• 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 cover art

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
  • Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682

    Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682
    Apr 29 2024
    Today we're joined by Azarakhsh (Aza) Jalalvand, a research scholar at Princeton University, to discuss his work using deep reinforcement learning to control plasma instabilities in nuclear fusion reactors. Aza explains his team developed a model to detect and avoid a fatal plasma instability called ‘tearing mode’. Aza walks us through the process of collecting and pre-processing the complex diagnostic data from fusion experiments, training the models, and deploying the controller algorithm on the DIII-D fusion research reactor. He shares insights from developing the controller and discusses the future challenges and opportunities for AI in enabling stable and efficient fusion energy production. The complete show notes for this episode can be found at twimlai.com/go/682.
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    42 mins
  • GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

    GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681
    Apr 22 2024
    Today we're joined by Kirk Marple, CEO and founder of Graphlit, to explore the emerging paradigm of "GraphRAG," or Graph Retrieval Augmented Generation. In our conversation, Kirk digs into the GraphRAG architecture and how Graphlit uses it to offer a multi-stage workflow for ingesting, processing, retrieving, and generating content using LLMs (like GPT-4) and other Generative AI tech. He shares how the system performs entity extraction to build a knowledge graph and how graph, vector, and object storage are integrated in the system. We dive into how the system uses “prompt compilation” to improve the results it gets from Large Language Models during generation. We conclude by discussing several use cases the approach supports, as well as future agent-based applications it enables. The complete show notes for this episode can be found at twimlai.com/go/681.
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    47 mins
  • Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

    Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680
    Apr 16 2024
    Today we're joined by Alex Havrilla, a PhD student at Georgia Tech, to discuss "Teaching Large Language Models to Reason with Reinforcement Learning." Alex discusses the role of creativity and exploration in problem solving and explores the opportunities presented by applying reinforcement learning algorithms to the challenge of improving reasoning in large language models. Alex also shares his research on the effect of noise on language model training, highlighting the robustness of LLM architecture. Finally, we delve into the future of RL, and the potential of combining language models with traditional methods to achieve more robust AI reasoning. The complete show notes for this episode can be found at twimlai.com/go/680.
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    46 mins
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