Geospatial Machine Learning at AWS with Kumar Chellapilla - #607
Today we continue our re:Invent 2022 series joined by Kumar Chellapilla, a general manager of ML and AI Services at AWS. We had the opportunity to speak with Kumar after announcing their recent addition of geospatial data to the SageMaker Platform. In our conversation, we explore Kumar’s role as the GM for a diverse array of SageMaker services, what has changed in the geospatial data landscape over the last 10 years, and why Amazon decided now was the right time to invest in geospatial data. We discuss the challenges of accessing and working with this data and the pain points they’re trying to solve. Finally, Kumar walks us through a few customer use cases, describes how this addition will make users more effective than they currently are, and shares his thoughts on the future of this space over the next 2-5 years, including the potential intersection of geospatial data and stable diffusion/generative models. The complete show notes for this episode can be found at twimlai.com/go/607
22 Dec 202236min
Real-Time ML Workflows at Capital One with Disha Singla - #606
Today we’re joined by Disha Singla, a senior director of machine learning engineering at Capital One. In our conversation with Disha, we explore her role as the leader of the Data Insights team at Capital One, where they’ve been tasked with creating reusable libraries, components, and workflows to make ML usable broadly across the company, as well as a platform to make it all accessible and to drive meaningful insights. We discuss the construction of her team, as well as the types of interactions and requests they receive from their customers (data scientists), productionized use cases from the platform, and their efforts to transition from batch to real-time deployment. Disha also shares her thoughts on the ROI of machine learning and getting buy-in from executives, how she sees machine learning evolving at the company over the next 10 years, and much more! The complete show notes for this episode can be found at twimlai.com/go/606
19 Dec 202243min
Weakly Supervised Causal Representation Learning with Johann Brehmer - #605
Today we’re excited to kick off our coverage of the 2022 NeurIPS conference with Johann Brehmer, a research scientist at Qualcomm AI Research in Amsterdam. We begin our conversation discussing some of the broader problems that causality will help us solve, before turning our focus to Johann’s paper Weakly supervised causal representation learning, which seeks to prove that high-level causal representations are identifiable in weakly supervised settings. We also discuss a few other papers that the team at Qualcomm presented, including neural topological ordering for computation graphs, as well as some of the demos they showcased, which we’ll link to on the show notes page. The complete show notes for this episode can be found at twimlai.com/go/605.
15 Dec 202246min
Stable Diffusion & Generative AI with Emad Mostaque - #604
Today we’re excited to kick off our 2022 AWS re:Invent series with a conversation with Emad Mostaque, Founder and CEO of Stability.ai. Stability.ai is a very popular name in the generative AI space at the moment, having taken the internet by storm with the release of its stable diffusion model just a few months ago. In our conversation with Emad, we discuss the story behind Stability's inception, the model's speed and scale, and the connection between stable diffusion and programming. We explore some of the spaces that Emad anticipates being disrupted by this technology, his thoughts on the open-source vs API debate, how they’re dealing with issues of user safety and artist attribution, and of course, what infrastructure they’re using to stand the model up. The complete show notes for this episode can be found at https://twimlai.com/go/604.
12 Dec 202242min
Exploring Large Language Models with ChatGPT - #603
Today we're joined by ChatGPT, the latest and coolest large language model developed by OpenAl. In our conversation with ChatGPT, we discuss the background and capabilities of large language models, the potential applications of these models, and some of the technical challenges and open questions in the field. We also explore the role of supervised learning in creating ChatGPT, and the use of PPO in training the model. Finally, we discuss the risks of misuse of large language models, and the best resources for learning more about these models and their applications. Join us for a fascinating conversation with ChatGPT, and learn more about the exciting world of large language models. The complete show notes for this episode can be found at https://twimlai.com/go/603
8 Dec 202236min
Accelerating Intelligence with AI-Generating Algorithms with Jeff Clune - #602
Are AI-generating algorithms the path to artificial general intelligence(AGI)? Today we’re joined by Jeff Clune, an associate professor of computer science at the University of British Columbia, and faculty member at the Vector Institute. In our conversation with Jeff, we discuss the broad ambitious goal of the AI field, artificial general intelligence, where we are on the path to achieving it, and his opinion on what we should be doing to get there, specifically, focusing on AI generating algorithms. With the goal of creating open-ended algorithms that can learn forever, Jeff shares his three pillars to an AI-GA, meta-learning architectures, meta-learning algorithms, and auto-generating learning environments. Finally, we discuss the inherent safety issues with these learning algorithms and Jeff’s thoughts on how to combat them, and what the not-so-distant future holds for this area of research. The complete show notes for this episode can be found at twimlai.com/go/602.
5 Dec 202256min
Programmatic Labeling and Data Scaling for Autonomous Commercial Aviation with Cedric Cocaud - #601
Today we’re joined by Cedric Cocaud, the chief engineer of the Wayfinder Group at Acubed, the innovation center for aircraft manufacturer Airbus. In our conversation with Cedric, we explore some of the technical challenges of innovation in the aircraft space, including autonomy. Cedric’s work on Project Vahana, Acubed’s foray into air taxis, attempted to leverage work in the self-driving car industry to develop fully autonomous planes. We discuss some of the algorithms being developed for this work, the data collection process, and Cedric’s thoughts on using synthetic data for these tasks. We also discuss the challenges of labeling the data, including programmatic and automated labeling, and much more.
28 Nov 202254min
Engineering Production NLP Systems at T-Mobile with Heather Nolis - #600
Today we’re joined by Heather Nolis, a principal machine learning engineer at T-Mobile. In our conversation with Heather, we explored her machine learning journey at T-Mobile, including their initial proof of concept project, which held the goal of putting their first real-time deep learning model into production. We discuss the use case, which aimed to build a model customer intent model that would pull relevant information about a customer during conversations with customer support. This process has now become widely known as blank assist. We also discuss the decision to use supervised learning to solve this problem and the challenges they faced when developing a taxonomy. Finally, we explore the idea of using small models vs uber-large models, the hardware being used to stand up their infrastructure, and how Heather thinks about the age-old question of build vs buy.
21 Nov 202243min
