[AJ007]

From the consumer's end, we expect computers to produce accurate calculations almost always. If a calculator produced the wrong answer to a basic mathematical function we would throw it out.

Humans are error prone, even when doing things they know and are good at.

Artificial intelligence is marketed as being a machine that is as smart as a human, but somehow we infer that because AI is a machine it will not make human mistakes. Mistakes are what produces learning.

The question becomes, do we only release AI for public use when it is assigned to a narrow range of problems and trained to 99.9% accuracy? Or does a consumer just throw AI at unknown, or even non trainable, problems and we take the result with a grain of salt? (Non trainable being something like predicting the value of the S&P 500 in 24 months.)

Perhaps a new words will be formed to describe AI, its behavior, accuracy, and experience? For now there is a lot of "one size fits all" and "holy grail" seeking. Big companies with armies of sales people seem to prefer this.

[TallGuyShort]

I think a search engine is the appropriate metaphor. Google's first result isn't always what you're looking for, but if you have no idea where to start, you can just type some words and Google will give you 10 pretty good ideas. You can then easily know if those 10 ideas are what you needed, or at least have a better idea of how to modify your query.

Watson, I think, will be the same way. Say for medical diagnoses - you don't just feed it observations and prescribe whatever it says. But if you want to ask about an unusual combinations of symptoms you've never encountered, it'll come back with something you can then go research. How you could make that connection before systems like Watson or Google (or similar medically-focussed systems if they exist) is beyond me - but they'll probably never replace the doctor's judgement. They're just a tool and should be treated that way.

[eru]

Watson can even come back with a suggestion of further tests to do.