Resistance is futile, but maybe not with artificial intelligence

25.03.2024 - 03:25 / tech.hindustantimes.com / Meta Ai

The novelist Neal Stephenson's prophetic power never ceases to astonish. The Diamond Age (1995), is set in a technologically highly advanced world, with ubiquitous nanotechnology in addition to something strangely familiar called “P.I.” The abbreviation is explained in the following exchange:

“I'm an engineer … Did some work on this project, as it happens.”

“What sort of work?”

“Oh, P.I. stuff mostly,” Hackworth said. Supposedly Finkle-McGraw still kept up with things and would recognize the abbreviation for pseudo-intelligence, and perhaps even appreciate that Hackworth had made this assumption.

Finkle-McGraw brightened a bit. “You know, when I was a lad they called it A.I. Artificial intelligence.”

Hackworth allowed himself a tight, narrow, and brief smile. “Well, there's something to be said for cheekiness, I suppose.”

Yet, for all their technological sophistication, these two men are “neo-Victorians.” They have chosen to embrace 19th-century manners and fashions partly as a defense against the collapse of state power and the fragmentation of nations into “phyles,” i.e., tribes.

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There is an important warning here for everyone giddy with the recent advances of generative AI. Breathtaking developments in the realm of technology do not render history obsolete. It lives on alongside the latest gadgetry, because the present is not where history ends and the future begins; it is where the past and the future fuse.

Since OpenAI released ChatGPT on Nov. 30, 2022, the world has been in an AI frenzy. Within just five days, ChatGPT had over one million users. Now there are 100 million. The company's latest triumph is Sora, which can churn out DreamWorks-quality animation in response to your most whimsical prompt. All of this is made possible by deep-learning systems with hundreds of billions of parameters, a term defined here. (To give an example, GPT-4's training model needs more than a trillion parameters.) And the amount of computation used to train state-of-the-art large language models (LLMs) is more than doubling every six months.

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Over time, this speed of increase in the size of the models must slow and converge with Moore's Law (see below), or the price of training new models will become exorbitant — out of any plausible relationship to the money that might be made from commercializing the technology. That means AI can advance only so long as computing power keeps getting exponentially cheaper.

I have six questions about the technological, financial and economic future of AI:

In 1965, Gordon Moore, the co-founder of Intel