592 episodes

Machine 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.

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

    • Technology
    • 4.7 • 346 Ratings

Machine 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.

    Principle-centric AI with Adrien Gaidon

    Principle-centric AI with Adrien Gaidon

    This 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 role of self-supervised machine learning and synthetic data in this and other research threads. Make sure you’re following along with the entire DCAI series at twimlai.com/go/dcai.
    The complete show notes for this episode can be found at twimlai.com/go/575

    • 47 min
    Data Debt in Machine Learning with D. Sculley

    Data Debt in Machine Learning with D. Sculley

    Today 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 shift towards data-centrism looks like in a world of increasingly larger models i.e. GPT-3 and the recent PALM models. We also explore common sources of data debt, what are things that the community can and have done to mitigate these issues, the usefulness of causal inference graphs in this work, and much more! If you enjoyed this interview or want to hear more on this topic, check back on the DCAI series page weekly at https://twimlai.com/podcast/twimlai/series/data-centric-ai.
    The complete show notes for this episode can be found at twimlai.com/go/574

    • 36 min
    AI for Enterprise Decisioning at Scale with Rob Walker

    AI for Enterprise Decisioning at Scale with Rob Walker

    Today 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 combination of machine learning and heuristics are currently co-existing in engagements, scaling model evaluation, and some of the challenges they’re facing when dealing with problems of responsible AI and how they’re managed. Finally, we spend a few minutes digging into the upcoming PegaWorld conference, and what attendees should anticipate at the event.
    The complete show notes for this episode can be found at twimlai.com/go/573

    • 38 min
    Data Rights, Quantification and Governance for Ethical AI with Margaret Mitchell

    Data Rights, Quantification and Governance for Ethical AI with Margaret Mitchell

    Today 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 of datasets and creation of models that don't disproportionately underperform or exploit subpopulations, and how data collection practices have changed over the years. 
    We also touch on changes to data protection laws happening in some pretty uncertain places, the evolution of her work on Model Cards, and how she’s using this and recent Data Cards work to lower the barrier to entry to responsibly informed development of data and sharing of data.
    The complete show notes for this episode can be found at twimlai.com/go/572

    • 41 min
    Studying Machine Intelligence with Been Kim

    Studying Machine Intelligence with Been Kim

    Today 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 take our working relationship with AI to the next level. 
    Before we dig into Been’s talk, she characterizes where we are as an industry and community with interpretability, and what the current state of the art is for interpretability techniques. We explore how the Gestalt principles appear in neural networks, Been’s choice to characterize communication with machines as a language as opposed to a set of principles or foundational understanding, and much much more.
    The complete show notes for this episode can be found at twimlai.com/go/571

    • 52 min
    Advances in Neural Compression with Auke Wiggers

    Advances in Neural Compression with Auke Wiggers

    Today 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 in real-time on a mobile device. Finally, we discuss another ICLR paper, “Transformer-based transform coding”, that proposes a vision transformer-based architecture for image and video coding, and some of his team’s other accepted works at the conference. 
    The complete show notes for this episode can be found at twimlai.com/go/570

    • 36 min

Customer Reviews

4.7 out of 5
346 Ratings

346 Ratings

fitelson ,

A premier podcast on AI/ML

I have enjoyed listening to many of the episodes and had fun participating in one

steve5688547 ,

Lots of potential but incompetent host

The guests are amazing and this could be such an amazing podcast for the ML community. Unfortunately, the host is both a poor conversationalist (interviews lack flow, feel disjointed and tortured), and comes to the interviews so poorly informed that he struggles to put follow up questions together or even understand what the guest is saying.

rohitand ,

excellent machine learning perspective

Sam puts lot of attention to every episode. Information is high quality and easy to grasp.

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