The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
717 episodes - English - Latest episode: about 1 month ago - ★★★★★ - 323 ratingsMachine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
Homepage Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Episodes
Multimodal, Multi-Lingual NLP at Hugging Face with John Bohannon and Douwe Kiela - #589
August 29, 2022 15:59 - 53 minutesIn this extra special episode of the TWIML AI Podcast, a friend of the show John Bohannon leads a jam-packed conversation with Hugging Face’s recently appointed head of research Douwe Kiela. In our conversation with Douwe, we explore his role at the company, how his perception of Hugging Face has changed since joining, and what research entails at the company. We discuss the emergence of the transformer model and the emergence of BERT-ology, the recent shift to solving more multimodal problem...
Synthetic Data Generation for Robotics with Bill Vass - #588
August 22, 2022 18:02 - 36 minutesToday we’re joined by Bill Vass, a VP of engineering at Amazon Web Services. Bill spoke at the most recent AWS re:MARS conference, where he delivered an engineering Keynote focused on some recent updates to Amazon sagemaker, including its support for synthetic data generation. In our conversation, we discussed all things synthetic data, including the importance of data quality when creating synthetic data, and some of the use cases that this data is being created for, including warehouses and...
Multi-Device, Multi-Use-Case Optimization with Jeff Gehlhaar - #587
August 15, 2022 18:17 - 43 minutesToday we’re joined by Jeff Gehlhaar, vice president of technology at Qualcomm Technologies. In our annual conversation with Jeff, we dig into the relationship between Jeff’s team on the product side and the research team, many of whom we’ve had on the podcast over the last few years. We discuss the challenges of real-world neural network deployment and doing quantization on-device, as well as a look at the tools that power their AI Stack. We also explore a few interesting automotive use cases...
Causal Conceptions of Fairness and their Consequences with Sharad Goel - #586
August 08, 2022 16:57 - 37 minutesToday we close out our ICML 2022 coverage joined by Sharad Goel, a professor of public policy at Harvard University. In our conversation with Sharad, we discuss his Outstanding Paper award winner Causal Conceptions of Fairness and their Consequences, which seeks to understand what it means to apply causality to the idea of fairness in ML. We explore the two broad classes of intent that have been conceptualized under the subfield of causal fairness and how they differ, the distinct ways causal...
Brain-Inspired Hardware and Algorithm Co-Design with Melika Payvand - #585
August 01, 2022 18:01 - 44 minutesToday we continue our ICML coverage joined by Melika Payvand, a research scientist at the Institute of Neuroinformatics at the University of Zurich and ETH Zurich. Melika spoke at the Hardware Aware Efficient Training (HAET) Workshop, delivering a keynote on Brain-inspired hardware and algorithm co-design for low power online training on the edge. In our conversation with Melika, we explore her work at the intersection of ML and neuroinformatics, what makes the proposed architecture “brain-in...
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
July 25, 2022 17:26 - 39 minutesToday we’re joined by Arash Behboodi, a machine learning researcher at Qualcomm Technologies. In our conversation with Arash, we explore his paper Equivariant Priors for Compressed Sensing with Unknown Orientation, which proposes using equivariant generative models as a prior means to show that signals with unknown orientations can be recovered with iterative gradient descent on the latent space of these models and provide additional theoretical recovery guarantees. We discuss the differences...
Managing Data Labeling Ops for Success with Audrey Smith - #583
July 18, 2022 17:18 - 47 minutesToday we continue our Data-Centric AI Series joined by Audrey Smith, the COO at MLtwist, and a recent participant in our panel on DCAI. In our conversation, we do a deep dive into data labeling for ML, exploring the typical journey for an organization to get started with labeling, her experience when making decisions around in-house vs outsourced labeling, and what commitments need to be made to achieve high-quality labels. We discuss how organizations that have made significant investments i...
Engineering A ML-Powered Developer-First Search Engine with Richard Socher - #582
July 11, 2022 17:09 - 46 minutesToday we’re joined by Richard Socher, the CEO of You.com. In our conversation with Richard, we explore the inspiration and motivation behind the You.com search engine, and how it differs from the traditional google search engine experience. We discuss some of the various ways that machine learning is used across the platform including how they surface relevant search results and some of the recent additions like code completion and a text generator that can write complete essays and blog post...
