• 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
Listen for free

  • 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.
    All rights reserved
    Show more Show less
Politics & Government Science
Show more Show less
Episodes
  • π0: A Foundation Model for Robotics with Sergey Levine - #719

    π0: A Foundation Model for Robotics with Sergey Levine - #719
    Feb 18 2025
    Today, we're joined by Sergey Levine, associate professor at UC Berkeley and co-founder of Physical Intelligence, to discuss π0 (pi-zero), a general-purpose robotic foundation model. We dig into the model architecture, which pairs a vision language model (VLM) with a diffusion-based action expert, and the model training "recipe," emphasizing the roles of pre-training and post-training with a diverse mixture of real-world data to ensure robust and intelligent robot learning. We review the data collection approach, which uses human operators and teleoperation rigs, the potential of synthetic data and reinforcement learning in enhancing robotic capabilities, and much more. We also introduce the team’s new FAST tokenizer, which opens the door to a fully Transformer-based model and significant improvements in learning and generalization. Finally, we cover the open-sourcing of π0 and future directions for their research. The complete show notes for this episode can be found at https://twimlai.com/go/719.
    Show more Show less
    53 mins
  • AI Trends 2025: AI Agents and Multi-Agent Systems with Victor Dibia - #718

    AI Trends 2025: AI Agents and Multi-Agent Systems with Victor Dibia - #718
    Feb 10 2025
    Today we’re joined by Victor Dibia, principal research software engineer at Microsoft Research, to explore the key trends and advancements in AI agents and multi-agent systems shaping 2025 and beyond. In this episode, we discuss the unique abilities that set AI agents apart from traditional software systems–reasoning, acting, communicating, and adapting. We also examine the rise of agentic foundation models, the emergence of interface agents like Claude with Computer Use and OpenAI Operator, the shift from simple task chains to complex workflows, and the growing range of enterprise use cases. Victor shares insights into emerging design patterns for autonomous multi-agent systems, including graph and message-driven architectures, the advantages of the “actor model” pattern as implemented in Microsoft’s AutoGen, and guidance on how users should approach the ”build vs. buy” decision when working with AI agent frameworks. We also address the challenges of evaluating end-to-end agent performance, the complexities of benchmarking agentic systems, and the implications of our reliance on LLMs as judges. Finally, we look ahead to the future of AI agents in 2025 and beyond, discuss emerging HCI challenges, their potential for impact on the workforce, and how they are poised to reshape fields like software engineering. The complete show notes for this episode can be found at https://twimlai.com/go/718.
    Show more Show less
    1 hr and 45 mins
  • Speculative Decoding and Efficient LLM Inference with Chris Lott - #717

    Speculative Decoding and Efficient LLM Inference with Chris Lott - #717
    Feb 4 2025
    Today, we're joined by Chris Lott, senior director of engineering at Qualcomm AI Research to discuss accelerating large language model inference. We explore the challenges presented by the LLM encoding and decoding (aka generation) and how these interact with various hardware constraints such as FLOPS, memory footprint and memory bandwidth to limit key inference metrics such as time-to-first-token, tokens per second, and tokens per joule. We then dig into a variety of techniques that can be used to accelerate inference such as KV compression, quantization, pruning, speculative decoding, and leveraging small language models (SLMs). We also discuss future directions for enabling on-device agentic experiences such as parallel generation and software tools like Qualcomm AI Orchestrator. The complete show notes for this episode can be found at https://twimlai.com/go/717.
    Show more Show less
    1 hr and 17 mins

What listeners say about The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Average customer ratings
Overall
  • 5 out of 5 stars
  • 5 Stars
    2
  • 4 Stars
    0
  • 3 Stars
    0
  • 2 Stars
    0
  • 1 Stars
    0
Performance
  • 5 out of 5 stars
  • 5 Stars
    2
  • 4 Stars
    0
  • 3 Stars
    0
  • 2 Stars
    0
  • 1 Stars
    0
Story
  • 5 out of 5 stars
  • 5 Stars
    2
  • 4 Stars
    0
  • 3 Stars
    0
  • 2 Stars
    0
  • 1 Stars
    0

Reviews - Please select the tabs below to change the source of reviews.

Audible.com reviews

Amazon reviews
No Reviews are Available