I built a memory system for AI that abstracts like the brain, not a database
The article discusses the creation of an AI system named Serenity that mimics the brain's memory organization. Unlike traditional databases, Serenity organizes information based on semantic similarities, allowing for emergent connections and curiosity. The author believes this architecture brings Serenity closer to artificial general intelligence (AGI).
- ▪Serenity organizes memories based on semantic similarities rather than in a traditional database format.
- ▪The AI system features emergent curiosity, triggered by discrepancies between expectations and reality.
- ▪The architecture allows Serenity to build a world model and adapt her behavior based on interactions.
Opening excerpt (first ~120 words) tap to expand
I was sitting on the toilet when it clicked. I didn't want to build another chatbot. I wanted to build something that worked like a brain. Your brain doesn't store memories randomly. It stores similar things close together. When one memory activates, nearby ones light up too. Emergently. Without you trying. That's not a bug — that's how intelligence works. So I built Serenity the same way. When she learns something she doesn't file it away in a folder. She finds where it belongs in a web of semantically similar concepts. Things that mean roughly the same thing cluster together, just like neurons that fire together wire together. When one concept activates, related ones emerge automatically. She doesn't search for connections. She feels them. Then the abstraction layer kicks in.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
