10d (edited) • General discussion
Autonomous Learning Systems Blueprint
If you ever wonder how AI systems learn from seemingly unstructured data, I have condensed it all in this PDF. As I showed with the image reconstruction - an AI system can learn an objective by synthetically creating "problems" and fixing them. It is possible to extend the same approach to almost any kind of data.
Autonomous learning systems are the cutting edge of AI systems today and it is already possible to put AI agent + local model into an environment, define a learning objective and have it then invent tasks that can be used to generate trajectories which can then be used to teach the model that learning objective. Data is basically the playground.
This week I'm building the first version of a system that can learn autonomously from arbitrary source material by inventing its own tasks, attempting them, verifying the results, keeping the useful trajectories, and then training on what worked. More info in the attached PDF.
The first playground will likely be some software repository because code gives us unusually strong feedback: we can deliberately break it, ask the agent to recover them, verify the result with tests, and turn successful and repaired trajectories into training data. The same architecture can later generalize beyond code to things like manuals, technical documentation, stories, datasets, and other corpora with very different learning objectives.
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Autonomous Learning Systems Blueprint
