Continual Learning for AI Agents
This article discusses the different layers of continual learning for AI agents - the model, the harness, and the context. Understanding these layers is crucial for building systems that can improve over time.
Why it matters
This article provides a more holistic view of continual learning for AI agents, which is important for developing systems that can continuously enhance their capabilities.
Key Points
- 1Continual learning in AI goes beyond just updating model weights
- 2Learning can happen at three distinct layers: the model, the harness, and the context
- 3Focusing on these three layers changes how we think about building systems that improve over time
Details
The article explains that most discussions around continual learning in AI focus solely on updating model weights. However, the author argues that for AI agents, learning can occur at three distinct layers: the model, the harness, and the context. The model layer refers to the neural network parameters that are updated through training. The harness layer encompasses the code and infrastructure that runs the model, which can also be improved over time. The context layer includes the external data, knowledge, and environment that the agent operates in, which can be expanded and refined as the agent learns. Understanding the differences between these three layers is crucial for building AI systems that can truly improve and adapt over time, rather than just updating their underlying models.
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