Lifeboat Foundation

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A group of physicists wants to use artificial intelligence to prove that reality doesn’t 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 I’m afraid it’s not.

Continue reading “AI could prove that reality doesn’t exist, physicists say” »

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Can you really send a particle into the past? New Scientist published an article about this last week, and though I’m quite fond of the concept of retrocausality, I’m afraid to say that reality is much less interesting than fiction. Let’s have a look.

Continue reading “Physicists Claim They Can Send Particles Into the Past” »

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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? Who’s at the forefront of the field now? Let’s have a look.

Continue reading “Biggest Self-Own in Quantum Computing, Ever” »

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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 that’s been called the “Hubble tension”. Now, a team of astrophysicists claims the Hubble tension is gone and it’s the fault of supernovae data. Let’s have a look.

Continue reading “A Huge Cosmology Problem Might Just Have Disappeared” »

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Aman Bhargava from Caltech and Cameron Witkowski from the University of Toronto to discuss their groundbreaking paper, “What’s the Magic Word? A Control Theory of LLM Prompting.” 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 “reachable set” 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 Cameron’s 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.

Continue reading “These two scientists have mapped out the insides or ‘reachable space’ of a language model using control theory, what they discovered was extremely surprising” »

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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.

Continue reading “Einstein’s Other Theory of Everything” »

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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. Let’s have a look.

Continue reading “Brain Really Uses Quantum Effects, New Study Finds” »

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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 — 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? Let’s have a look.

Continue reading “CERN Looks for Origins of Quantum Randomness” »

Inside every plant, animal and human cell are billions of molecular machines. They’re made up of proteins, DNA and other molecules, but no single piece works on its own. Only by seeing how they interact together, across millions of types of combinations, can we start to truly understand life’s processes.

In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy.

We hope AlphaFold 3 will help transform our understanding of the biological world and drug discovery. Scientists can access the majority of its capabilities, for free, through our newly launched AlphaFold Server, an easy-to-use research tool. To build on AlphaFold 3’s potential for drug design, Isomorphic Labs is already collaborating with pharmaceutical companies to apply it to real-world drug design challenges and, ultimately, develop new life-changing treatments for patients.