> in the not so distant future AIs will make all economically relevant decisions
I don't doubt that, just doubt that they will make unanimously good decisions, as expected of a superhuman system. AI systems are yet to be "intelligent" which makes half of their name a PR gag.
Pattern recognition is simply not equivalent to intelligence. It is certainly a pillar of intelligence, however.
> AI systems are better than humans at making decisions
Are they? Dermatology, transportation, face recognition, NLP... in all these fields we are yet to reach human-like performance outside of ultra-specific tasks. There is a difference between recognizing a human face every time, even if obscured by sunglasses or half hidden behind a pint of beer, and recognizing 100.000 human faces per second with an error rate of 8% because of shadows.
Decent performance can be reached when you train AI to do one hyper-specific thing under just the right circumstances with mountains of data which need to be prepared just right. Otherwise you end up with bias and other issues. I wonder how AlphaGo would have performed if it was required to suddenly switch to Mahjong or cooking but I suppose that wasn't its purpose.
Please also mail me a link for systems that prepare training data to automatically and reliably prevent bias and other major issues that lead to malperforming systems.
Demonstrations optimized for PR, AI companies which employ humans as AI-pretenders, and catastrophically failing systems are the reality of AI today. I don't doubt that we will improve but superhuman AI requires more than mere linear improvement from where we are right now.
I don't doubt that, just doubt that they will make unanimously good decisions, as expected of a superhuman system. AI systems are yet to be "intelligent" which makes half of their name a PR gag.
Pattern recognition is simply not equivalent to intelligence. It is certainly a pillar of intelligence, however.
> AI systems are better than humans at making decisions
Are they? Dermatology, transportation, face recognition, NLP... in all these fields we are yet to reach human-like performance outside of ultra-specific tasks. There is a difference between recognizing a human face every time, even if obscured by sunglasses or half hidden behind a pint of beer, and recognizing 100.000 human faces per second with an error rate of 8% because of shadows.
Decent performance can be reached when you train AI to do one hyper-specific thing under just the right circumstances with mountains of data which need to be prepared just right. Otherwise you end up with bias and other issues. I wonder how AlphaGo would have performed if it was required to suddenly switch to Mahjong or cooking but I suppose that wasn't its purpose.
Please also mail me a link for systems that prepare training data to automatically and reliably prevent bias and other major issues that lead to malperforming systems.
Demonstrations optimized for PR, AI companies which employ humans as AI-pretenders, and catastrophically failing systems are the reality of AI today. I don't doubt that we will improve but superhuman AI requires more than mere linear improvement from where we are right now.