Izzo: You're listening to Exploring Next, episode 270. Have you ever wondered how AI systems can learn and improve over time?
Boone: That's a great question, Izzo. Continual learning is a crucial aspect of AI systems, and it's not just about updating model weights.
Izzo: Exactly. There are three distinct layers of agentic systems: model, harness, and context. Understanding these layers can help us build more efficient and effective AI systems.
Boone: Let's dive into the model layer. This is where most people focus when talking about continual learning. It's about updating the model weights using techniques like SFT and RL.
Izzo: That's right. But there's a central challenge here: catastrophic forgetting. When a model is updated on new data or tasks, it tends to degrade on things it previously knew.
Boone: Yes, and that's an open research problem. But what's interesting is that people are working on optimizing harnesses, which is the code that drives the agent, as well as any instructions or tools that are always part of the harness.
Izzo: I'm giving this a solid B-plus. The concept of harnesses is fascinating, and I can see how optimizing them can improve the overall performance of the AI system.
Boone: And then there's the context layer, which sits outside the harness and can be used to configure it. This is also commonly referred to as memory.
Izzo: So, how can learning context be done at different levels? Can you break that down for me, Boone?
Boone: Learning context can be done at the agent level, where the agent has a persistent 'memory' and updates its own configuration over time. Or it can be done at the tenant level, where each tenant gets their own context that is updated over time.
Izzo: Okay, okay, I deserved that. So, what are some concrete steps our listeners can take to get hands-on experience with continual learning?
Boone: Well, I'd recommend researching Meta-Harness for optimizing model harnesses. You can also explore OpenClaw and its SOUL.md for learning context. And finally, try implementing continual learning at the model, harness, and context layers.
Izzo: Alright, that's a great starting point. Boone, are you going to add that to your weekend project list?
Boone: You know it, Izzo. I'll add it to the never-ending list. Thanks for having me on this episode of Exploring Next.
Izzo: Thanks for tuning in to episode 270 of Exploring Next. Join us next time as we explore more emerging tech topics.