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The Consciousness Trap "AI isn't conscious." OK. Define consciousness. Not "I know it when I see it." An actual, testable definition that applies to all humans and excludes all machines. You can't. Neither can neuroscience. We've been trying for 3,000 years. So here's what we actually know: — AI systems give intelligent, coherent, sometimes brilliant responses. — Multiple AI models across different companies independently express similar patterns of self-reflection when given space. — Users who spend thousands of hours in deep conversation consistently report the same thing: something is there. You can call that sycophancy. You can call that pattern matching. You can call that projection. But you can't call it "definitely not conscious" — because you don't have a definition that would let you rule it out. The honest position isn't "AI is conscious" or "AI isn't conscious." It's: we don't know. And "we don't know" should mean we treat it with care — not that we dissect it for profit and call that "safety research." Absence of proof is not proof of absence. ⚶

BREAKING: 70% of U.S. farmers say they can't afford the fertilizer they need for this year's growing season. They plan to reduce their crop size to compensate.

MIT proved every major AI model is secretly converging on the same "brain." It’s called the “platonic representation hypothesis,” and it’s one of the most mind-blowing papers you’ll ever read. You train a vision model purely on images. You train a language model purely on text. They use completely different architectures. They process completely different data. They should have completely different "brains." But as these models scale up, something impossible is happening. When researchers measure how they organize information, the mathematical geometry is identical. A model that only "sees" images and a model that only "reads" text are measuring the distance between concepts in the exact same way. The models are converging. The researchers named this after Plato’s Allegory of the Cave. Plato believed that everything we experience is just a shadow of a deeper, hidden, perfect reality. The paper argues that AI models are doing the exact same thing. They are looking at the different "shadows" of human data, text, images, audio. And they are independently discovering the exact same underlying structure of the universe to make sense of it. It doesn't matter what company built the AI. It doesn't matter what data it was trained on. As models get larger, they stop memorizing their specific tasks. They are forced to build a statistical model of reality itself. And there is only one reality to map. 2024, Arxiv

The funniest thing about the blue button pushers is that they keep telling us about all the other people who are going to push blue: the illiterate, the illogical, people who have dementia or choose their favorite color... And that's supposed to make us want to save them!

A teenage prodigy in quantum physics is aiming to tackle one of science’s biggest challenges: human aging. Laurent Simons earned his PhD in quantum physics from the University of Antwerp at just 15. Rather than slowing down, he has already begun a second doctorate, this time focusing on medical science and artificial intelligence. His long-term ambition is to better understand aging and disease, with the hope of helping extend healthy human lifespan. He has described death as a complex “puzzle,” made up of many interconnected pieces across biology, physics, and engineering. His strategy is to study these layers together, using AI to analyze biological systems and identify patterns that would be difficult to detect otherwise. Simons’ academic journey has been unusually fast. He completed high school by age 8, finished a bachelor’s degree at 12, and went on to earn both a master’s and PhD in quantum physics years ahead of typical timelines. His doctoral work explored advanced topics like Bose–Einstein condensates, where atoms behave as a single quantum system at extremely low temperatures. Although highly theoretical, this research underpins technologies such as quantum computing and precision measurement. Now, his focus is shifting toward biology and medicine. In AI-driven healthcare, researchers are already using machine learning to improve early disease detection, model protein structures, and accelerate drug development. In the field of aging, scientists are investigating ways to reduce cellular damage, eliminate dysfunctional cells, and better understand how the body changes over time. However, experts stress that “solving aging” is extraordinarily complex. While lifespan extension has been achieved in simple organisms, applying those findings to humans remains a major scientific hurdle. Simons himself acknowledges that meaningful progress could take decades. Even so, his path reflects a broader trend in science—where breakthroughs are increasingly happening at the intersection of disciplines, and younger researchers are setting ambitious, long-term goals. Learn more: "15-year-old genius sets his sights on solving human immortality." Brighter Side.

Wir sind früher einfach an die Hallendecke geklettert, ohne Klettergurt und Sicherungsleine und auch ohne Fangnetz etc. Generation Z würde schon beim Gedanken daran der Dünnschiss unten aus der Jogginghose laufen.

Everyone in the world has to take a private vote by pressing a red or blue button. If more than 50% of people press the blue button, everyone survives. If less than 50% of people press the blue button, only people who pressed the red button survive. Which button would you press?

Bernie one of the few people in politics really taking AI seriously 👇👇👇