How to create self learning agents

In this episode, we explore how to build cost-effective, self-learning systems using open-source large language models like Llama 3.1. From fine-tuning models with specialized datasets to setting up efficient data pipelines and feedback mechanisms, we cover the essential components for creating adaptive AI systems.

Disclaimer: This episode - as any other of this podcast - is a result of a conversation between me and some LLM. I choose the topics based on my interests to help me gain insight and more clarity. I can’t give any warranty that every aspect is correct. LLMs can hallucinate or be trained with incorrect data.

We discuss strategies to reduce costs, like quantization and model parallelism, and explore platforms like RunPod for affordable GPU resources. We also touch on the exciting potential of peer-to-peer AI networks, such as Petals and HiveMind, which decentralize AI processing for broader accessibility.

Finally, we introduce Retrieval-Augmented Generation (RAG) as a way to enhance LLMs by connecting them to external data sources. Tools like LangChain and retrieval systems (e.g., ElasticSearch, FAISS, and vector databases) are highlighted for building smarter and more dynamic AI.

This episode is a practical guide for anyone interested in creating AI systems that learn, adapt, and evolve—without breaking the bank!