Engineering an ML-Powered Developer-First Search Engine with Richard Socher - #582
July 11, 2022 17:09 - 46 minutesToday we’re joined by Richard Socher, the CEO of You.com. In our conversation with Richard, we explore the inspiration and motivation behind the You.com search engine, and how it differs from the traditional google search engine experience. We discuss some of the various ways that machine learning is used across the platform including how they surface relevant search results and some of the recent additions like code completion and a text generator that can write complete essays and blog post...
On The Path Towards Robot Vision with Aljosa Osep - #581
July 04, 2022 14:55 - 47 minutesToday we wrap up our coverage of the 2022 CVPR conference joined by Aljosa Osep, a postdoc at the Technical University of Munich & Carnegie Mellon University. In our conversation with Aljosa, we explore his broader research interests in achieving robot vision, and his vision for what it will look like when that goal is achieved. The first paper we dig into is Text2Pos: Text-to-Point-Cloud Cross-Modal Localization, which proposes a cross-modal localization module that learns to align textual d...
More Language, Less Labeling with Kate Saenko - #580
June 27, 2022 16:30 - 47 minutesToday we continue our CVPR series joined by Kate Saenko, an associate professor at Boston University and a consulting professor for the MIT-IBM Watson AI Lab. In our conversation with Kate, we explore her research in multimodal learning, which she spoke about at the Multimodal Learning and Applications Workshop, one of a whopping 6 workshops she spoke at. We discuss the emergence of multimodal learning, the current research frontier, and Kate’s thoughts on the inherent bias in LLMs and how to...
Optical Flow Estimation, Panoptic Segmentation, and Vision Transformers with Fatih Porikli - #579
June 20, 2022 17:18 - 51 minutesToday we kick off our annual coverage of the CVPR conference joined by Fatih Porikli, Senior Director of Engineering at Qualcomm AI Research. In our conversation with Fatih, we explore a trio of CVPR-accepted papers, as well as a pair of upcoming workshops at the event. The first paper, Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation, presents a novel framework to integrate semantic and instance contexts for panoptic segmentation. Next ...
Data Governance for Data Science with Adam Wood - #578
June 13, 2022 16:38 - 39 minutesToday we’re joined by Adam Wood, Director of Data Governance and Data Quality at Mastercard. In our conversation with Adam, we explore the challenges that come along with data governance at a global scale, including dealing with regional regulations like GDPR and federating records at scale. We discuss the role of feature stores in keeping track of data lineage and how Adam and his team have dealt with the challenges of metadata management, how large organizations like Mastercard are dealing ...
Feature Platforms for Data-Centric AI with Mike Del Balso - #577
June 06, 2022 19:28 - 46 minutesIn the latest installment of our Data-Centric AI series, we’re joined by a friend of the show Mike Del Balso, Co-founder and CEO of Tecton. If you’ve heard any of our other conversations with Mike, you know we spend a lot of time discussing feature stores, or as he now refers to them, feature platforms. We explore the current complexity of data infrastructure broadly and how that has changed over the last five years, as well as the maturation of streaming data platforms. We discuss the wide v...
The Fallacy of "Ground Truth" with Shayan Mohanty - #576
May 30, 2022 19:21 - 51 minutesToday we continue our Data-centric AI series joined by Shayan Mohanty, CEO at Watchful. In our conversation with Shayan, we focus on the data labeling aspect of the machine learning process, and ways that a data-centric approach could add value and reduce cost by multiple orders of magnitude. Shayan helps us define “data-centric”, while discussing the main challenges that organizations face when dealing with labeling, how these problems are currently being solved, and how techniques like acti...
Principle-centric AI with Adrien Gaidon - #575
May 23, 2022 18:49 - 47 minutesThis week, we continue our conversations around the topic of Data-Centric AI joined by a friend of the show Adrien Gaidon, the head of ML research at the Toyota Research Institute (TRI). In our chat, Adrien expresses a fourth, somewhat contrarian, viewpoint to the three prominent schools of thought that organizations tend to fall into, as well as a great story about how the breakthrough came via an unlikely source. We explore his principle-centric approach to machine learning as well as the r...
