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

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

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

De: Sam Charrington
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 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.All rights reserved Ciencia Política y Gobierno
Episodios
  • LLMs for Equities Feature Forecasting at Two Sigma with Ben Wellington - #736

    LLMs for Equities Feature Forecasting at Two Sigma with Ben Wellington - #736
    Jun 17 2025
    Today, we're joined by Ben Wellington, deputy head of feature forecasting at Two Sigma. We dig into the team’s end-to-end approach to leveraging AI in equities feature forecasting, covering how they identify and create features, collect and quantify historical data, and build predictive models to forecast market behavior and asset prices for trading and investment. We explore the firm's platform-centric approach to managing an extensive portfolio of features and models, the impact of multimodal LLMs on accelerating the process of extracting novel features, the importance of strict data timestamping to prevent temporal leakage, and the way they consider build vs. buy decisions in a rapidly evolving landscape. Lastly, Ben also shares insights on leveraging open-source models and the future of agentic AI in quantitative finance. The complete show notes for this episode can be found at https://twimlai.com/go/736.
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    1 h
  • Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735

    Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735
    Jun 10 2025
    Today, we're joined by Jason Corso, co-founder of Voxel51 and professor at the University of Michigan, to explore automated labeling in computer vision. Jason introduces FiftyOne, an open-source platform for visualizing datasets, analyzing models, and improving data quality. We focus on Voxel51’s recent research report, “Zero-shot auto-labeling rivals human performance,” which demonstrates how zero-shot auto-labeling with foundation models can yield to significant cost and time savings compared to traditional human annotation. Jason explains how auto-labels, despite being "noisier" at lower confidence thresholds, can lead to better downstream model performance. We also cover Voxel51's "verified auto-labeling" approach, which utilizes a "stoplight" QA workflow (green, yellow, red light) to minimize human review. Finally, we discuss the challenges of handling decision boundary uncertainty and out-of-domain classes, the differences between synthetic data generation in vision and language domains, and the potential of agentic labeling. The complete show notes for this episode can be found at https://twimlai.com/go/735.
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    57 m
  • Grokking, Generalization Collapse, and the Dynamics of Training Deep Neural Networks with Charles Martin - #734

    Grokking, Generalization Collapse, and the Dynamics of Training Deep Neural Networks with Charles Martin - #734
    Jun 5 2025
    Today, we're joined by Charles Martin, founder of Calculation Consulting, to discuss Weight Watcher, an open-source tool for analyzing and improving Deep Neural Networks (DNNs) based on principles from theoretical physics. We explore the foundations of the Heavy-Tailed Self-Regularization (HTSR) theory that underpins it, which combines random matrix theory and renormalization group ideas to uncover deep insights about model training dynamics. Charles walks us through WeightWatcher’s ability to detect three distinct learning phases—underfitting, grokking, and generalization collapse—and how its signature “layer quality” metric reveals whether individual layers are underfit, overfit, or optimally tuned. Additionally, we dig into the complexities involved in fine-tuning models, the surprising correlation between model optimality and hallucination, the often-underestimated challenges of search relevance, and their implications for RAG. Finally, Charles shares his insights into real-world applications of generative AI and his lessons learned from working in the field. The complete show notes for this episode can be found at https://twimlai.com/go/734.
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    1 h y 25 m
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