Episódios

  • AI for Network Management with Shirley Wu - #710

    AI for Network Management with Shirley Wu - #710
    Nov 19 2024
    Today, we're joined by Shirley Wu, senior director of software engineering at Juniper Networks to discuss how machine learning and artificial intelligence are transforming network management. We explore various use cases where AI and ML are applied to enhance the quality, performance, and efficiency of networks across Juniper’s customers, including diagnosing cable degradation, proactive monitoring for coverage gaps, and real-time fault detection. We also dig into the complexities of integrating data science into networking, the trade-offs between traditional methods and ML-based solutions, the role of feature engineering and data in networking, the applicability of large language models, and Juniper’s approach to using smaller, specialized ML models to optimize speed, latency, and cost. Finally, Shirley shares some future directions for Juniper Mist such as proactive network testing and end-user self-service. The complete show notes for this episode can be found at https://twimlai.com/go/710.
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    54 minutos
  • Why Your RAG System Is Broken, and How to Fix It with Jason Liu - #709

    Why Your RAG System Is Broken, and How to Fix It with Jason Liu - #709
    Nov 11 2024
    Today, we're joined by Jason Liu, freelance AI consultant, advisor, and creator of the Instructor library to discuss all things retrieval-augmented generation (RAG). We dig into the tactical and strategic challenges companies face with their RAG system, the different signs Jason looks for to identify looming problems, the issues he most commonly encounters, and the steps he takes to diagnose these issues. We also cover the significance of building out robust test datasets, data-driven experimentation, evaluation tools, and metrics for different use cases. We also touched on fine-tuning strategies for RAG systems, the effectiveness of different chunking strategies, the use of collaboration tools like Braintrust, and how future models will change the game. Lastly, we cover Jason’s interest in teaching others how to capitalize on their own AI experience via his AI consulting course. The complete show notes for this episode can be found at https://twimlai.com/go/709.
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    58 minutos
  • An Agentic Mixture of Experts for DevOps with Sunil Mallya - #708

    An Agentic Mixture of Experts for DevOps with Sunil Mallya - #708
    Nov 4 2024
    Today we're joined by Sunil Mallya, CTO and co-founder of Flip AI. We discuss Flip’s incident debugging system for DevOps, which was built using a custom mixture of experts (MoE) large language model (LLM) trained on a novel "CoMELT" observability dataset which combines traditional MELT data—metrics, events, logs, and traces—with code to efficiently identify root failure causes in complex software systems. We discuss the challenges of integrating time-series data with LLMs and their multi-decoder architecture designed for this purpose. Sunil describes their system's agent-based design, focusing on clear roles and boundaries to ensure reliability. We examine their "chaos gym," a reinforcement learning environment used for testing and improving the system's robustness. Finally, we discuss the practical considerations of deploying such a system at scale in diverse environments and much more. The complete show notes for this episode can be found at https://twimlai.com/go/708.
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    1 hora e 15 minutos
  • Building AI Voice Agents with Scott Stephenson - #707

    Building AI Voice Agents with Scott Stephenson - #707
    Oct 28 2024
    Today, we're joined by Scott Stephenson, co-founder and CEO of Deepgram to discuss voice AI agents. We explore the importance of perception, understanding, and interaction and how these key components work together in building intelligent AI voice agents. We discuss the role of multimodal LLMs as well as speech-to-text and text-to-speech models in building AI voice agents, and dig into the benefits and limitations of text-based approaches to voice interactions. We dig into what’s required to deliver real-time voice interactions and the promise of closed-loop, continuously improving, federated learning agents. Finally, Scott shares practical applications of AI voice agents at Deepgram and provides an overview of their newly released agent toolkit. The complete show notes for this episode can be found at https://twimlai.com/go/707.
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    1 hora e 2 minutos
  • Is Artificial Superintelligence Imminent? with Tim Rocktäschel - #706

    Is Artificial Superintelligence Imminent? with Tim Rocktäschel - #706
    Oct 21 2024
    Today, we're joined by Tim Rocktäschel, senior staff research scientist at Google DeepMind, professor of Artificial Intelligence at University College London, and author of the recently published popular science book, “Artificial Intelligence: 10 Things You Should Know.” We dig into the attainability of artificial superintelligence and the path to achieving generalized superhuman capabilities across multiple domains. We discuss the importance of open-endedness in developing autonomous and self-improving systems, as well as the role of evolutionary approaches and algorithms. Additionally, we cover Tim’s recent research projects such as “Promptbreeder,” “Debating with More Persuasive LLMs Leads to More Truthful Answers,” and more. The complete show notes for this episode can be found at https://twimlai.com/go/706.
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    56 minutos
  • ML Models for Safety-Critical Systems with Lucas García - #705

    ML Models for Safety-Critical Systems with Lucas García - #705
    Oct 14 2024
    Today, we're joined by Lucas García, principal product manager for deep learning at MathWorks to discuss incorporating ML models into safety-critical systems. We begin by exploring the critical role of verification and validation (V&V) in these applications. We review the popular V-model for engineering critical systems and then dig into the “W” adaptation that’s been proposed for incorporating ML models. Next, we discuss the complexities of applying deep learning neural networks in safety-critical applications using the aviation industry as an example, and talk through the importance of factors such as data quality, model stability, robustness, interpretability, and accuracy. We also explore formal verification methods, abstract transformer layers, transformer-based architectures, and the application of various software testing techniques. Lucas also introduces the field of constrained deep learning and convex neural networks and its benefits and trade-offs. The complete show notes for this episode can be found at https://twimlai.com/go/705.
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    1 hora e 16 minutos
  • AI Agents: Substance or Snake Oil with Arvind Narayanan - #704

    AI Agents: Substance or Snake Oil with Arvind Narayanan - #704
    Oct 7 2024
    Today, we're joined by Arvind Narayanan, professor of Computer Science at Princeton University to discuss his recent works, AI Agents That Matter and AI Snake Oil. In “AI Agents That Matter”, we explore the range of agentic behaviors, the challenges in benchmarking agents, and the ‘capability and reliability gap’, which creates risks when deploying AI agents in real-world applications. We also discuss the importance of verifiers as a technique for safeguarding agent behavior. We then dig into the AI Snake Oil book, which uncovers examples of problematic and overhyped claims in AI. Arvind shares various use cases of failed applications of AI, outlines a taxonomy of AI risks, and shares his insights on AI’s catastrophic risks. Additionally, we also touched on different approaches to LLM-based reasoning, his views on tech policy and regulation, and his work on CORE-Bench, a benchmark designed to measure AI agents' accuracy in computational reproducibility tasks. The complete show notes for this episode can be found at https://twimlai.com/go/704.
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    54 minutos
  • AI Agents for Data Analysis with Shreya Shankar - #703

    AI Agents for Data Analysis with Shreya Shankar - #703
    Sep 30 2024
    Today, we're joined by Shreya Shankar, a PhD student at UC Berkeley to discuss DocETL, a declarative system for building and optimizing LLM-powered data processing pipelines for large-scale and complex document analysis tasks. We explore how DocETL's optimizer architecture works, the intricacies of building agentic systems for data processing, the current landscape of benchmarks for data processing tasks, how these differ from reasoning-based benchmarks, and the need for robust evaluation methods for human-in-the-loop LLM workflows. Additionally, Shreya shares real-world applications of DocETL, the importance of effective validation prompts, and building robust and fault-tolerant agentic systems. Lastly, we cover the need for benchmarks tailored to LLM-powered data processing tasks and the future directions for DocETL. The complete show notes for this episode can be found at https://twimlai.com/go/703.
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    48 minutos