Data Debt in Machine Learning with D. Sculley - #574
May 19, 2022 19:31 - 36 minutesToday we kick things off with a conversation with D. Sculley, a director on the Google Brain team. Many listeners of today’s show will know D. from his work on the paper, The Hidden Technical Debt in Machine Learning Systems, and of course, the infamous diagram. D. has recently translated the idea of technical debt into data debt, something we spend a bit of time on in the interview. We discuss his view of the concept of DCAI, where debt fits into the conversation of data quality, and what a ...
AI for Enterprise Decisioning at Scale with Rob Walker - #573
May 16, 2022 15:36 - 39 minutesToday we’re joined by Rob Walker, VP of decisioning & analytics and gm of one-to-one customer engagement at Pegasystems. Rob, who you might know from his previous appearances on the podcast, joins us to discuss his work on AI and ML in the context of customer engagement and decisioning, the various problems that need to be solved, including solving the “next best” problem. We explore the distinction between the idea of the next best action and determining it from a recommender system, how the...
Data Rights, Quantification and Governance for Ethical AI with Margaret Mitchell - #572
May 12, 2022 16:43 - 41 minutesToday we close out our coverage of the ICLR series joined by Meg Mitchell, chief ethics scientist and researcher at Hugging Face. In our conversation with Meg, we discuss her participation in the WikiM3L Workshop, as well as her transition into her new role at Hugging Face, which has afforded her the ability to prioritize coding in her work around AI ethics. We explore her thoughts on the work happening in the fields of data curation and data governance, her interest in the inclusive sharing ...
Studying Machine Intelligence with Been Kim - #571
May 09, 2022 15:59 - 52 minutesToday we continue our ICLR coverage joined by Been Kim, a staff research scientist at Google Brain, and an ICLR 2022 Invited Speaker. Been, whose research has historically been focused on interpretability in machine learning, delivered the keynote Beyond interpretability: developing a language to shape our relationships with AI, which explores the need to study AI machines as scientific objects, in isolation and with humans, which will provide principles for tools, but also is necessary to ta...
Advances in Neural Compression with Auke Wiggers - #570
May 02, 2022 16:00 - 37 minutesToday we’re joined by Auke Wiggers, an AI research scientist at Qualcomm. In our conversation with Auke, we discuss his team’s recent research on data compression using generative models. We discuss the relationship between historical compression research and the current trend of neural compression, and the benefit of neural codecs, which learn to compress data from examples. We also explore the performance evaluation process and the recent developments that show that these models can operate...
Mixture-of-Experts and Trends in Large-Scale Language Modeling with Irwan Bello - #569
April 25, 2022 16:55 - 46 minutesToday we’re joined by Irwan Bello, formerly a research scientist at Google Brain, and now on the founding team at a stealth AI startup. We begin our conversation with an exploration of Irwan’s recent paper, Designing Effective Sparse Expert Models, which acts as a design guide for building sparse large language model architectures. We discuss mixture of experts as a technique, the scalability of this method, and it's applicability beyond NLP tasks the data sets this experiment was benchmarked...
Daring to DAIR: Distributed AI Research with Timnit Gebru - #568
April 18, 2022 16:00 - 51 minutesToday we’re joined by friend of the show Timnit Gebru, the founder and executive director of DAIR, the Distributed Artificial Intelligence Research Institute. In our conversation with Timnit, we discuss her journey to create DAIR, their goals and some of the challenges shes faced along the way. We start is the obvious place, Timnit being “resignated” from Google after writing and publishing a paper detailing the dangers of large language models, the fallout from that paper and her firing, and...
Hierarchical and Continual RL with Doina Precup - #567
April 11, 2022 16:38 - 50 minutesToday we’re joined by Doina Precup, a research team lead at DeepMind Montreal, and a professor at McGill University. In our conversation with Doina, we discuss her recent research interests, including her work in hierarchical reinforcement learning, with the goal being agents learning abstract representations, especially over time. We also explore her work on reward specification for RL agents, where she hypothesizes that a reward signal in a complex environment could lead an agent to develop...
Open-Source Drug Discovery with DeepChem with Bharath Ramsundar - #566
April 04, 2022 16:01 - 29 minutesToday we’re joined by Bharath Ramsundar, founder and CEO of Deep Forest Sciences. In our conversation with Bharath, we explore his work on the DeepChem, an open-source library for drug discovery, materials science, quantum chemistry, and biology tools. We discuss the challenges that biotech and pharmaceutical companies are facing as they attempt to incorporate AI into the drug discovery process, where the innovation frontier is, and what the promise is for AI in this field in the near term. W...
