loader from loading.io

Causality 101 with Robert Osazuwa Ness - #342

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

Release Date: 01/27/2020

Simulating the Future of Traffic with RL w/ Cathy Wu - #362 show art Simulating the Future of Traffic with RL w/ Cathy Wu - #362

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

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.

info_outline
Consciousness and COVID-19 with Yoshua Bengio - #361 show art Consciousness and COVID-19 with Yoshua Bengio - #361

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

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.

info_outline
Geometry-Aware Neural Rendering with Josh Tobin - #360 show art Geometry-Aware Neural Rendering with Josh Tobin - #360

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

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.

info_outline
The Third Wave of Robotic Learning with Ken Goldberg - #359 show art The Third Wave of Robotic Learning with Ken Goldberg - #359

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

Today we’re joined by Ken Goldberg, professor of engineering at UC Berkeley, focused on robotic learning.

info_outline
Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358 show art Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358

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

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.

info_outline
Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357 show art Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357

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

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.

info_outline
SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356 show art SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356

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

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.

info_outline
Advancements in Machine Learning with Sergey Levine - #355 show art Advancements in Machine Learning with Sergey Levine - #355

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

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!

info_outline
Secrets of a Kaggle Grandmaster with David Odaibo - #354 show art Secrets of a Kaggle Grandmaster with David Odaibo - #354

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

Imagine spending years learning ML from the ground up, from its theoretical foundations, but still feeling like you didn’t really know how to apply it. That’s where David Odaibo found himself in 2015, after the second year of his PhD. David’s solution was Kaggle, a popular platform for data science competitions.

info_outline
NLP for Mapping Physics Research with Matteo Chinazzi - #353 show art NLP for Mapping Physics Research with Matteo Chinazzi - #353

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

Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Research Space: a Machine Learning Approach.

info_outline
 
More Episodes

Today we’re accompanied by Robert Osazuwa Ness, Machine Learning Research Engineer at ML Startup Gamalon and Instructor at Northeastern University. Robert, who we had the pleasure of meeting at the Black in AI Workshop at NeurIPS last month, joins us to discuss:

  • Causality, what it means, and how that meaning changes across domains and users.
  • Benefits of causal models vs non-causal models.
  • Real-world applications of causality. 
  • Various tools and packages for causality, 
  • Areas where it is effectively being deployed, like ML in production.
  • Our upcoming study group based around his new course sequence, “Causal Modeling in Machine Learning,” for which you can find details at twimlai.com/community.

The complete show notes for this episode can be found at twimlai.com/talk/342.