Pre-trained systems dominate AI today (as deep as the P in GPT). My team and I have been researching and building alternatives given how the hallucination, blackbox and other problems don't seem solvable within* the current paradigm.
One of our first applications is a network automation solution for Netbox-- locally trained AI Agents unique to each Netbox account to predict roles of newly added devices given the current local list of devices, like a context-aware autocomplete.
Agents are lightweight by design, this particular Netbox Agent is 40-neuron, and when hooked up demo.netbox.dev and consistently gets 80%+ accuracy predicting device roles even when trained on ~60 devices only.
We'd keen for feedback, if this is useful, how we could extend it if it is, or if it sparks other application ideas.
* Somebody has to be the pre-trainer, leaving an irreducible gap of misunderstanding between AI and its application which we are trying to diminish by adding a layer of local training.
One of our first applications is a network automation solution for Netbox-- locally trained AI Agents unique to each Netbox account to predict roles of newly added devices given the current local list of devices, like a context-aware autocomplete.
Agents are lightweight by design, this particular Netbox Agent is 40-neuron, and when hooked up demo.netbox.dev and consistently gets 80%+ accuracy predicting device roles even when trained on ~60 devices only.
Try it out: https://aolabs-netbox.streamlit.app/
You can use dummy data from demo.netbox.dev. More on Netbox: https://github.com/netbox-community/netbox
We'd keen for feedback, if this is useful, how we could extend it if it is, or if it sparks other application ideas.
* Somebody has to be the pre-trainer, leaving an irreducible gap of misunderstanding between AI and its application which we are trying to diminish by adding a layer of local training.