist with the Stats & Information Group at ESPN. A former Navy pilot and lifelong football fan, Brian saw the correlation between fighter pilots and quarterbacks in the quick decisions both roles make on a regular basis. In this episode, we discuss his paper: “DeepQB: Deep Learning with Player Tracking to Quantify Quarterback Decision-Making & Performance”, what it means for football, and his excitement for machine learning in sports.
5 Sep 201950min
Measuring Performance Under Pressure Using ML with Lotte Bransen - TWIML Talk #296
Today we're joined by Lotte Bransen, a Scientific Researcher at SciSports. With a background in mathematics, econometrics, and soccer, Lotte has honed her research on analytics of the game and its players, using trained models to understand the impact of mental pressure on a player’s performance. In this episode, Lotte discusses her paper, ‘Choke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure’ and the implications of her research in the world of sports.
3 Sep 201934min
Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295
Today we're joined by Lukas Biewald, CEO and Co-Founder of Weights & Biases. Lukas founded the company after seeing a need for reproducibility in deep learning experiments. In this episode, we discuss his experiment tracking tool, how it works, the components that make it unique, and the collaborative culture that Lukas promotes. Listen in to how he got his start in deep learning and experiment tracking, the current Weights & Biases success strategy, and what his team is working on today.
29 Aug 201942min
Re-Architecting Data Science at iRobot with Angela Bassa - TWIML Talk #294
Today we’re joined by Angela Bassa, Director of Data Science at iRobot. In our conversation, Angela and I discuss: • iRobot's re-architecture, and a look at the evolution of iRobot. • Where iRobot gets its data from and how they taxonomize data science. • The platforms and processes that have been put into place to support delivering models in production. •The role of DevOps in bringing these various platforms together, and much more!
26 Aug 201948min
Disentangled Representations & Google Research Football with Olivier Bachem - TWIML Talk #293
Today we’re joined by Olivier Bachem, a research scientist at Google AI on the Brain team. Olivier joins us to discuss his work on Google’s research football project, their foray into building a novel reinforcement learning environment. Olivier and Sam discuss what makes this environment different than other available RL environments, such as OpenAI Gym and PyGame, what other techniques they explored while using this environment, and what’s on the horizon for their team and Football RLE.
22 Aug 201942min
Neural Network Quantization and Compression with Tijmen Blankevoort - TWIML Talk #292
Today we’re joined by Tijmen Blankevoort, a staff engineer at Qualcomm, who leads their compression and quantization research teams. In our conversation with Tijmen we discuss: • The ins and outs of compression and quantization of ML models, specifically NNs, • How much models can actually be compressed, and the best way to achieve compression, • We also look at a few recent papers including “Lottery Hypothesis."
19 Aug 201950min
Identifying New Materials with NLP with Anubhav Jain - TWIML Talk #291
Today we are joined by Anubhav Jain, Staff Scientist & Chemist at Lawrence Berkeley National Lab. We discuss his latest paper, ‘Unsupervised word embeddings capture latent knowledge from materials science literature’. Anubhav explains the design of a system that takes the literature and uses natural language processing to conceptualize complex material science concepts. He also discusses scientific literature mining and how the method can recommend materials for functional applications in the future.
15 Aug 201939min
