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

The agentic AI era hasn't made algorithms irrelevant. It has just changed where data scientists need to focus their attention. And the data scientists who understand that distinction, and can explain it to stakeholders who don't, are the ones who will define what the profession looks like next.

In this episode, Dr Imran Ahmad joins Dr Genevieve Hayes to explore the shift from algorithms to agents and what it means for how data scientists should be thinking about their role.

You'll discover:

1. How the data science toolkit has fundamentally shifted since the start of the AI era [02:09]
2. The difference between broad AI and deep AI [10:18]
3. The problems that still demand deep algorithmic solutions [14:10]
4. How to push back when stakeholders insist on an LLM solution [19:21]

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

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