『Value Driven Data Science』のカバーアート

Value Driven Data Science

Value Driven Data Science

著者: Dr Genevieve Hayes
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Your stakeholders don’t want a better model. They want a better decision. Value Driven Data Science is a masterclass for data professionals ready to become strategic experts, valued for their judgement, not just the technical outputs they produce. Each week, Dr Genevieve Hayes speaks with leading thinkers and data practitioners who have already made that shift. You’ll leave every episode with an idea or framework you can start using right away. If you’re tired of being treated as just another data scientist, Value Driven Data Science will transform you into the trusted advisor your stakeholders ask for by name.© 2026 Genevieve Hayes Consulting 経済学
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  • Episode 128: [Value Boost] The Three Types of Memory Every AI Agent Needs
    2026/10/21

    One of the most frustrating things about working with AI is when it seems to suddenly forget everything it knew about you and your work. This is annoying when using AI chatbots. When building AI systems, it can be catastrophic.

    In this Value Boost episode, Dr Imran Ahmad joins Dr Genevieve Hayes to explain what memory actually means in AI agents and what data scientists need to understand to build systems that actually remember what matters.

    You'll discover:

    1. The three types of memory every AI agent needs [01:54]
    2. Why context and memory are not the same thing [05:37]
    3. What context rotting is and why it matters for how you design AI systems [09:15]
    4. How to make sure LLMs pay attention to what actually matters [10:39]

    Guest Bio

    Dr Imran Ahmad is a data scientist and president of data consultancy Cloudanum. He is also the author of 30 Agents Every AI Engineer Must Build and 50 Algorithms Every Programmer Should Know.

    Links

    • Connect with Imran on LinkedIn
    • Connect with Genevieve on LinkedIn
    • Be among the first to hear about the release of each new podcast episode by signing up HERE
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    13 分
  • Episode 126: Why AI Excels at Some Things and Sucks at Others
    2026/10/07

    Large language models excel at some tasks while struggling badly with other. At a time where it seems everyone is currently trying to make sense of AI, understanding and being able to explain exactly why this is the case, has become one of the most useful things data scientists can offer their stakeholders.

    In this special reverse interview episode, Lauren Pearl switches roles with Dr Genevieve Hayes to explore why LLMs work the way they do and what that means for anyone using AI in their work.

    You'll discover:
    1. What your training data tells you about what AI can and cannot do [05:05]
    2. Why high context problems are particularly hard for LLMs [06:28]
    3. How AI gives you the right answer for the wrong reason [08:08]
    4. Why some problems will always be beyond what LLMs can solve [14:22]

    Guest Bio

    Lauren Pearl is a business strategist, three-time founder and CFO advisor who helps start-up teams build data-driven businesses that thrive. She is also the resident start-up finance expert at NYU Stern’s Berkley Center for Entrepreneurship, where she teaches financial modelling to founders, and the co-host of The Growth-Minded CFO podcast.

    Links

    • Connect with Lauren on LinkedIn
    • Subscribe to The Daily CFO newsletter
    • Connect with Genevieve on LinkedIn
    • Be among the first to hear about the release of each new podcast episode by signing up HERE
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    21 分
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