So perhaps a “proto-AGI” is a better term to use for it. Not quite the full thing just yet, but shows clear generality across a wide number of domains. If it can spread out further and become much larger, as well as have recursivity (which might require an entirely different architecture), it could become what we’ve all been waiting for.
I would agree with “proto-AGI”. I might soon write a blog on this, but ideally we could define a continuous value to track how close we are to AGI, which is increasing if:
-the tasks to solve are very different from each other
-the tasks are complex
-how well a task have been solved
-few experience (or info) is fed to the system
-experience is not directly related to the task
-experience is very raw
-computation is done in few steps
Then adding new tasks and changing the environment.
I have always been cautios, but I would say yes this time.
With the caveat that it learns new tasks only from supervised data, and not reusing previous experience.
So perhaps a “proto-AGI” is a better term to use for it. Not quite the full thing just yet, but shows clear generality across a wide number of domains. If it can spread out further and become much larger, as well as have recursivity (which might require an entirely different architecture), it could become what we’ve all been waiting for.
I would agree with “proto-AGI”. I might soon write a blog on this, but ideally we could define a continuous value to track how close we are to AGI, which is increasing if:
-the tasks to solve are very different from each other
-the tasks are complex
-how well a task have been solved
-few experience (or info) is fed to the system
-experience is not directly related to the task
-experience is very raw
-computation is done in few steps
Then adding new tasks and changing the environment.