Rereading the OP here, I think my interpretation of that sentence is different from yours. I read it as meaning “they’ll be trialed just beyond their area of reliable competence, and the appearance of incompetence that results from that will both linger and be interpreted as a general feeling that they’re incompetent, which in the public mood overpowers the quieter competence even if the models don’t continue to be used for those tasks and even if they’re being put to productive use for something else”.
(The amount of “let’s laugh at the language model for its terrible chess skills and conclude that AI is all a sham” already…)
I am really thinking that they’ll be deployed beyond their areas of reliable competence. If it can do even 50% of the work it might be worth it. As that goes up, it doesn’t need to be nearly 100% competent. I guess a factor I didn’t mention is that the rates of alarming mistakes should be far higher in deployment than testing, because the real world throws lots of curve balls it’s hard to come up with in training and testing.
And I think the downsides of AI incompetence will not fall on mostly on the businesses that deploy them, but on the AI itself. Which isn’t right, but it’s helpful for people blaming and fearing AI.
Rereading the OP here, I think my interpretation of that sentence is different from yours. I read it as meaning “they’ll be trialed just beyond their area of reliable competence, and the appearance of incompetence that results from that will both linger and be interpreted as a general feeling that they’re incompetent, which in the public mood overpowers the quieter competence even if the models don’t continue to be used for those tasks and even if they’re being put to productive use for something else”.
(The amount of “let’s laugh at the language model for its terrible chess skills and conclude that AI is all a sham” already…)
I am really thinking that they’ll be deployed beyond their areas of reliable competence. If it can do even 50% of the work it might be worth it. As that goes up, it doesn’t need to be nearly 100% competent. I guess a factor I didn’t mention is that the rates of alarming mistakes should be far higher in deployment than testing, because the real world throws lots of curve balls it’s hard to come up with in training and testing.
And I think the downsides of AI incompetence will not fall on mostly on the businesses that deploy them, but on the AI itself. Which isn’t right, but it’s helpful for people blaming and fearing AI.