'What is the meaning of this modern civilization? To make all people forget God and thus prepare the end of the world.'
(Frithjof Schuon, letter to Benjamin Black Elk, 24 July 1959)
@QuantumAlteredX Synchromysticism: "meaningful coincidences in movies with mystical connotation" -@Seallion
https://t.co/MTilhC3r41
https://t.co/dQdZZnaAC7
‘[The] scientist is never more deeply under the sway of his metaphysical presuppositions than when he is unaware of their very existence.’
– Ludwig Klages
"Where men are forbidden to honor a king they honor millionaires, athletes, or film-stars instead: even famous prostitutes or gangsters. For spiritual nature, like bodily nature, will be served; deny it food and it will gobble poison."
— C.S. Lewis, Equality
I am surprised how many AI utopianists double down on AI-generated synthetic data, to train "superintelligence" or "solve biology".
Synthetic data is pretty much a dead end in serious AI research, outside of a few specific domains where data integrity can be easily verified (e.g. coding - where generated code can be computationally checked for errors).
For more open-ended tasks in the sciences, humanities etc. - synthetic data tends to compound existing errors and hallucinations, *worsening* models that are downstream trained on it.
Synthetic data is not a magic work-around to having to actually tirelessly observe the real world and slowly build up high-quality data.
And this is one of many reasons why scarcity of high-quality data is probably the biggest bottleneck to AI progress.
⚠️ AI agents are wildly premature technology that is being rolled out way too fast. The deepest lesson about the vibe coded AI agent disaster story that is running around is NOT about losing your data. ⚠️
𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝘀𝗮𝗳𝗲𝘁𝘆.
The user wasn’t (totally) naive. He thought system prompts and guardrails would save him. They didn’t.
In this case he lost data. Eventually people will lose lives.
This is not even wrong.
alphaFold does not model "folding".
It models 𝑠𝑡𝑎𝑡𝑖𝑐 strictures as measured in crystals.
They represent a subset of protein structures and exclude functional regions present in the majority of human proteins: https://t.co/aPCZguOxwy
Hype hurts scientific credibility.
The “Dark Ages” produced some of the brightest lights in human history.
Gothic cathedrals that still silence crowds.
Universities that shaped Western learning.
Music, philosophy, and theology that still form our moral imagination.
Maybe the age was never dark.
Maybe we just forgot how to see its light.
Robert Skidelsky, economic historian, 1939-2026 via @FT
“I have come to see economics as a fundamentally regressive discipline . . . disguised by increasingly sophisticated mathematics and statistics.”
Heard him at Columbus during GFC https://t.co/CMXh8ePjr3
🦔 Michael Wooldridge, professor of AI at Oxford, is warning that the race to market AI has raised the risk of a "Hindenburg moment" that could shatter global confidence in the technology. He's delivering the Royal Society's Michael Faraday prize lecture Wednesday titled "This is not the AI we were promised."
His concern is that commercial pressure is pushing companies to release AI tools before their capabilities and flaws are fully understood. The scenarios he imagines include a deadly software update for self-driving cars, an AI-powered hack that grounds global airlines, or a major company collapsing because AI did something catastrophically stupid. "These are very, very plausible scenarios," he said.
Wooldridge's deeper point is about the gap between what researchers expected and what we got. Many anticipated AI that computed sound, complete solutions. Instead, large language models predict the next word based on probability, leading to systems that are incredibly effective at some tasks and terrible at others, with no way to know which is which. They fail unpredictably and have no idea when they're wrong, but are designed to sound confident regardless.
My Take
The Hindenburg comparison is striking because that disaster didn't just kill 36 people. It killed an entire technology. Airships were dead from that point forward. Wooldridge is suggesting AI could face something similar if a high-profile failure hits the right sector at the right moment.
What resonates with me is his point about unpredictable failure modes. These systems don't know when they're wrong, but they're designed to never sound uncertain. That's a dangerous combination when you're deploying them into cars, airlines, and financial systems. One quote stuck with me. He said maybe we need AIs that talk like the Star Trek computer, telling you when there's insufficient data to answer instead of confidently making something up. We got the opposite. We got systems optimized to sound authoritative whether they know anything or not.
Hedgie🤗
you are reading this in the branch where you were curious enough to look but there is a version of you who don't read this. that version will never know what it missed in this timeline