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

Writing is perhaps the greatest of human inventions, binding together people who never knew each other, citizens of distant epochs. Books break the shackles of time. A book is proof that humans are capable of working magic.”

Fittingly, the first [[chatbot]] was a designed as a parlour trick. In 1966 Joseph Weizenbaum, a computer scientist at MIT created “[[ELIZA]]” to explore communication between humans and machines. [[ELIZA]] used pattern matching and substitution techniques to generate realistic conversations. By today’s standards, [[ELIZA]] was rudimentary, simply regurgitating whatever was typed into it, reformatted as an open-ended statement or question, thereby inviting further input. As a session continued, the user’s answers became more specific and elaborate, allowing [[ELIZA]] to seem ever more perceptive in its responses.  

Even though [[ELIZA]] was a basic “keepy uppy” machine, it proved surprisingly addictive — even to those who knew how it worked. Weizenbaum was famously shocked how easily people, including his own secretary, were prepared to believe [[ELIZA]] “understood” them and contributed meaningfully to the interaction.  

[[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.

 

Yet again, we subordinate ourselves to suit the convenience of the machines. We really need to have some self-respect and kick this habit.

By writing prompts, we create our own expectation of what we will see. When the pattern-matching machine produces something roughly like it, we then use our own imaginations to backfill, frame, filter, correct, boost, sharpen and polish the output into what we ''want'' to see. We render that output as commensurate we can with 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 the 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, 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 — or we could just use a conventional photo of a tennis racquet.

AI-generated image generation famously struggles with hands, eyes and logical three-dimensional architecture. First impressions can be stunning, but the second look reveals an absurdist symphony. It is just as true of text prompts: on close inspection we can see the countless minute logical ''cul-de-sacs'' and two-footed hacks from which the clever story miraculously [[Emergence|emerge]]s. (To be sure, many human authors write in logical ''cul-de-sacs'' and two-footed hacks, but that is another story.)

Either way, the novelty soon palls and, as we persevere, we begin to see the magician’s wires. We get a sense of how the model goes about what it does: its familiar tropes and tics and persistent 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.  

In this way, the appeal of iterating a targeted work product with a random pattern-matcher soon loses its lustre. The first couple of passes are great: they get from zero to 0.5. But the marginal improvement in each following round diminishes, as the machine reaches asymptotically towards its upper capability in producing what you had in mind, which we estimate unscientifically as about 75% of it.  

Now, as [[generative AI]] improves towards 100 — assuming it does improve: there are some indications it may not; see below — that threshold may move but it will never get to 100. In the mean time, as each successive round takes more time and bears less fruit, mortal enthusiasm and patience with the LLM will have long-since waned: well before the [[Singularity]] arrives.

And many improvements we will see will largely be in the [[meatware]]: as we refine and elaborate our queries; we learn how better to frame our queries, and “prompt-engineering” becomes the skill, rather than the dumb, parallel pattern-matching process that responds to it. Ours is the skill going in, and ours is the skill construing the output. The machine just does the boring bit. But this is all machines have ''ever'' done. This is the basic proposition of mechanising.

So in all kinds of literature ''bar one'', “construal” is where the real magic happens. It is the [[Emergent|emergent]] creative act and community consensus that renders ''King Lear'' a timeless cultural leviathan and {{br|Dracula: The Undead}} forgettable pap.<ref>Maybe not ''that'' forgettable, come to think of it: it has stayed with me 15 years, after all.</ref> A literary work may start with the text, but it stays there barely a moment. The “meaning” of literature is necessarily personal to the reader: it lives between our ears, and within the cultural milieu that interconnects the reading population.<ref>Call me [[post-modern]] — go on, do — but I don’t hold with [[Carl Sagan]]’s idea that a book teleports its author “inside our heads”. That would be to equate reading with symbol-processing. It absolutely isn’t, and that metaphor gravely underestimates the human brain when in construction mode. </ref>

“Construal” and “construction” are interchangeable in this sense: over time that cultural milieu takes the received corpus of literature and, literally, ''constructs'' it into edifices its authors can have scarce have imagined. ''Hamlet'' speaks, still, to the social and human dilemmas of the twenty-first century in ways Shakespeare cannot possibly have contemplated.<ref>A bit ironic that Microsoft should call its chatbot “Bard”, of all things.</ref> to be clear: the reader of literature is no symbol processor, decrypting text to reveal a one-to-one scale assembly of the content of the author’s head. Literature is not an instruction manual.

Now there is one kind of “literature” where the last thing the writer wants is for the reader use her imagination to fill in holes in the meaning. Where clarity of authorial intention is paramount; where communicating and understanding ''purpose'' is the sole priority: ''legal'' literature.  

The ''last'' thing a legal drafter wants is to cede interpretative control to the reader. Rather, she seeks to squash all opportunities for improvisation presented by creative ambiguity. This is why, just as there are no atheists in foxholes, [[there are no metaphors in a trust deed|there are no metaphors in a Trust Deed]].  

Legal drafting seeks to be as [[finite]] as it can be. It strives do to readers what code does to hardware: to reduce them to mere symbol-processing machines, extracting the author’s single incontrovertible meaning. To leaves as little room as possible for interpretation. That there is such a living to be made conducting commercial litigation demonstrates how hard this is.

This is one reason why [[legalese]] is so laboured. It is designed to chase down all blind alleys, previsualise all phantoms and prescribe outcomes for all logical possibilities. To do so, it removes all ambiguity and renders the text as mechanical, precise and reliable as it can be. ''[[There are no metaphors in a trust deed]]''. Where normal literature favours possibility over certainty, legal language bestows [[certainty]] at the cost of [[possibility]], and to hell with literary style and elegance.

Legal language is, in [[James Carse]]’s sense, ''finite''. Literature is ''[[Finite and Infinite Games|infinite]]''.

Now: the punchline. Given how integral the reader and her cultural baggage are to the creative act in ''normal'' literature, we can see how, in that domain, a [[large learning model]], which spits out text ripe with possibilities, begging for someone to “construct” it, is a feasible model: to move from a model where ''most'' of the creative work is done by the reader to one where ''all'' of it is, is no great step.  

There is enough bad human literature like that out there now, that is is no great stretch to imagine doing without the human altogether. In that case, what does it matter what the text says, as long as it is coherent enough for an enterprising reader to make something out of it?  

''But that does not work at all at all for legal language''. Legal language is code: it must say exactly what the parties require: nothing more or less, and it must do it in a way that leaves nothing open to a later creative act of interpretation. We should regard legal drafting as closer to computer code than literature: a form of symbol processing where the meaning resides wholly within and is fully limited by the text.