I still think that the lure of personal assistants who remember previous conversations in order to react appropriately
This is possible. When you open a new session, the task context includes the prior text log. However, the AI has not had weight adjustments directly from this one session, and there is no “global” counter that it increments for every “satisfied user” or some other heuristic. It’s not necessarily even the same model—all the context required to continue a session has to be in that “context” data structure, which must be all human readable, and other models can load the same context and do intelligent things to continue serving a user.
This is similar to how Google services are made of many stateless microservices, but they do handle user data which can be large.
I also think there are a lot of applications where designers don’t want reliability, exactly. The obvious example is AI art.
There are reliability metrics here also. To use AI art there are checkable truths. Is the dog eating ice cream (the prompt) or meat? Once you converge on an improvement to reliability, you don’t want to backslide. So you need a test bench, where one model generates images and another model checks them for correctness in satisfying the prompt, and it needs to be very large. And then after you get it to work you do not want the model leaving the CI pipeline to receive any edits—no on-line learning, no ‘state’ that causes it to process prompts differently.
It’s the same argument. Production software systems from the giants all have converged to this because it is correct. “janky” software you are familiar with usually belongs to poor companies, and I don’t think this is a coincidence.
I still expect that one component of that, for ‘typical’ agents, is power-seeking behavior. (Link points to a rather general argument that many models seek power, not dependent on overly abstract definitions of ‘agency’.)
Power seeking behavior likely comes from an outer goal, like “make more money”, aka a global state counter. If the system produces the same outputs in any order it is run, and has no “benefit” from the board state changing favorably (because it will often not even be the agent ‘seeing’ futures with a better board state, it will have been replaced with a different agent) this breaks.
I still think that the lure of personal assistants who remember previous conversations in order to react appropriately
This is possible. When you open a new session, the task context includes the prior text log. However, the AI has not had weight adjustments directly from this one session, and there is no “global” counter that it increments for every “satisfied user” or some other heuristic. It’s not necessarily even the same model—all the context required to continue a session has to be in that “context” data structure, which must be all human readable, and other models can load the same context and do intelligent things to continue serving a user.
This is similar to how Google services are made of many stateless microservices, but they do handle user data which can be large.
I also think there are a lot of applications where designers don’t want reliability, exactly. The obvious example is AI art.
There are reliability metrics here also. To use AI art there are checkable truths. Is the dog eating ice cream (the prompt) or meat? Once you converge on an improvement to reliability, you don’t want to backslide. So you need a test bench, where one model generates images and another model checks them for correctness in satisfying the prompt, and it needs to be very large. And then after you get it to work you do not want the model leaving the CI pipeline to receive any edits—no on-line learning, no ‘state’ that causes it to process prompts differently.
It’s the same argument. Production software systems from the giants all have converged to this because it is correct. “janky” software you are familiar with usually belongs to poor companies, and I don’t think this is a coincidence.
I still expect that one component of that, for ‘typical’ agents, is power-seeking behavior. (Link points to a rather general argument that many models seek power, not dependent on overly abstract definitions of ‘agency’.)
Power seeking behavior likely comes from an outer goal, like “make more money”, aka a global state counter. If the system produces the same outputs in any order it is run, and has no “benefit” from the board state changing favorably (because it will often not even be the agent ‘seeing’ futures with a better board state, it will have been replaced with a different agent) this breaks.