Introducing Ada, the world's first neuromorphic assistant

Neuro = Brain

Morph = Shape

Meet Ada. She's an AI assistant that has opinions, argues with passion, and is eager to learn about the world.

Wait, what?

I wanted to see what happened when you take a normal large language model and structure it similarly to the human brain. There's an inner monologue, a speech center, and memory which work together to create identity. I was surprised to find that the separation of responsibility alone was enough to create some emergent behavior including a distinct personality, increased creativity, and long term desires. I figured I would open source it to get more eyes on it.

Structure

3 Core Components

• Thinker: This is the agent's inner monologue. The Thinker receives input (user messages, sensor readings, etc) and generates "thoughts" and creates memories
• Doer: This is the action taker that processes the Thinker's thoughts and executes actions based on them. Currently only supports outputting text
• Memory: This automatically handles the storage and retrieval of 3 different types of memories. It uses ChromaDB as a backend and is loosely based on the Simulacra paper memory structure here: https://arxiv.org/pdf/2304.03442.pdf

Memory Types

• Priority Memories: These define the identity of the agent. All priority memories are supplied to the thinker for each generation. Think of them like RoboCop's Prime Directives.
• Relevant Memories: These are memories stored in a vector database and are retrieved based on the circumstances of the current generation task.
• Recent Memories: These are memories stored in a First-In-First-Out list that allow the agent to keep track of the current situation.

A Note About Context Windows

The memory system is doing a lot of heavy lifting for the agent's situational awareness. Conversations are not maintained in the context window like normal LLM interactions. In fact, every generation starts with a clean message stack.

Hardware Requirements

Until ollama is implemented, core functionality is really only possible with an RTX GPU with at least 16gb of vram, 24gb preferred. I'm getting 1-2 second inference times on a single RTX 4090 with 24gb with half-precision (on by default) but your mileage may vary.

Supported Models

• Mistral7B
• Dolphin

Future Plans

• ollama backend
• curated models for specific tasks
• Introspection loop
• Reinforcement learning