Simulating the Future of Traffic with RL w/ Cathy Wu - #362
Today we’re joined by Cathy Wu, Assistant Professor at MIT. We had the pleasure of catching up with Cathy to discuss her work applying RL to mixed autonomy traffic, specifically, understanding the potential impact autonomous vehicles would have on various mixed-autonomy scenarios. To better understand this, Cathy built multiple RL simulations, including a track, intersection, and merge scenarios. We talk through how each scenario is set up, how human drivers are modeled, the results, and much more.
2 Apr 202035min
Consciousness and COVID-19 with Yoshua Bengio - #361
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.
30 Mars 202049min
Geometry-Aware Neural Rendering with Josh Tobin - #360
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
26 Mars 202026min
The Third Wave of Robotic Learning with Ken Goldberg - #359
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.
23 Mars 20201h 1min
Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358
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.
18 Mars 202027min
Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357
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.
16 Mars 202034min
SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356
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.
12 Mars 202031min
Advancements in Machine Learning with Sergey Levine - #355
Today we're joined by Sergey Levine, an Assistant Professor at UC Berkeley. We last heard from Sergey back in 2017, where we explored Deep Robotic Learning. Sergey and his lab’s recent efforts have been focused on contributing to a future where machines can be “out there in the real world, learning continuously through their own experience.” We caught up with Sergey at NeurIPS 2019, where Sergey and his team presented 12 different papers -- which means a lot of ground to cover!
9 Mars 202043min
