Humans have the drive to go about because of a body that has various demands. And with every doing, humans accumulate Ego which again helps their drive to learn, explore, do stuff or define their behaviour. Humans are self-learning systems. The rewards and penalty are also self-created. Unless general AI systems start to model the above which creates self-learning, they will be just specific systems with a tiny aspec…
You don't need to program Ego to program a drive to learn new things, just a procedure to calculate the expected value of new information from engaging in some candidate training activity.
Training AI to Do Everything in the Digital Universe
21–23 of 23 posts
Re: Training AI to Do Everything in the Digital Universe
#22Interesting read, thank you for sharing. The author summarizes the thesis of the OpenAI team to be that by exposing AIs to a variety of experiences, the AIs will learn flexible problem solving skills. I'm not convinced this is true. Without imparting upon the AI some mechanism for reasoning across experiences, (e.g. reasoning through analogy), the AI will simply be trained on many specific experiences. How does the A…
I just don't trust general, high level descriptions. People have been going at this goal for decades without much proveable success on general intelligence. So when I read generalities I think they're not any further, just still trying. But when someone gives you a gory technical breakdown of a subproblem, that's when you know you've got something and they're serious.
Re: Training AI to Do Everything in the Digital Universe
#23Interesting read, thank you for sharing. The author summarizes the thesis of the OpenAI team to be that by exposing AIs to a variety of experiences, the AIs will learn flexible problem solving skills. I'm not convinced this is true. Without imparting upon the AI some mechanism for reasoning across experiences, (e.g. reasoning through analogy), the AI will simply be trained on many specific experiences. How does the A…
I just don't trust general, high level descriptions. People have been going at this goal for decades without much proveable success on general intelligence. So when I read generalities I think they're not any further, just still trying. But when someone gives you a gory technical breakdown of a subproblem, that's when you know you've got something and they're serious.
THIS DAY, artificial neural models calculate 10^14+ synaptic operations per second.
BY EXTENSION, artificial cognitive models have reduced larger number of cognitive tasks, as time diverged, in tandem with enhanced parallelism.
THUSLY, exascale artificial neural models are but likely by 2020. (Moore's Law)
It requires not Einsteinian intellect, such that one observes the prior sequence.