Template:M intro technology rumours of our demise: Difference between revisions

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→‎If AI is a cheapest-to-deliver strategy you are doing it wrong

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 {{D|Cheapest-to-deliver|/ˈʧiːpɪst tuː dɪˈlɪvə/|adj}}
-Of the range of possible ways of discharging a [[contract|contractual obligation]], the one that will cost you the least and irritate your customer the most should you choose it.}}
+Of the range of possible ways of discharging your [[contract|contractual obligation]] to the letter, the one that will cost you the least and irritate your customer the most should you choose it.}}
-Imagine a personal [[large language model]], private to a single client user — free, therefore, of data privacy concerns — that would pattern-match purely by reference to its client’s actual reading and listening history, prompts, instructions and to the recommendations of pattern-matched like-minded readers, which searched through the entire human creative ''oeuvre'' — the billions of books, plays, films, recordings and artworks, known and not, that already exist — and, instead of using them to generate random mashups, would be designed to return works of as yet undiscovered delight?
+Imagine we each had private [[large language model]]s at our personal disposal — free, therefore, of data privacy concerns — that could pattern-match by reference to our individual reading and listening histories, our engineered prompts, our instructions and the recommendations of like-minded readers. Our LLM would search through the entire human ''oeuvre'' — the billions of books, plays, films, recordings and artworks, known and not, that already exist, but instead of using that information to generate random mashups, it would return existing works from the canon of as yet undiscovered delight?
-''This is not just the Spotify recommendation algorithm'', as occasionally delightful as that is. Like any commercial algorithm, that has its own primary goal: revenue maximisation. “Client delight” may be a necessary by-product, but only as far as it intersects with that primary commercial goal. As long as clients are delighted ''enough'' to keep listening, the algorithm doesn’t care ''how'' delighted they are. As with the JC’s school exam grades: anything more than 51% is wasted effort.<ref>Try as he might, the JC  was never able to persuade his dear old ''Mutti'' about this.</ref>
+''This is not just the Spotify recommendation algorithm'', as occasionally delightful as that is. Like any commercial algorithm, that has its own primary goal: revenue maximisation. A certain amount of “customer delight” may be a necessary by-product, but only as far as it intersects with that primary commercial goal. As long as customers are just delighted ''enough'' to keep listening, the algorithm doesn’t care ''how'' delighted they are. (As with the JC’s school exam grades: anything more than 51% is wasted effort.<ref>Try as he might, the JC  was never able to persuade his dear old ''Mutti'' about this.</ref>)
 Commercial algorithms need only follow a ''[[cheapest to deliver]]'' strategy: they “[[satisfice]]”. Being targeted primarily at revenue optimisation, they will tend to converge upon what is likely to be popular, because that is easier to find. Rather than scanning the entire depth of human content, skim the top and keep the punters happy enough.