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 Even though [[ELIZA]] was a basic “keepy uppy” machine, it proved surprisingly addictive. Weizenbaum was famously shocked how easily people were prepared to believe [[ELIZA]] “understood” them and contributed meaningfully to the interaction. When trying it out, Weizenbaum’s own secretary, who had watched him build the programme for months and knew how it worked, shooed him out of the room so she could have privacy with the machine.
-This is, of course, how all “mind-reading” works: with the right kind of clever questions, the conjurer extracts from the subject herself all the information she needs to create the illusion.
+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 who is doing the creative heavy lifting needed to turn machine-made screed into magic: ''we are''. 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''. 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.
-By writing prompts, we create our own 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'' that output as commensurate we can with our original instructions.
+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 had a human done the same thing: we would think such a human were 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 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.
+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.
-First impressions can be stunning, but the second look reveals an absurdist symphony. AI image generation famously 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 the 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]]''.
+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:
+{{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>
+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]]''.
 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.