Advancing Hands-On Machine Learning Education with Sebastian Raschka - #565
March 28, 2022 16:18 - 40 minutesToday we’re joined by Sebastian Raschka, an assistant professor at the University of Wisconsin-Madison and lead AI educator at Grid.ai. In our conversation with Sebastian, we explore his work around AI education, including the “hands-on” philosophy that he takes when building these courses, his recent book Machine Learning with PyTorch and Scikit-Learn, his advise to beginners in the field when they’re trying to choose tools and frameworks, and more. We also discuss his work on Pytorch Light...
Big Science and Embodied Learning at Hugging Face 🤗 with Thomas Wolf - #564
March 21, 2022 16:00 - 47 minutesToday we’re joined by Thomas Wolf, co-founder and chief science officer at Hugging Face 🤗. We cover a ton of ground In our conversation, starting with Thomas’ interesting backstory as a quantum physicist and patent lawyer, and how that lead him to a career in machine learning. We explore how Hugging Face began, what the current direction is for the company, and how much of their focus is NLP and language models versus other disciplines. We also discuss the BigScience project, a year-long rese...
Full-Stack AI Systems Development with Murali Akula - #563
March 14, 2022 16:07 - 44 minutesToday we’re joined by Murali Akula, a Sr. director of Software Engineering at Qualcomm. In our conversation with Murali, we explore his role at Qualcomm, where he leads the corporate research team focused on the development and deployment of AI onto Snapdragon chips, their unique definition of “full stack”, and how that philosophy permeates into every step of the software development process. We explore the complexities that are unique to doing machine learning on resource constrained devices...
100x Improvements in Deep Learning Performance with Sparsity with Subutai Ahmad - #562
March 07, 2022 17:08 - 50 minutesToday 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 colum...
100x Improvements in Deep Learning Performance with Sparsity, w/ Subutai Ahmad - #562
March 07, 2022 17:08 - 50 minutesToday 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 colum...
Scaling BERT and GPT-3 for Financial Services with Jennifer Glore - #561
February 28, 2022 16:55 - 44 minutesToday 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 mode...
Scaling BERT and GPT for Financial Services with Jennifer Glore - #561
February 28, 2022 16:55 - 44 minutesToday 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 mode...
Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560
February 21, 2022 17:05 - 1 hourToday 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 we...
Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559
February 14, 2022 17:57 - 51 minutesToday 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 evaluate...
Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558
February 07, 2022 17:00 - 53 minutesToday 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 t...
Differentiable Programming for Oceanography with Patrick Heimbach - #557
January 31, 2022 17:42 - 34 minutesToday 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...
Trends in Machine Learning & Deep Learning with Zachary Lipton - #556
January 27, 2022 17:31 - 1 hourToday 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 l...
Solving the Cocktail Party Problem with Machine Learning, w/ Jonathan Le Roux - #555
January 24, 2022 17:14 - 35 minutesToday 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 t...
Machine Learning for Earthquake Seismology with Karianne Bergen - #554
January 20, 2022 17:12 - 35 minutesToday 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 th...
The New DBfication of ML/AI with Arun Kumar - #553
January 17, 2022 17:22 - 46 minutesToday 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 po...
Building Public Interest Technology with Meredith Broussard - #552
January 13, 2022 18:05 - 30 minutesToday we’re joined by Meredith Broussard, an associate professor at NYU & research director at the NYU Alliance for Public Interest Technology. Meredith was a keynote speaker at the recent NeurIPS conference, and we had the pleasure of speaking with her to discuss her talk from the event, and her upcoming book, tentatively titled More Than A Glitch: What Everyone Needs To Know About Making Technology Anti-Racist, Accessible, And Otherwise Useful To All. In our conversation, we explore Meredit...
A Universal Law of Robustness via Isoperimetry with Sebastien Bubeck - #551
January 10, 2022 17:23 - 39 minutesToday we’re joined by Sebastian Bubeck a sr principal research manager at Microsoft, and author of the paper A Universal Law of Robustness via Isoperimetry, a NeurIPS 2021 Outstanding Paper Award recipient. We begin our conversation with Sebastian with a bit of a primer on convex optimization, a topic that hasn’t come up much in previous interviews. We explore the problem that convex optimization is trying to solve, the application of convex optimization to multi-armed bandit problems, metric...
