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Cherry Ventures

Breaking down the latest developments in AI with two experts — Jasper Masemann, investment partner at Cherry Ventures, and Lutz Finger, a visiting senior lecturer at Cornell University's SC Johnson College of Business and CEO and Co-founder of R2Decide.

Lutz & Jasper powered by Cherry Ventures

This episode breaks down what agents really are, moving past the "Matrix guy with sunglasses" imagery. Lutz and Jasper explain how agents are pieces of code that change how we interact with computers, taking defined tasks and breaking them down step by step.

A key insight emerges: agents aren't entirely new. Agents build on concepts from Robotic Process Automation (RPA), but with crucial differences. Where RPA was limited to rigid rule-based decisions, agents can handle uncertainty and unstructured data.

The discussion takes an interesting turn when Lutz brings up Microsoft's Clippy - the infamous paperclip assistant. While Clippy was widely mocked, it represents an early attempt at what we're now trying to achieve with agents. The key difference? Modern agents are more sophisticated in how they can be guided and controlled.

Cherry Ventures share have insights from their investment portfolio, including:

 • Software testing companies using "self-healing" mechanisms
 • E-commerce applications that improve search and product discovery
 • Document analysis and processing systems

AI Agents: The Future of Work?

Lutz and Jasper jump into autonomous agents, looking at the possibilities and the limitations of these AI-powered task masters.

Connecting LLMs to the world: The possibilities of the feedback loop

Lutz and Jasper talk what's new in AI and make some predictions for the rest of the year. The big question at hand: Will open or closed AI models win?

Will open or closed AI models win?

Lutz and Jasper break down AI as interface.

AI as an interface

This week, Lutz and Jasper sit down with a special guest — Reetu Kainulainen, cofounder and CEO of Ultimate. For the past six years, he and his team have been exploring and using conversational AI to improve customer service. The team just launched UltimateGPT, which allows users to integrate ChatGPT into their support center.

How Ultimate evolved from hundreds of supervised models to UltimateGPT

In this week’s episode, our hosts debate the role — and larger influence — that AI and neural networks will have on music itself and the overarching music industry. How will music creation, ranging from ideation to composition to final production be impacted? What will happen to the so-called creative “human touch”? What about issues surrounding copyright or royalties? All this and much more.

The future is indie: AI’s impact on the music industry

In this week's episode, Lutz and Jasper breakdown the impact of bias in AI — and what it means for startups.

Bias in, bias out: Addressing the impact of bias in AI

This week, Jasper and Lutz go back to school. Speaking in front of Lutz's course at Cornell University's SC Johnson College of Business, Jasper and Lutz speak with Torben Gerkensmeyer, Head of Engineering at LegalOS around the power and potential of LLMs in the legal space plus in work overall.

Will LLMs scale and revolutionize the legal space?

What do the latest funding rounds tell us about the recent evolution of AI? This week, our hosts jump into AI funding landscape, looking primarily at three rounds to hit the headlines as of late. But, more than simply skimming the funding surface, they look at the different use cases behind the companies, continuing to explore the full range of AI's impact.

What the latest in AI funding tells us

AI is not totally absent of human work. So, what's the human factor in AI? Lutz and Jasper break it down, including a primer on supervised and unsupervised learning.

AI and the human factor

LLMs + vector search = RAG.In this episode, we introduce you to Retrieval-Augmented Generation (RAG) and how it might allow us to better and more easily implement LLMs in our day-to-day work. We believe current LLMs are best used as an interface, and if we want to make enterprise search useful, RAGs can be the solution.

Are we one step closer to using LLMs in practice?

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