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

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 Okay, these require motor control and interaction with the irreducibly messy [[off-world|real world]], so there are practical barriers to progression. But other such facilities would not: imagine a machine that could search through all the billions of books, recordings and artworks that humanity has created, and surface the undiscovered genius, to give its [[Bayesian prior]]s a fighting chance?
-''This is not just the Spotify algorithm'', as occasionally delightful as it is. That has its own agenda: revenue maximisation for Spotify is its primary goal, and reader enlightenment is an occasional by-product, where the two intersect. That will tend to serve up populist mush: the reader’s personal “[[cheesecake for the brain]]”. Per {{author|Anita Elberse}}’s {{br|The Blockbuster Effect}}, commercial algorithms, targeted primarily at revenue optimisation, have had the counter-intuitive effect of ''truncating'' the “[[long tail]]” of consumer choice Wired’s Chris Anderson famously envisioned it. A sensible use for this technology would ''extend'' it.
+''This is not just the Spotify algorithm'', as occasionally delightful as it is. That has its own agenda: revenue maximisation for Spotify is its primary goal, and listener enlightenment is an occasional by-product, where the two intersect. But as long as you are satisfied enough to keep listening, it doesn't care. It is a ''[[cheapest to deliver]]'' strategy: it satisfices. It will tend to serve up populist mush: the reader’s personal “[[cheesecake for the brain]]”. Per {{author|Anita Elberse}}’s {{br|The Blockbuster Effect}}, commercial algorithms, targeted primarily at revenue optimisation, have had the counter-intuitive effect of ''truncating'' the “[[long tail]]” of consumer choice Wired’s Chris Anderson famously envisioned it. A sensible use for this technology would ''extend'' it.
-This would be a personal LLM, private to the user, free, therefore, of data privacy concerns, that would pattern-match purely by reference to the user’s actual reading habits, instructions and the recommendations of like-minded readers. This algorithm would be tasked with ''diversifying'' — finding undiscovered works — rather than ''converging'' — gravitating towards common, popular, commercially promoted ones.
+If artificial intelligence is so spectacular, shouldn’t we be a bit more ambitious in our expectations about what it could do? Isn't “giving you the bare minimum to keep stringing you along” just a little ''underwhelming''?
+Imagine a personal large language model, private to the user — free, therefore, of the usual data privacy concerns — that would pattern-match purely by reference to the it's single client’s actual reading habits, prompts and instructions and the recommendations of pattern-matched like-minded readers. This algorithm would be tasked with ''diversifying'' — finding undiscovered works the client might not otherwise find — and rather than ''converging'' — gravitating towards common, popular, commercially promoted ones, it would actively avoid them. .
 A rudimentary version of this exists in the LibraryThing recommendation engine, but the scope, with artificial intelligence is huge.