The Cisco AI Insights Podcast

Cisco Podcast Network

Curious about the latest in AI? Cisco’s podcast unpacks artificial intelligence, data science, and machine learning. Hosts Rafael Herrera and Sonia Marques welcome monthly guests, including AI PhDs and experts, better understand AI academic papers and discuss their impact in and out of Cisco. Whether you’re new to AI or an experienced pro, tune in for insights the technology shaping our future.

Episodes

  1. Jul 29

    EP. 7 - Building Better Memory for Smarter AI Agents

    This month on The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques are joined by Cisco software engineer João Costa to explore Andrej Karpathy’s GitHub Gist on the LLM wiki pattern concept. It imagines how Large Language Models can move beyond one-off chat sessions and help build a persistent, interconnected knowledge base that grows and improves over time. The conversation examines how an LLM wiki turns raw sources such as meeting transcripts, research papers, websites, and project notes into structured markdown pages with cross-links between related concepts, creating something closer to a living digital brain than a static document store. João explains how tools like Obsidian can provide a clear window into this knowledge graph, while the LLM acts as a scribe that summarizes, curates, connects, and updates information for personal productivity, software development, research, and collaborative work. The episode also highlights the importance of linting, the ongoing process of detecting contradictions, removing stale details, and reducing hallucinated information before it spreads through the wider knowledge base. A special thank you to Andrej Karpathy for developing and sharing his GitHub Gist. To explore the Gist yourself, visit this link: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

  2. Jun 30

    EP. 6 - How Real-World Messiness Impacts AI Agent Success

    In this episode of the Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques are joined by Cisco ML Engineer Paul Mutawe to explore the fascinating paper, "Measuring AI Ability to Complete Long Software Tasks," which introduces a novel time horizon metric to evaluate how autonomously AI agents can execute complex, multi-hour engineering projects. The discussion looks at the rapid evolution of these agents, highlighting the key finding that the fifty percent success time horizon is doubling every two hundred and seven days, while detailing how unbiased benchmarking environments like the Modular Public harness are used to evaluate frontier models alongside real-world complexities like the sixteen-item messiness factor, which significantly reduces agent success rates, and the critical need for human-in-the-loop oversight to combat context rot. A special thank you to the research team at Model Evaluation and Threat Research, who developed this paper. If you are interested in reading the paper yourself, please visit the link: https://arxiv.org/pdf/2503.14499

  3. May 29

    EP. 5 - Forecasting Mental Health with ML, Deep Learning and LLMs

    In this episode of The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques are joined by Cisco’s Technical Leader in Machine Learning Engineering Leticia Fernandes to explore the groundbreaking study, "A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting Using Smartphone Sensing Data," which evaluates how different AI architectures analyze complex smartphone behavioral data to predict future mental health states. The discussion delves into the intricacies of forecasting mental health changes using five years of data from the College Experience Sensing dataset, highlighting how deep learning models, particularly transformer architectures, outperform traditional machine learning and Large Language Models by effectively leveraging personalized user behavior to identify subtle anomalies that could signal declining mental health, while also addressing the challenges of data imbalance and the inherent limitations of LLMs in processing high-dimensional, non-textual temporal sequences. A special thank you to the researchers from The Singapore University of Technology and Design, that developed this month's paper. If you are interested in reading the paper yourself, please visit this link: https://arxiv.org/pdf/2601.03603

  4. Apr 29

    EP. 4 - Can AI Pick Stocks by Reading the News?

    In this month’s episode of The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques explore the evolving landscape of sentiment analysis with Cisco machine learning engineer Joan Rossello. The conversation centers on the research paper from Imperial College London, “FinDPO: Financial Sentiment Analysis for Algorithmic Trading Through Preference Optimization of LLMs,” which proposes a new way to train large language models to better interpret nuanced human language. By comparing output quality, this preference-based training improved generalization and helped models interpret complex financial language without relying on simple memorization. Additionally, the discussion explored how sentiment scores derived from model probabilities were used to rank companies based on the strength of their news coverage, yielding promising results. A special thank you to the team that developed this month's paper. If you are interested in reading the paper yourself, please visit this link: ⁠ https://arxiv.org/abs/2507.18417.

  5. Mar 26

    EP. 3 - Rethinking AI Performance Metrics

    In the latest episode of the Cisco AI Insights podcast, hosts Rafael Herrera and Sonia Marques are joined by Dr. Catarina Carvalho, a Cisco leader in machine learning engineering. Together, they unpack the complex academic paper " Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following," developed by researchers from the University of Maryland and the University of Waterloo.   As the industry moves toward more reliable multimodal models, traditional pass-or-fail evaluation is no longer sufficient. This paper introduces a hierarchical framework that uses "LLM-as-a-judge" to evaluate outputs across five distinct criteria: visual grounding, logical coherence, factuality, reflection, and conciseness. Dr. Carvalho guides the discussion through the nuances of this "judge of judges" approach, exploring why human alignment remains the gold standard even as we automate evaluation processes.   A special thank you to the teams at both The University of Waterloo and The University of Maryland, College Park, for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://arxiv.org/pdf/2511.21662.

  6. Feb 26

    EP. 2 - Unlocking Cost-Effective AI with Small Language Models

    In the latest episode of the Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques welcome Cisco AI operations engineer James Tidd for a discussion on the world of small language models (SLMs) and the evolution of efficient AI inference. Together, they unravel the complexities behind “Fast Inference from Transformers via Speculative Decoding,” a groundbreaking paper from Google that explores how smaller draft models can speed up large language model predictions while maintaining accuracy. James shares his hands-on experience experimenting with the technique, leveraging knowledge distillation and speculative execution. The trio also discusses the potential of this approach to optimize AI, reduce power consumption and costs, and help businesses of all sizes get more out of existing hardware. A special thank you to Google’s AI team for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: research.google/blog/looking-back…ulative-decoding/.

  7. Jan 21

    EP. 1 - Building Better AI Agents

    In this month’s episode of The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques welcome Giota Antonakaki, a Cisco leader in machine learning engineering, for an illuminating deep dive into a recent academic research shaping AI agent technology. Together, they unpack “The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs”, a paper challenging the common belief that scaling large language models (LLMs) results in only marginal gains.    The conversation explores fresh concepts like “long horizon” and “self-conditioning,” revealing how even small improvements in LLM step accuracy can dramatically boost performance in complex, multi-step tasks. Giota breaks down the difference between planning, reasoning, and execution in LLMs, and why reasoning-focused models outperform even the largest standard LLMs for long-running AI agents.  We also extend a special thank you to Akshit Sinha and his team of researchers for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://arxiv.org/abs/2509.09677.

  • 7 Episodes

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Curious about the latest in AI? Cisco’s podcast unpacks artificial intelligence, data science, and machine learning. Hosts Rafael Herrera and Sonia Marques welcome monthly guests, including AI PhDs and experts, better understand AI academic papers and discuss their impact in and out of Cisco. Whether you’re new to AI or an experienced pro, tune in for insights the technology shaping our future.

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