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

Today we’re joined by Subutai Ahmad, VP of research at Numenta. While we’ve had numerous conversations about the biological inspirations of deep learning models with folks working at the intersection of deep learning and neuroscience, we dig into uncharted territory with Subutai. We set the stage by digging into some of fundamental ideas behind Numenta’s research and the present landscape of neuroscience, before exploring our first big topic of the podcast: the cortical column. Cortical columns are a group of neurons in the cortex of the brain which have nearly identical receptive fields; we discuss the behavior of these columns, why they’re a structure worth mimicing computationally, how far along we are in understanding the cortical column, and how these columns relate to neurons.

We also discuss what it means for a model to have inherent 3d understanding and for computational models to be inherently sensory motor, and where we are with these lines of research. Finally, we dig into our other big idea, sparsity. We explore the fundamental ideals of sparsity and the differences between sparse and dense networks, and applying sparsity and optimization to drive greater efficiency in current deep learning networks, including transformers and other large language models. 

The complete show notes for this episode can be found at twimlai.com/go/562

Today we’re joined by Jennifer Glore, VP of customer engineering at SambaNova Systems. In our conversation with Jennifer, we discuss how, and why, Sambanova, who is primarily focused on building hardware to support machine learning applications, has built a GPT language model for the financial services industry. Jennifer shares her thoughts on the progress of industries like banking and finance, as well as other traditional organizations, in their attempts at using transformers and other models, and where they’ve begun to see success, as well as some of the hidden challenges that orgs run into that impede their progress. Finally, we explore their experience replicating the GPT-3 paper from a R&D perspective, how they’re addressing issues of predictability, controllability, governance, etc, and much more.

The complete show notes for this episode can be found at twimlai.com/go/561

Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great resources that Kamyar shared on the show notes page at twimlai.com/go/560!

Today we’re joined by Rishabh Agarwal, a research scientist at Google Brain in Montreal. In our conversation with Rishabh, we discuss his recent paper Deep Reinforcement Learning at the Edge of the Statistical Precipice, which won an outstanding paper award at the most recent NeurIPS conference. In this paper, Rishabh and his coauthors call for a change in how deep RL performance is reported on benchmarks when using only a few runs, acknowledging that typically, DeepRL algorithms are evaluated by the performance on a large suite of tasks. Using the Atari 100k benchmark, they found substantial disparities in the conclusions from point estimates alone versus statistical analysis. We explore the reception of this paper from the research community, some of the more surprising results, what incentives researchers have to implement these types of changes in self-reporting when publishing, and much more.

The complete show notes for this episode can be found at twimlai.com/go/559

Today we’re joined by Rafael Gomez-Bombarelli, an assistant professor in the department of material science and engineering at MIT. In our conversation with Rafa, we explore his goal of ​​fusing machine learning and atomistic simulations for designing materials, a topic he spoke about at the recent SigOpt AI & HPC Summit. We discuss the two ways in which he thinks of material design, virtual screening and inverse design, as well as the unique challenges each technique presents. We also talk through the use of generative models for simulation, the type of training data necessary for these tasks, and if he’s building hand-coded simulations vs existing packages or tools. Finally, we explore the dynamic relationship between simulation and modeling and how the results of one drive the others efforts, and how hyperparameter optimization gets incorporated into the various projects.

The complete show notes for this episode can be found at twimlai.com/go/558

Today we’re joined by Patrick Heimbach, a professor at the University of Texas working at the intersection of ML and oceanography. In our conversation with Patrick, we explore some of the challenges of computational oceanography, the potential use cases for machine learning in this field, as well as how it can be used to support scientists in solving simulation problems, and the role of differential programming and how it is expressed in his work. 

The complete show notes for this episode can be found at twimlai.com/go/557

Today we continue our AI Rewind 2021 series joined by a friend of the show, assistant professor at Carnegie Mellon University, and AI Rewind veteran, Zack Lipton! In our conversation with Zack, we touch on recurring themes like “NLP Eating AI” and the recent slowdown in innovation in the field, the redistribution of resources across research problems, and where the opportunities for real breakthroughs lie. We also discuss problems facing the current peer-review system, notable research from last year like the introduction of the WILDS library, and the evolution of problems (and potential solutions) in fairness, bias, and equity. Of course, we explore some of the use cases and application areas that made notable progress in 2021, what Zack is looking forward to in 2022 and beyond, and much more!

The complete show notes for this episode can be found at twimlai.com/go/556

Today we’re joined by Jonathan Le Roux, a senior principal research scientist at Mitsubishi Electric Research Laboratories (MERL). At MERL, Jonathan and his team are focused on using machine learning to solve the “cocktail party problem”, focusing on not only the separation of speech from noise, but also the separation of speech from speech. In our conversation with Jonathan, we focus on his paper The Cocktail Fork Problem: Three-Stem Audio Separation For Real-World Soundtracks, which looks to separate and enhance a complex acoustic scene into three distinct categories, speech, music, and sound effects. We explore the challenges of working with such noisy data, the model architecture used to solve this problem, how ML/DL fits into solving the larger cocktail party problem, future directions for this line of research, and much more!

The complete show notes for this episode can be found at twimlai.com/go/555

Today we’re joined by Karianne Bergen, an assistant professor at Brown University. In our conversation with Karianne, we explore her work at the intersection of earthquake seismology and machine learning, where she’s working on interpretable data classification for seismology. We discuss some of the challenges that present themselves when trying to solve this problem, and the state of applying machine learning to seismological events and earth sciences. Karianne also shares her thoughts on the different relationships that computer scientists and natural scientists have with machine learning, and how to bridge that gap to create tools that work broadly for all scientists.

The complete show notes for this episode can be found at twimlai.com/go/554

Today we’re joined by Arun Kumarm, an associate professor at UC San Diego. We had the pleasure of catching up with Arun prior to the Workshop on Databases and AI at NeurIPS 2021, where he delivered the talk “The New DBfication of ML/AI.” In our conversation, we explore this “database-ification” of machine learning, a concept analogous to the transformation of relational SQL computation. We discuss the relationship between the ML and database fields and how the merging of the two could have positive outcomes for the end-to-end ML workflow, and a few tools that his team has developed, Cerebro, a tool for reproducible model selection, and SortingHat, a tool for automating data prep, and how tools like these and others affect Arun’s outlook on the future of machine learning platforms and MLOps.

The complete show notes for this episode can be found at twimlai.com/go/553