Template:M intro work Large Learning Model: Difference between revisions

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 This is, of course, how all “mind-reading” works: by asking the right kinds of question, the conjurer extracts from the subject all the information needed to create the illusion of telepathy.
 =====LLMs as conjuring tricks=====
-''[[LLM]]s work the same way''. Like all good conjuring tricks, [[generative AI]] relies on misdirection: its singular genius is that it lets us misdirect ''ourselves'', into wilfully suspending disbelief, never noticing that it is ''we'' who are doing the creative heavy lifting to turn machine-made screed into magic. We are neuro-linguistically programming ''ourselves'' to be wowed by LLMs when the clever part is really happening inside our own heads.
+''[[LLM]]s work the same way''.
-By writing prompts, we create an expectation of what we will see. When the pattern-matching machine produces something roughly like that, we use our own imaginations to backfill, frame, filter, correct, boost, render, sharpen and polish the output into what we ''wanted'' to see. We ''construe'' the output to conform to our original instructions.
+Like all good conjuring tricks, [[generative AI]] relies on misdirection: its singular genius is that it lets us misdirect ''ourselves''. We wilfully suspend disbelief, never noticing who is creatively re-rendering machine-made screed as magic. ''We are''. We are neuro-linguistically programming ''ourselves'' to be wowed by a {{plainlink|https://en.wikipedia.org/wiki/Digesting_Duck|mechanical duck}}, when the clever part is really happening inside our own heads. Humans are seldom stupider than when they misattributing their own genius to a box of gears and pulleys.
+By writing “prompts”, we create an expectation of what we will see. When the pattern-matching machine produces something roughly like that, we use our own imaginations to backfill, frame, filter, correct, boost, render, sharpen and polish the output into what we ''wanted'' to see. We ''construe'' the output to conform to our original instructions.
 When we say, “fetch me a tennis racquet”, and the machine comes back with something more like a lacrosse stick, we are far more impressed than we would be of a human who did the same thing: we would think such a human a bit ''dim''. But with [[generative AI]] we don’t, at first, even notice we are not getting what we asked for. We might think, “oh, nice try!” or “well, that will do,” or perhaps, “ok, computer: try again, but make the basket bigger, the handle shorter, and tighten up the net.” We can iterate this way until we have what we want — though note all the refining intelligence is coming from ''us'' — or we could just Google for a conventional photo of a tennis racquet. As the novelty wears off, that is what more and more people will do.
-One of the more fantastic claims for Google glass was that it could read subtle clues in facial expressions to detect underlying emotional state of the subject:
+But todays technologists would have us believe that anything we frail meatsacks can do pales against the might of the magic box. We must resist being suckered by this disposition. For example, one of the more fantastic claims for Google glass was that it could read facial expressions to detect underlying emotional states:
 {{quote|The app detects emotion in the faces it sees using an algorithm generated by a machine learning system. This AI system was trained on large datasets of faces to decode the emotions from facial expressions.<ref>[https://www.cbc.ca/radio/quirks/march-30-erasing-memories-biggest-t-rex-and-the-smell-of-parkinson-s-and-more-1.5075050/google-glasses-could-help-kids-with-autism-read-emotional-cues-in-people-s-faces-1.5075055 As reported on CNBC]</ref>}}
-''But humans don’t need AI goggles to read each others’ emotions''. They are really good at that — far better than any [[algorithm]] — already. Even dogs can read facial expressions.<ref>[https://www.rspca.org.uk/-/blog_how_dogs_know_what_were_feeling#:~:text=In%20recent%20times%2C%20research%20has,human%20emotions%20by%20smell%20alone. Dogs know what we are feeling], RSPCA.</ref>
+Sounds amazing, huh? But ''humans don’t need AI goggles to read each others’ emotions''. They do it naturally. They are really good at it — far better than any [[algorithm]] — already. Even ''dogs'' can read facial expressions.<ref>[https://www.rspca.org.uk/-/blog_how_dogs_know_what_were_feeling#:~:text=In%20recent%20times%2C%20research%20has,human%20emotions%20by%20smell%20alone. Dogs know what we are feeling], RSPCA.</ref>
-So it is with [[generative AI]]. First impressions can be stunning, but the second look reveals an absurdist symphony. AI image generation struggles with hands, eyes and logical three-dimensional architecture. It is just as true of text prompts: on closer inspection we see the countless minute logical ''cul-de-sacs'', bad guesses and ''non sequiturs'' from which a clever story miraculously [[Emergence|emerge]]s. (To be sure, many human authors write in logical ''cul-de-sacs'', bad guesses and ''non sequiturs'', but that is another story.) Again, where is the magic in this process? ''Inside the [[meatware]]''.
+Why are we aspiring to have machines do badly things we already do effortlessly? Who benefits from that?
-Either way, the novelty soon palls and, as we persevere, we begin to see more and more of the magician’s wires. The first couple of passes are great: they get from zero to 0.5 of what we wanted. But the marginal improvement in each following round diminishes, as the machine reaches asymptotically towards an upper capability in “producing what you had in mind”, which we estimate, unscientifically, as about 75% of it.
+So it is with [[generative AI]]. AI image generation struggles with hands, eyes and logical three-dimensional architecture. It is just as true of text prompts: on close inspection we see the countless logical ''cul-de-sacs'', bad guesses and ''non sequiturs'' from which a clever story miraculously [[Emergence|emerge]]s. (To be sure, many human authors write in logical ''cul-de-sacs'', bad guesses and ''non sequiturs'', but that is another story.) Again, where is the magic in this process? ''Inside the [[meatware]]''.
-As we go, we get a sense of how the model works: its familiar tropes, tics and consistent ways of doing things which are never quite what you have in mind. The piquant surprise at what it produces dampens at each go-round, eventually settling into an [[Entropy|entropic]] and vaguely dissatisfying quotidian.
+Either way, as we persevere, we see more and more of the magician’s wires. We sense how the model works: its familiar tropes, tics and consistent ways of doing things, which are never quite what you had in mind. The first couple of passes are great: they get from zero to 50% of what we wanted. But the marginal improvement in each following round diminishes. The piquant surprise at what the machine can produce dampens at each go-round, reaching asymptotically towards an upper capability, well short of 100%, in “producing what you had in mind”. Eventually we settling into an [[Entropy|entropic]] and vaguely dissatisfying quotidian. “Okay, it isn’t quite what I had in mind. but in the interests of time it will do.”
-Now, as [[generative AI]] improves — assuming it ''does'' improve: there are some indications it may not; see below — the threshold of its “expectation-meeting capability” may move towards 100% but will never quite get there. And, again, don’t underestimate how important the meatware is in that refining process: many of its improvements will be down to how we learn to better to frame our queries. “Prompt-engineering” becomes the real skill, rather than the dumb, parallel pattern-matching process that responds to it.
+Again, we subordinate our own interests to the machine’s. We accept a mediocre job we would not take from a human. We must be more exacting lest we let the machines become our overlords, not by conquest but our own pathetic surrender.
-Ours is the skill going in, and ours is the skill construing the output. The machine just does the boring bit. But this is what machines have ''always'' done: the bits that require strength, speed, reliability, and economy. ''Not ingenuity''. This is the basic proposition of mechanising. Humans have been mechanising things since someone invented the wheel.
+Ours is the skill going in, and ours is the skill construing the output. The machine does the boring bit:  the bit that machines have ''always'' done: applies uninspired strength, speed, reliability, and economy. ''Not ingenuity''.
 =====LLMs and literary theory=====