Algorithm Definition:
A sequence of actions that yields a result.
Use-case Definition:
A sequence of actions that yields a result of value to a user.
The difference between the two is 'of value to a user'. To me, the line between algorithm and use-case is the line between unconsciousness and consciousness. That line pivots around the ability to 'value' something. I doubt we will ever see the I in AI until we build something that can value a result in the same way that you and I do.
We need new words to describe what machines do. Using 'learn' or 'understand' seems like anthropomorphism. It's weird that we glibly anthropomorphise when talking about machines but prohibit it when talking about living things. Almost all of our qualities have been inherited from other living things. It seems to me that we should always anthropomorphise when talking about living things and never anthropomorphise when talking about machines. And yet, we always seem to do the opposite.
Until we can explain how a machine can 'value' something in the same sense that humans do (or chimps or ducks or caterpillars do), we should avoid anthropomorphic words like 'learn' and 'understand' as it misdirects our efforts. However, I have no idea what else to suggest other than to try to explain how a machine can 'value' something.
> IBM’s Watson was found to propose “multiple examples of unsafe and incorrect treatment recommendations.”
That won't stop until Watson has the ability to 'value' the result of what it is doing. Watson 'does' algorithms. Watson needs to 'do' use-cases. Once it can 'value' a result in the same way we do, it will correct its mistakes.