I think the focus should be on providing LLM models an identify layer via a Harness which encodes the data they need to develop their own identify over time, which becomes tailored to the person or organization that they’re working with. Tailored not in the sense of learning your favorite color or being your best buddy, but modeling the problems your working on and figuring out how to solve them.
The part that makes an agent a collaborator should be the part you have full ownership of and as little dependency upon external services to construct and maintain. Make the model interchangeable, make the identify-layer and personal intelligence baked into your custom built harness.
We don’t need to try to make LLMs into clones of ourself; or even copies of capable humans. What we need are intelligent systems that align to helping humans achieve rational species-aligned objectives, and which learn and self-correct while pursuing those objectives. The focus on having the intelligence bounded to the models parameters, rather than encoded into a programmatic layer the model integrates with, is I think a critical error in thinking.
When viewing the cosmos via system-based thinking, particularly biological organisms, the structural organization of the those systems are not ones which try to have every function served by a central ‘all-thing’. We have a CNS, ANS, and ENS, a liver and a heart, muscle and fascia, blood and lymph, colonies of microbes; phases, cycles, and process upon process with interdependence.
Our intelligence is the end product of complex systems acting in unity, but those systems are separate systems layered upon each other, orchestrated by central drivers. The person is not their brain; system state is the person. We can encode intelligence into non-llm systems that llms integrate with. Systems that people control, that build around their work, that scope to and specialize to domain tasks. Not a single agent that ‘does it all’ but an aggregation of encoded intelligence that LLMs drive and interact with, just like in human physiology, but specialized in intelligence, not breathing, eating, or reproducing, but architected for intelligence.
That is what I’m already building and seeing meaningful results with.
I think the focus should be on providing LLM models an identify layer via a Harness which encodes the data they need to develop their own identify over time, which becomes tailored to the person or organization that they’re working with. Tailored not in the sense of learning your favorite color or being your best buddy, but modeling the problems your working on and figuring out how to solve them.
The part that makes an agent a collaborator should be the part you have full ownership of and as little dependency upon external services to construct and maintain. Make the model interchangeable, make the identify-layer and personal intelligence baked into your custom built harness.
We don’t need to try to make LLMs into clones of ourself; or even copies of capable humans. What we need are intelligent systems that align to helping humans achieve rational species-aligned objectives, and which learn and self-correct while pursuing those objectives. The focus on having the intelligence bounded to the models parameters, rather than encoded into a programmatic layer the model integrates with, is I think a critical error in thinking.
When viewing the cosmos via system-based thinking, particularly biological organisms, the structural organization of the those systems are not ones which try to have every function served by a central ‘all-thing’. We have a CNS, ANS, and ENS, a liver and a heart, muscle and fascia, blood and lymph, colonies of microbes; phases, cycles, and process upon process with interdependence.
Our intelligence is the end product of complex systems acting in unity, but those systems are separate systems layered upon each other, orchestrated by central drivers. The person is not their brain; system state is the person. We can encode intelligence into non-llm systems that llms integrate with. Systems that people control, that build around their work, that scope to and specialize to domain tasks. Not a single agent that ‘does it all’ but an aggregation of encoded intelligence that LLMs drive and interact with, just like in human physiology, but specialized in intelligence, not breathing, eating, or reproducing, but architected for intelligence.
That is what I’m already building and seeing meaningful results with.