Re:Sergey Brin warns of AI threats through a 'technology ren...
by cm2187 on Monday April 30, 2018 @10:47AM
(#16958940)
Good list. I would add:
- AI relies on data being available. For many professions there is simply no data to train an algorithm on, even when this would be a good fit for AI. An example I have in mind is skin disease diagnosis. We should start having generalist and dermatologists build a database of pictures and metadata of all the patients they see, even (and mostly) those that have nothing but just wanted to double check some suspicious spot. The only sort of database they have right now is collections of photos of the most pathological cases, but without the benign photos they are pretty much useless to build an automated AI diagnosis. Building this database might run against the incentive of the profession to be automated.
- AI is only economically viable above a minimum scale. The AI algorithm (and associated system) will not write or maintain itself. So you need to pay a team of software engineers to do that. That may or may not be more economical than paying a few people to do the job. We already hit that limit with software today. There is a lot of mundane office tasks that should have already been replaced with software but haven't.
- this one will be less popular here but I think is an important ceiling. There are many fields that software developers are completely uninterested in, and where the underlying business people are not technical enough to develop their own software. Certain areas of banking (outside of markets and retail), complex accounting rules, law, etc. The domain needs to be accessible to developers and that will make many professions very hard to automate even when they should. And the businesses may have no incentive to push for automation themselves (lawyers charge by the hour, they don't want more efficiency!).
- AI relies on data being available. For many professions there is simply no data to train an algorithm on, even when this would be a good fit for AI. An example I have in mind is skin disease diagnosis. We should start having generalist and dermatologists build a database of pictures and metadata of all the patients they see, even (and mostly) those that have nothing but just wanted to double check some suspicious spot. The only sort of database they have right now is collections of photos of the most pathological cases, but without the benign photos they are pretty much useless to build an automated AI diagnosis. Building this database might run against the incentive of the profession to be automated.
- AI is only economically viable above a minimum scale. The AI algorithm (and associated system) will not write or maintain itself. So you need to pay a team of software engineers to do that. That may or may not be more economical than paying a few people to do the job. We already hit that limit with software today. There is a lot of mundane office tasks that should have already been replaced with software but haven't.
- this one will be less popular here but I think is an important ceiling. There are many fields that software developers are completely uninterested in, and where the underlying business people are not technical enough to develop their own software. Certain areas of banking (outside of markets and retail), complex accounting rules, law, etc. The domain needs to be accessible to developers and that will make many professions very hard to automate even when they should. And the businesses may have no incentive to push for automation themselves (lawyers charge by the hour, they don't want more efficiency!).