What the abacus predicts for AI

While this post can stand alone, it builds on Part 1.

When our faces get too hot, we can vainly wave our hands to push air at them. It’s convenient when a fan replaces human-powered cooling; it’s even better when spinning fans advance beyond human ability. But it is transformative when a fan is strong and fast enough to be a helicopter.

Abilities that lie beyond some barrier of velocity are Speed-Locked Enablements.

Usually, a law of physics, an effect of human perception, or a combination thereof, brings about the unlock. A fast enough rhythm is a musical note. A rapid sanding system becomes a firestarter. A succession of pictures renders an animation.

Initially, scaling speed just scales the task. A shovel that is a thousand times stronger and faster, like a backhoe, just digs bigger and faster holes.

Consider the evolution of calculators, starting with the abacus. Let’s say an abacus can do five arithmetic operations per second. It’s a serious gain for most number-crunchers, but ultimately, they’re simply crunching numbers faster, hitting quotas or leaving work early. It’s an enhancement as opposed to an enablement. The first mechanical calculators were actually slower, but they teed up enablement speeds by replacing the human engine. Early computers, including electronic systems like the ENIAC of the 1940s, were essentially (albeit reductively) conveniences. They were time-savers and accuracy enforcers, and made tedious math problems, like flight trajectories, more palatable.

In 1951, MIT’s Whirlwind I computer broke the speed barrier to an enablement: it could simulate an aircraft’s trajectory in real-time. The transformation was not a result of a new core mechanism - all computing devices (excluding quantum) are just simple arithmetic operations on countable objects, be they marbles or charges. The transformation was the result of speed.

When the crunched numbers represent pixel colors, and the math is fast enough, we get Graphical User Interfaces. Go faster, and we can navigate spacecraft, simulate crashes, or render the sun reflecting off an orc’s swinging axe. All the parallelization, optimization, and specialized components (like GPUs) that enable digital experiences promote on the same simple arithmetic as the abacus; they just spin the math-wheel faster. Phones can do billions of bitwise operations per second, beefy computers can do trillions.

Which takes us to the LLM, the abacus of artificial reasoning. Like the abacus, it started at human-ish speeds, with words-per-second instead of calculations (“words” being a more friendly unit than “tokens”). We’d type or say some input and watch the AI write back at similar speeds, maybe 5-10 words per second - again, like the abacus. With initial speedups, we got faster chatbots. But what happens at a million words per second? A billion?

Can hallucinations be drowned out by instant quorum? What kinds of architectures will be built to parallelize, orchestrate cores, augment LLMs like the CPU? What abilities unlock when an AI has a trillion collaborative ideas per second, or a robot can ponder, “What should I do next?” after every micrometer movement and change in the wind? What if we could simulate the collective individual responses to a policy change or price hike or opening of park? What if technology was so responsive that it was designed in the instant at hand? What if each human’s need was like a pixel, and the AI was fast enough to solve them all in a way that formed a holistic picture?

GPT made this timeline of CPU speed and abilities

The Hypothetical Museum, Pt II (human-authored)

<- Part 1. The launch of the Era of Synthetic Reasoning had been fittingly momentous, by all accounts. And there were many accounts, as every imaginary soul in the Hypothetical Museum of Human Activity had gathered outside the brand new Room of Reasoning to witness the occasion. Old ceremonial texts had to be dusted off to do the thing properly; it had been a long, long time since a raw human ability like “digging” or “travel” had received its first tool. Many there had never considered that “reasoning” might exist, with any utility, outside an organic mind. That irony had been playfully worked into the presentation - books were borrowed from the Library of Everything and select passages read that more or less declared “the pure humanness of reason.”

The crowning moment was the Anointing of the Prime Ancestor - placing an artifact on the pedestal that marked the beginning of a new timeline of inventions - this first “Tool for Reasoning”. The Large Language Model had been chosen, represented by a sort of Platonic ideal LLM with just enough GPT-2 vibes to qualify as a corporeal tool. Of course, the choice of LLM was preceded and followed by much deliberation. Why not the more general concept of “AI”? Or the Transformer Architecture that made it work, or even the Deep Neural Networks that were surely the porting mechanism from biological reasoning to the synthetic version. But debates were always part of such occasions; seasoned curators remembered the tense conversations about which sharpened stick or rock counted as the “First Digging Tool”. A placard attempted to capture all the concepts, generally concluding that AI-related technologies had brought about an auspicious new thing to be endlessly used and improved. Further nitpicking was left to the Museum’s historians and philosophers.

Everyone else soon turned their attention to the new timeline itself, watching for the inevitable emergence of descendent creations. They were not disappointed. Daily, often multiple times per day, couriers rushed in models with multi-modal inputs, novel training mechanisms, attention-head modifications, parallelization, dedicated chips, even diffusion-based language models. The growth was so rapid that the Museum Architecture Team had to move up expansion plans by decades. (They defended original estimates by pointing out that, after Stairs and The Pulley were invented, the Methods of Human Ascent exhibit had served, almost unaltered, for centuries.)

Even more staggering was the proliferation of Supported Inventions. A nicety of the Museum’s hypothetical nature is that fanciful features are trivial to implement. Features like Support Tracers, glowing lines that emanate from artifacts, connecting to tangential, but dependent, inventions outside their strict lineage. For instance, the Internal Combustion Engine radiates many Support Tracers, one of which meanders through the Museum for a half kilometer before arriving at the 1997 Honda Civic.

Like the combustion engine, the LLM was soon tangled in a dense web of glowing Tracers, as startups and research groups rapidly built atop it. To better estimate growth rates, the Architecture Team looked for recent inventions with similar trajectories. The CPU was the obvious choice, already brightly pointing to the LLM with a thick Support Tracer. But unlike the LLM, the CPU itself was not a First Version; it was a clear descendant of the First Enhancers of Human Calculation, like the abacus.

The abacus was brilliant and useful and helped change industries, but it was not renowned as a catalyst for other inventions, as the LLM and CPU were. In some parts of the Non-Hypothetical World, it was still in use after more that 4,500 years. Like most First Enhancements, the abacus made work moderately easier, though there were still elite humans who could beat the machine-users. What made the Museum’s Architecture Team extremely nervous was the steady, predictable set of Support Tracers from the abacus family - accounting systems, currency conversion, land surveys - right up until the early 1950s. There, the massive, five-thousand vacuum tube Whirlwind I sat, with a disconcerting Support Tracer labeled “REALTIME defense systems”.

See also:

https://en.wikipedia.org/wiki/Beta_movement

Computing history

https://en.wikipedia.org/wiki/Timeline_of_computing_hardware_before_1950

https://en.wikipedia.org/wiki/History_of_general-purpose_CPUs

https://en.wikipedia.org/wiki/Mechanical_computer

https://en.wikipedia.org/wiki/History_of_computing_hardware

https://en.wikipedia.org/wiki/Abacus