LLM Mindset Myth #4: The BIGGER the AI, the SMARTER IT IS

An ongoing dimension of the ZeroWidth project is for us to try and serve as a weed whacker to the fast-proliferating thicket of hype and falsehood that surrounds AI. One of these kill-your-darlings fallacies is the prevailing fiction (not idiocy, nobody said “idiocy”) that where AI is concerned, bigger is automatically better; that hugeness is somehow a virtue in its own right. The field seems to have fallen into lockstep beneath the Banner of Giganticness For Its Own Sake with regard to AI, a tendency that’s no doubt driven and intensified by the headline-hungry profiteers of the venture capital world, who seem daily to summon the tech media with an arms race of click-baity claims about immensity of scale.

But for you - the individual person or organization attempting to complete your day’s tasks - the vastness of a system’s data scale is at best conceptually impressive, but is not inherently impactful on the work you’re seeking to complete. But while an aircraft carrier is an indisputably impressive piece of hardware, it is not well suited to every task and cannot be wedged into every setting. To say nothing of the turning radius on one of those guys.

Because in order to determine if an AI platform or system is “better,” we need to arrive at some means of assessing how we mean that. Left unexamined, the notion of “smartness” is an obvious good; but for any AI-enabled agent to qualify as “smart,” it must meet some standard of applicableness, of aptness, of pertinence, of precision. And experiential, logistical considerations must figure in, as well: considerations like ease of use, how readily it is adapted to your purposes, how fully it lends itself to collaboration among teams whose members may have different objectives and approaches, different idiomatic vocabularies, differing levels of technical savvy. In short, if your team is trying to use a platform that’s frustrating to use, or that demands some esoteric level of expertise, or that yields inaccurate or unsuitable results, then the mindblowing size of the data set underlying it immediately ceases to matter to you.

We all have an intuitive grasp of the reality that “big” does not equate with “smart” - it can, certainly, but it’s not a given. It’s possible for example that Hulk Hogan has many fine qualities, but I don’t think any of us are inclined to list “prodigious intellect” among them - we’d probably have to push pretty far past “well-muscled” and “dauntingly tanned” and, I don’t know, “growly-voiced” to do so. But nobody’s bursting into the room going “Hulkster! We need you to recalibrate the particle accelerator! Stat!” And if for some reason we did, we’d rightly feel immediate remorse and mounting anxiety about it. And no amount of “You can count on me, brother!” from him could lessen our unease.

It’s the same with Large Language Model (LLM) AI systems - it’s like asking auto-fill to compose the Magna Carta. Even if the drop-down menu is effectively infinite, the menu has no volition, no contextual grasp, no ability to ascribe weight and priority. These functions fall to us - we must imbue such systems with discernment, with sound judgment, we must instruct them in the making of meaning.

Where AI is concerned, “smart” must be understood to mean how specifically applicable its interventions are - so vastness of data scale is no measure of effectiveness. Accurate, specific AI outputs hinge upon the training it receives - so applicable, well-curated data is of greater importance than quantity. AI is actually only as “smart” as the quality of teaching it receives from us. And if you’re saying “Well, but, I don’t know how to do that, though” - fear not. ZeroWidth has built a suite of variable, customizable, intuitive tools that can assist you in navigating this stuff.