The current strategy of the AI hype machine is to exhaust people's reserves of attention by presenting a never-ending stream of hard-to-verify "positive" claims. It's Gish Gallop done on the Internet scale with a never-ending parade of tech influencers, proxy "journalists" and low-value accounts. The whole strategy aims for saturation and demoralized acceptance.
It's no surprise that people readjust their immediate reactions by expressing hostility and skepticism about anything AI-related without spending much time on analysis. In fact, it's an entirely rational repones.
Complaining about it without acknowledging the larger picture is disingenuous.
In this particular case, using the term "machine learning" would likely avoid the immediate negative reaction.
The Gaussian Processes underpinning this work are hardly a product of the 'AI Hype Machine' - they've been around for decades, have strong statistical underpinnings, and are being widely explored for experimental design across many disciplines. Reflexive and poorly-informed backlash to any variety of machine learning is no more productive than blindly hyping up LLMs.
Meta Platforms, Inc featuring this technology with a title announcing “AI for American-produced cement and concrete” is, on the other hand, 1000% a product of the AI Hype Machine.
Sure, it's clearly marketing. I think a private company pursuing marketing via open research with open source code (including datasets) is a good trade. A hypey blogpost + research is better than no blogpost and no research.
Was that the one immediately after the great paradigm shift of November 2025, and before the great paradigm shift of January 2026? I think I remember it.
There was no such paradigm shift. LLMs still suck just as much as they did before, in the exact same ways they did before. In 6 months you'll be trying to BS us about the "great paradigm shift of summer 2026".
It's no surprise that people readjust their immediate reactions by expressing hostility and skepticism about anything AI-related without spending much time on analysis. In fact, it's an entirely rational repones.
Complaining about it without acknowledging the larger picture is disingenuous.
In this particular case, using the term "machine learning" would likely avoid the immediate negative reaction.