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

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

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

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

Today we’re joined by return guest Ken Goldberg, a professor at UC Berkeley and the chief scientist at Ambi Robotics. It’s been a few years since our initial conversation with Ken, so we spent a bit of time talking through the progress that has been made in robotics in the time that has passed. We discuss Ken’s recent work, including the paper Autonomously Untangling Long Cables, which won Best Systems Paper at the RSS conference earlier this year, including the complexity of the problem and why it is classified as a systems challenge, as well as the advancements in hardware that made solving this problem possible. We also explore Ken’s thoughts on the push towards simulation by research entities and large tech companies, and the potential for causal modeling to find its way into robotics. Finally, we discuss the recent showcase of Optimus, Tesla, and Elon Musk’s “humanoid” robot and how far we are from it being a viable piece of technology. The complete show notes for this episode can be found at twimlai.com/go/599.

14 Nov 202247min

Today friend of the show and esteemed guest host John Bohannon is back with another great interview, this time around joined by Oren Etzioni, former CEO of the Allen Institute for AI, where he is currently an advisor. In our conversation with Oren, we discuss his philosophy as a researcher and how that has manifested in his pivot to institution builder. We also explore his thoughts on the current landscape of NLP, including the emergence of LLMs and the hype being built up around AI systems from folks like Elon Musk. Finally, we explore some of the research coming out of AI2, including Semantic Scholar, an AI-powered research tool analogous to arxiv, and the somewhat controversial Delphi project, a research prototype designed to model people’s moral judgments on a variety of everyday situations.

7 Nov 202253min

Over the last few years, it’s been established that your ML team needs at least some basic tooling in order to be effective, providing support for various aspects of the machine learning workflow, from data acquisition and management, to model development and optimization, to model deployment and monitoring. But how do you get there? Many tools available off the shelf, both commercial and open source, can help. At the extremes, these tools can fall into one of a couple of buckets. End-to-end platforms that try to provide support for many aspects of the ML lifecycle, and specialized tools that offer deep functionality in a particular domain or area. At TWIMLcon: AI Platforms 2022, our panelists debated the merits of these approaches in The Great MLOps Debate: End-to-End ML Platforms vs Specialized Tools.

31 Okt 202247min

Much of the way we talk and think about MLOps comes from the perspective of large consumer internet companies like Facebook or Google. If you work at a FAANG company, these approaches might work well for you. But what about if you work at one of the many small, B2B companies that stand to benefit through the use of machine learning? How should you be thinking about MLOps and the ML lifecycle in that case? In this live podcast interview from TWIMLcon: AI Platforms 2022, Sam Charrington explores these questions with Jacopo Tagliabue, whose perspectives and contributions on scaling down MLOps have served to make the field more accessible and relevant to a wider array of practitioners.

24 Okt 202249min

Today we’re joined by Ali Rodell, a senior director of machine learning engineering at Capital One. In our conversation with Ali, we explore his role as the head of model development platforms at Capital One, including how his 25+ years in software development have shaped his view on building platforms and the evolution of the platforms space over the last 10 years. We discuss the importance of a healthy open source tooling ecosystem, Capital One’s use of various open source capabilites like kubeflow and kubernetes to build out platforms, and some of the challenges that come along with modifying/customizing these tools to work for him and his teams. Finally, we explore the range of user personas that need to be accounted for when making decisions about tooling, supporting things like Jupyter notebooks and other low level tools, and how that can be potentially challenging in a highly regulated environment like the financial industry. The complete show notes for this episode can be found at twimlai.com/go/595

17 Okt 202243min