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

  • By: Sam Charrington
  • Podcast
  • 5.0 out of 5 stars (2 ratings)

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

By: Sam Charrington
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  • Summary

  • 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.
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Episodes
  • AI Agents for Data Analysis with Shreya Shankar - #703

    AI Agents for Data Analysis with Shreya Shankar - #703
    Oct 1 2024
    Today, we're joined by Shreya Shankar, a PhD student at UC Berkeley to discuss DocETL, a declarative system for building and optimizing LLM-powered data processing pipelines for large-scale and complex document analysis tasks. We explore how DocETL's optimizer architecture works, the intricacies of building agentic systems for data processing, the current landscape of benchmarks for data processing tasks, how these differ from reasoning-based benchmarks, and the need for robust evaluation methods for human-in-the-loop LLM workflows. Additionally, Shreya shares real-world applications of DocETL, the importance of effective validation prompts, and building robust and fault-tolerant agentic systems. Lastly, we cover the need for benchmarks tailored to LLM-powered data processing tasks and the future directions for DocETL. The complete show notes for this episode can be found at https://twimlai.com/go/703.
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    48 mins
  • Stealing Part of a Production Language Model with Nicholas Carlini - #702

    Stealing Part of a Production Language Model with Nicholas Carlini - #702
    Sep 23 2024
    Today, we're joined by Nicholas Carlini, research scientist at Google DeepMind to discuss adversarial machine learning and model security, focusing on his 2024 ICML best paper winner, “Stealing part of a production language model.” We dig into this work, which demonstrated the ability to successfully steal the last layer of production language models including ChatGPT and PaLM-2. Nicholas shares the current landscape of AI security research in the age of LLMs, the implications of model stealing, ethical concerns surrounding model privacy, how the attack works, and the significance of the embedding layer in language models. We also discuss the remediation strategies implemented by OpenAI and Google, and the future directions in the field of AI security. Plus, we also cover his other ICML 2024 best paper, “Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining,” which questions the use and promotion of differential privacy in conjunction with pre-trained models. The complete show notes for this episode can be found at https://twimlai.com/go/702.
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    1 hr and 4 mins
  • Supercharging Developer Productivity with ChatGPT and Claude with Simon Willison - #701

    Supercharging Developer Productivity with ChatGPT and Claude with Simon Willison - #701
    Sep 16 2024
    Today, we're joined by Simon Willison, independent researcher and creator of Datasette to discuss the many ways software developers and engineers can take advantage of large language models (LLMs) to boost their productivity. We dig into Simon’s own workflows and how he uses popular models like ChatGPT and Anthropic’s Claude to write and test hundreds of lines of code while out walking his dog. We review Simon’s favorite prompting and debugging techniques, his strategies for sidestepping the limitations of contemporary models, how he uses Claude’s Artifacts feature for rapid prototyping, his thoughts on the use and impact of vision models, the role he sees for open source models and local LLMs, and much more. The complete show notes for this episode can be found at https://twimlai.com/go/701.
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    1 hr and 14 mins
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