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

  • Auteur(s): Sam Charrington
  • Podcast

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

Auteur(s): Sam Charrington
Écouter gratuitement

  • Résumé

  • 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
    Voir plus Voir moins
Politique Science
Voir plus Voir moins
Épisodes
  • 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.
    Voir plus Voir moins
    1 h et 45 min
  • 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.
    Voir plus Voir moins
    1 h et 17 min
  • Ensuring Privacy for Any LLM with Patricia Thaine - #716

    Ensuring Privacy for Any LLM with Patricia Thaine - #716
    Jan 28 2025
    Today, we're joined by Patricia Thaine, co-founder and CEO of Private AI to discuss techniques for ensuring privacy, data minimization, and compliance when using 3rd-party large language models (LLMs) and other AI services. We explore the risks of data leakage from LLMs and embeddings, the complexities of identifying and redacting personal information across various data flows, and the approach Private AI has taken to mitigate these risks. We also dig into the challenges of entity recognition in multimodal systems including OCR files, documents, images, and audio, and the importance of data quality and model accuracy. Additionally, Patricia shares insights on the limitations of data anonymization, the benefits of balancing real-world and synthetic data in model training and development, and the relationship between privacy and bias in AI. Finally, we touch on the evolving landscape of AI regulations like GDPR, CPRA, and the EU AI Act, and the future of privacy in artificial intelligence. The complete show notes for this episode can be found at https://twimlai.com/go/716.
    Voir plus Voir moins
    52 min

Ce que les auditeurs disent de The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Moyenne des évaluations de clients

Évaluations – Cliquez sur les onglets pour changer la source des évaluations.

Évaluations sur Audible.ca

Évaluations sur Amazon.ca
Il n'y a pas encore de critiques pour ce titre.