Trends in NLP with John Bohannon - #550
January 06, 2022 18:07 - 1 hourToday we’re joined by friend of the show John Bohannon, the director of science at Primer AI, to help us showcase all of the great achievements and accomplishments in NLP in 2021! In our conversation, John shares his two major takeaways from last year, 1) NLP as we know it has changed, and we’re back into the incremental phase of the science, and 2) NLP is “eating” the rest of machine learning. We explore the implications of these two major themes across the discipline, as well as best papers...
Trends in Computer Vision with Georgia Gkioxari - #549
January 03, 2022 20:09 - 58 minutesHappy New Year! We’re excited to kick off 2022 joined by Georgia Gkioxari, a research scientist at Meta AI, to showcase the best advances in the field of computer vision over the past 12 months, and what the future holds for this domain. Welcome back to AI Rewind! In our conversation Georgia highlights the emergence of the transformer model in CV research, what kind of performance results we’re seeing vs CNNs, and the immediate impact of NeRF, amongst a host of other great research. We also ...
Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik - #548
December 27, 2021 17:10 - 36 minutesToday we close out the 2021 NeurIPS series joined by Alison Gopnik, a professor at UC Berkeley and an invited speaker at the Causal Inference & Machine Learning: Why now? Workshop. In our conversation with Alison, we explore the question, “how is it that we can know so much about the world around us from so little information?,” and how her background in psychology, philosophy, and epistemology has guided her along the path to finding this answer through the actions of children. We discuss th...
Hypergraphs, Simplicial Complexes and Graph Representations of Complex Systems with Tina Eliassi-Rad - #547
December 23, 2021 17:46 - 35 minutesToday we continue our NeurIPS coverage joined by Tina Eliassi-Rad, a professor at Northeastern University, and an invited speaker at the I Still Can't Believe It's Not Better! Workshop. In our conversation with Tina, we explore her research at the intersection of network science, complex networks, and machine learning, how graphs are used in her work and how it differs from typical graph machine learning use cases. We also discuss her talk from the workshop, “The Why, How, and When of Represe...
Deep Learning, Transformers, and the Consequences of Scale with Oriol Vinyals - #546
December 20, 2021 16:29 - 52 minutesToday we’re excited to kick off our annual NeurIPS, joined by Oriol Vinyals, the lead of the deep learning team at Deepmind. We cover a lot of ground in our conversation with Oriol, beginning with a look at his research agenda and why the scope has remained wide even through the maturity of the field, his thoughts on transformer models and if they will get us beyond the current state of DL, or if some other model architecture would be more advantageous. We also touch on his thoughts on the la...
Optimization, Machine Learning and Intelligent Experimentation with Michael McCourt - #545
December 16, 2021 17:49 - 45 minutesToday we’re joined by Michael McCourt the head of engineering at SigOpt. In our conversation with Michael, we explore the vast space around the topic of optimization, including the technical differences between ML and optimization and where they’re applied, what the path to increasing complexity looks like for a practitioner and the relationship between optimization and active learning. We also discuss the research frontier for optimization and how folks think about the interesting challenges...
Jupyter and the Evolution of ML Tooling with Brian Granger - #544
December 13, 2021 17:00 - 57 minutesToday we conclude our AWS re:Invent coverage joined by Brian Granger, a senior principal technologist at Amazon Web Services, and a co-creator of Project Jupyter. In our conversion with Brian, we discuss the inception and early vision of Project Jupyter, including how the explosion of machine learning and deep learning shifted the landscape for the notebook, and how they balanced the needs of these new user bases vs their existing community of scientific computing users. We also explore AWS’s...
Creating a Data-Driven Culture at ADP with Jack Berkowitz - #543
December 09, 2021 16:46 - 34 minutesToday we continue our 2021 re:Invent series joined by Jack Berkowitz, chief data officer at ADP. In our conversation with Jack, we explore the ever evolving role and growth of machine learning at the company, from the evolution of their ML platform, to the unique team structure. We discuss Jack’s perspective on data governance, the broad use cases for ML, how they approached the decision to move to the cloud, and the impact of scale in the way they deal with data. Finally, we touch on where i...