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

De: Sam Charrington
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  • Resumen

  • 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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Episodios
  • Simplifying On-Device AI for Developers with Siddhika Nevrekar - #697

    Simplifying On-Device AI for Developers with Siddhika Nevrekar - #697
    Aug 12 2024
    Today, we're joined by Siddhika Nevrekar, AI Hub head at Qualcomm Technologies, to discuss on-device AI and how to make it easier for developers to take advantage of device capabilities. We unpack the motivations for AI engineers to move model inference from the cloud to local devices, and explore the challenges associated with on-device AI. We dig into the role of hardware solutions, from powerful system-on-chips (SoC) to neural processors, the importance of collaboration between community runtimes like ONNX and TFLite and chip manufacturers, the unique challenges of IoT and autonomous vehicles, and the key metrics developers should focus on to ensure optimal on-device performance. Finally, Siddhika introduces Qualcomm's AI Hub, a platform developed to simplify the process of testing and optimizing AI models across different devices. The complete show notes for this episode can be found at https://twimlai.com/go/697.
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    47 m
  • Genie: Generative Interactive Environments with Ashley Edwards - #696

    Genie: Generative Interactive Environments with Ashley Edwards - #696
    Aug 5 2024
    Today, we're joined by Ashley Edwards, a member of technical staff at Runway, to discuss Genie: Generative Interactive Environments, a system for creating ‘playable’ video environments for training deep reinforcement learning (RL) agents at scale in a completely unsupervised manner. We explore the motivations behind Genie, the challenges of data acquisition for RL, and Genie’s capability to learn world models from videos without explicit action data, enabling seamless interaction and frame prediction. Ashley walks us through Genie’s core components—the latent action model, video tokenizer, and dynamics model—and explains how these elements collaborate to predict future frames in video sequences. We discuss the model architecture, training strategies, benchmarks used, as well as the application of spatiotemporal transformers and the MaskGIT techniques used for efficient token prediction and representation. Finally, we touched on Genie’s practical implications, its comparison to other video generation models like “Sora,” and potential future directions in video generation and diffusion models. The complete show notes for this episode can be found at https://twimlai.com/go/696.
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    47 m
  • Bridging the Sim2real Gap in Robotics with Marius Memmel - #695

    Bridging the Sim2real Gap in Robotics with Marius Memmel - #695
    Jul 30 2024
    Today, we're joined by Marius Memmel, a PhD student at the University of Washington, to discuss his research on sim-to-real transfer approaches for developing autonomous robotic agents in unstructured environments. Our conversation focuses on his recent ASID and URDFormer papers. We explore the complexities presented by real-world settings like a cluttered kitchen, data acquisition challenges for training robust models, the importance of simulation, and the challenge of bridging the sim2real gap in robotics. Marius introduces ASID, a framework designed to enable robots to autonomously generate and refine simulation models to improve sim-to-real transfer. We discuss the role of Fisher information as a metric for trajectory sensitivity to physical parameters and the importance of exploration and exploitation phases in robot learning. Additionally, we cover URDFormer, a transformer-based model that generates URDF documents for scene and object reconstruction to create realistic simulation environments. The complete show notes for this episode can be found at https://twimlai.com/go/695.
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    57 m
Política y Gobierno Ciencia
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