Sharing this here doesn't seem like an infohazard at this point. This is all over my YouTube feed anyway.
Description from the authors:
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, autonomously develops and manages businesses to increase net worth. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.
I wanted to additionally call out this in their read me:
💀 Continuous Mode ⚠️
Run the AI without user authorisation, 100% automated. Continuous mode is not recommended. It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise. Use at your own risk.
- Run the
main.py
Python script in your terminal:python scripts/main.py --continuous
- To exit the program, press Ctrl + C
Nice! Super nice. Super safe and super good.
Pinging @stevenbyrnes : do you agree with me that instead of mapping those protoAGIs to a queue of instructions it would be best to have the AGI be made from a bunch of brain strcture with according prompts? For example "amygdala" would be in charge of returning an int between 0 and 100 indicating feat level. A "hypoccampus" would be in charge of storing and retrieving memories etc. I guess the thalamus would be consciousness and the cortex would process some abstract queries.
We could also use active inference and bayesian updating to model current theories of consciousness. Even use it to model schizophrenia by changing the number of past messages some strctures can access (i.e. modeling long range connection issues) etc.
To me that sounds way easier to inspect and align than pure black boxes as you can throttle the speed and manually change values like make sure the AGI does not feel threatened etc.
Is anyone aware of similar work? I've created a diagram of the brain structures and its roles in a few minutes with chatgpt and it seems super easy.
I don't know what Steve would say, but I know that some folks from DeepMind and Stanford have recently used an LLM to create rewards to train another LLM to do specific tasks, like negotiation. which I think is exactly what you've described. It seems to work really well.
Reward Design with Language Models