AI with two brains - catastrophic forgetting solved

 

Brain 1 – General knowledge
A pretrained foundation model (e.g., Llama‑2, 7‑13 B) that already knows English, basic math, common‑sense facts, etc.
It stays static so it never loses the broad knowledge it was trained on.
Brain 2 – Domain‑specific, continuously refreshed
A lightweight component that learns from your daily 1 GB corpus.
It can be implemented as:
a retrieval index (FAISS/Chroma) that stores embeddings of the fresh documents, or
a fine‑tuned adapter (LoRA/QLoRA) that gets updated each night with the new data.
Because only this part changes, you avoid catastrophic forgetting in the general brain.

How they interact
A small router (a few‑shot classifier or a rule‑based switch) decides, for each query, whether to:

answer directly from Brain 1, or
pull the most relevant chunks from Brain 2 (retrieval) and feed them together with the question to Brain 1 for a grounded answer.

Practical stack you could use

General LLM – meta-llama/Llama-2-7b-chat (run locally via Ollama or through an API).
Domain adapter – Hugging Face PEFT (LoRA) trained nightly on the new 1 GB.
Retrieval – sentence-transformers/all-MiniLM-L6-v2 + Chroma/FAISS.
Orchestration – LangChain or LlamaIndex to glue the router, retrieval, and LLM together.

If you want a minimal end‑to‑end example (router + retrieval) I can sketch it out for you. Let me know which part you’d like to dive deeper into!

Small‑scale (edge / tiny)

Micro‑controller duo: a cheap MCU running a 1‑2 B‑parameter LLM (e.g., TinyLlama) paired with a local vector store (FAISS‑lite) on a Raspberry Pi Zero.
Smart‑phone combo: Android/iOS app that loads a 3‑B‑parameter model and uses an on‑device embedding DB (Chroma) for a specific hobby corpus.

Medium‑scale (on‑prem server)

GPU workstation: a 7‑13 B‑parameter foundation model (Llama‑2‑7B or Mistral‑7B) coupled with a dedicated retrieval index (FAISS or Chroma) that holds a 1 GB daily domain dump.
Hybrid cloud appliance: a containerised “two‑brain” stack (e.g., LangChain + Ollama) running on a single‑node DGX box, where one brain is the base LLM and the second is a fine‑tuned LoRA adapter refreshed nightly.

Large‑scale (cloud / cluster)

Multi‑region deployment: a massive foundation model (GPT‑4‑size or larger) paired with a sharded vector database (Pinecone, Weaviate) that ingests terabytes of fresh data each day; a router decides which brain answers.
Mixture‑of‑Experts (MoE) architecture: a “big brain” made of hundreds of specialist sub‑models (e.g., Switch‑Transformer‑1.6 T) plus a separate retrieval brain that pulls in domain‑specific snippets on demand.

generated by meta ai