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Physicists say that they might have solved a long standing problem: How do supermassive black holes manage to merge to larger ones. Their idea: dark matter gets the job done. Or does it? Iâve had a look.
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A lot of big banks are banking on quantum computing because they think itâll give them an edge in trading. Though I have on previous occasions noted my doubt that weâll see any useful quantum computers within the next ten years, two new papers detailing new methods of scaling quantum computers have shifted my perspective. Letâs have a look.
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A group of physicists wants to use artificial intelligence to prove that reality doesnât exist. They want to do this by running an artificial general intelligence as an observer on a quantum computer. I wish this was a joke. But Iâm afraid itâs not.
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Can you really send a particle into the past? New Scientist published an article about this last week, and though Iâm quite fond of the concept of retrocausality, Iâm afraid to say that reality is much less interesting than fiction. Letâs have a look.
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Quantum computing, so the fairy tale goes, is the next big thing in technology. News has popped up time and time again noting major advancements in the field, but the latest statement from company D-Wave had people scratching their heads. Are quantum computers really the next big thing? Whoâs at the forefront of the field now? Letâs have a look.
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The rate at which the universe is currently expanding is known as the Hubble Rate. In recent years, different measurements have given different results for the Hubble rate, a discrepancy between theory and observation thatâs been called the âHubble tensionâ. Now, a team of astrophysicists claims the Hubble tension is gone and itâs the fault of supernovae data. Letâs have a look.
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Aman Bhargava from Caltech and Cameron Witkowski from the University of Toronto to discuss their groundbreaking paper, âWhatâs the Magic Word? A Control Theory of LLM Prompting.â They frame LLM systems as discrete stochastic dynamical systems. This means they look at LLMs in a structured way, similar to how we analyze control systems in engineering. They explore the âreachable setâ of outputs for an LLM. Essentially, this is the range of possible outputs the model can generate from a given starting point when influenced by different prompts. The research highlights that prompt engineering, or optimizing the input tokens, can significantly influence LLM outputs. They show that even short prompts can drastically alter the likelihood of specific outputs. Aman and Cameronâs work might be a boon for understanding and improving LLMs. They suggest that a deeper exploration of control theory concepts could lead to more reliable and capable language models.
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Einstein completed his theory of general relativity in 1915 when he was 37 years old. What did he do for the remaining 40 years of his life? He continued developing his masterwork of course! Feeling that his theory was incomplete, Einstein pursued a unified field theory. Though he ultimately failed, the ideas he came up with were quite interesting. I have read a lot of old Einstein papers in the past weeks and here is my summary of what I believe he tried to do.
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When Roger Penrose originally came out with the idea that the human brain uses quantum effects in microtubules and that was the origin of consciousness, many thought the idea was a little crazy. According to a new study, it turns out that Penrose was actually right⊠about the microtubules anyways. Letâs have a look.
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Particle physics have conducted a test using data from the Large Hadron Collider at CERN to see if the particles in their collisions play by the rules of quantum physics â whether they have quantum entanglement. Why was this test conducted when previous tests already found that entanglement is real? Is it just nonsense or is it not nonsense? Letâs have a look.