The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by return guest Karim Beguir, Co-Founder and CEO of InstaDeep. In our conversation, we chat with Karim about InstaDeep’s new offering, DeepPCB, an end-to-end platform for automated circuit board design. We discuss challenges and problems with some of the original iterations of auto-routers, how Karim defines circuit board “complexity,” the differences between reinforcement learning being used for games and in this use case, and their spotlight paper from NeurIPS.

Today we’re joined by David Duvenaud, Assistant Professor at the University of Toronto, to discuss his research on Neural Ordinary Differential Equations, a type of continuous-depth neural network. In our conversation, we talk through a few of David’s papers on the subject. We discuss the problem that David is trying to solve with this research, the potential that ODEs have to replace “the backbone” of the neural networks that are used to train today, and David’s approach to engineering.

Today we’re joined by Sharad Goel, Assistant Professor at Stanford. Sharad, who also has appointments in the computer science, sociology, and law departments, has spent recent years focused on applying ML to understanding and improving public policy. In our conversation, we discuss Sharad’s extensive work on discriminatory policing, and The Stanford Open Policing Project. We also dig into Sharad’s paper “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.”

Today we’re joined by one of, if not the most cited computer scientist in the world, Yoshua Bengio, Professor at the University of Montreal and the Founder and Scientific Director of MILA. We caught up with Yoshua to explore his work on consciousness, including how Yoshua defines consciousness, his paper “The Consciousness Prior,” as well as his current endeavor in building a COVID-19 tracing application, and the use of ML to propose experimental candidate drugs.

Today we’re joined by Josh Tobin, Co-Organizer of the machine learning training program Full Stack Deep Learning. We had the pleasure of sitting down with Josh prior to his presentation of his paper Geometry-Aware Neural Rendering at NeurIPS. Josh's goal is to develop implicit scene understanding, building upon Deepmind's Neural scene representation and rendering work. We discuss challenges, the various datasets used to train his model, and the similarities between VAE training and his process, and mor

Today we’re joined by Ken Goldberg, professor of engineering at UC Berkeley, focused on robotic learning. In our conversation with Ken, we chat about some of the challenges that arise when working on robotic grasping, including uncertainty in perception, control, and physics. We also discuss his view on the role of physics in robotic learning, and his thoughts on potential robot use cases, from the use of robots in assisting in telemedicine, agriculture, and even robotic Covid-19 testing.

Today we’re joined by Stefan Lee, an assistant professor at Oregon State University. In our conversation, we focus on his paper ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. We discuss the development and training process for this model, the adaptation of the training process to incorporate additional visual information to BERT models, where this research leads from the perspective of integration between visual and language tasks.

Today we’re joined by Jürgen Schmidhuber, Co-Founder and Chief Scientist of NNAISENSE, the Scientific Director at IDSIA, as well as a Professor of AI at USI and SUPSI in Switzerland. Jürgen’s lab is well known for creating the Long Short-Term Memory (LSTM) network, and in this conversation, we discuss some of the recent research coming out of his lab, namely Upside-Down Reinforcement Learning.

Beidi Chen is part of the team that developed a cheaper, algorithmic, CPU alternative to state-of-the-art GPU machines. They presented their findings at NeurIPS 2019 and have since gained a lot of attention for their paper, SLIDE: In Defense of Smart Algorithms Over Hardware Acceleration for Large-Scale Deep Learning Systems. Beidi shares how the team took a new look at deep learning with the case of extreme classification by turning it into a search problem and using locality-sensitive hashing.