Is a cutting-edge application that unveils the political biases embedded in artificial intelligence systems. Explore and analyze the political leanings of AIs with our intuitive platform, designed to foster transparency in the world of artificial intelligence. Stay informed and uncover the political inclinations shaping the algorithms behind the technology revolution.
Nvidia CEO Jensen Huang says he uses AI chatbots like OpenAI’s ChatGPT or Google’s Gemini to write his first drafts for him.
Stephen Wolfram is a physicist, mathematician, and programmer who believes he has discovered the computational rules that organize the universe at the finest grain. These rules are not physical rules like the equations of state or Maxwell’s equations. According to Wolfram, these are rules that govern how the universe evolves and operates at a level at least one step down below the reality that we inhabit. His computational principles are inspired by the results observed in cellular automata systems, which show that it’s possible to take a very simple system, with very simple rules, and end up at complex patterns that often look organic and always look far more intricate than the black and white squares that the game started with. He believes that the hyperspace relationships that emerge when he applies a computational rule over and over again represent the nature of the universe — and that the relationships that emerge contain everything from the seed of human experience to the equations for relativity, evolution, and black holes. We sit down with him for a conversation about the platonic endeavor that he has undertaken, where to draw the line between lived experience and the computational universe, the limits of physics, and the value of purpose and the source of consciousness.
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Aging is defined as the deterioration of function over time, and it is one of the main risk factors for numerous chronic diseases. Although aging is a complex phenomenon affecting the whole organism, it is proved that the solely manifestation of aging in the hematopoietic system affects the whole organism. Last September, Dr. M. Carolina Florian and her team revealed the significance of using blood stem cells to pharmacologically target aging of the whole body, thereby suggesting rejuvenating strategies that could extend healthspan and lifespan.
Now, in a Nature Aging, they propose rejuvenating aged blood stem cells by treating them with the drug Rhosin, a small molecule that inhibits RhoA, a protein that is highly activated in aged hematopoietic stem cells. This study combined in vivo and in vitro assays at IDIBELL together with innovative machine learning techniques by the Barcelona Institute for Global Health (ISGlobal), a center supported by the “la Caixa” Foundation, and the Barcelona Supercomputing Center.
Researchers have created an artificial intelligence model that can identify which mutations in human proteins are most likely to cause disease, even when those mutations have never been seen before in any person.
The model, called popEVE, was created using data from hundreds of thousands of different species and of genetic variation across the human population. The vast evolutionary record allows the tool to see which parts of every one of the roughly 20,000 human proteins are essential for life and which can tolerate change.
That allows popEVE to not only identify disease-causing mutations but also rank how severe they are across the body. The findings, published today in Nature Genetics by researchers at Harvard Medical School and the Center for Genomic Regulation (CRG) in Barcelona, could transform how doctors diagnose genetic disease.
Rapidly expanding advances in computational prediction capabilities have led to the identification of many potential materials that were previously unknown, including millions of solid-state compounds and hundreds of nanoparticles with complex compositions and morphologies. Autonomous workflows are being developed to accelerate experimental validation of these bulk and nanoscale materials through synthesis. For colloidal nanoparticles, such strategies have focused primarily on compositionally simple systems, due in part to limitations in the generalizability of chemical reactions and incompatibilities between automated setups and mainstream laboratory methods. As a result, the scope of theoretical versus synthesizable materials is rapidly diverging. Here, we use a simple automated platform to drive a massively generalizable reaction capable of producing more than 651 quadrillion distinct core@multishell nanoparticles using a single set of reaction conditions. As a strategic model system, we chose a family of seven isostructural layered rare earth (RE) oxychloride compounds, REOCl (RE = La, Ce, Pr, Nd, Sm, Gd, Dy), which are well-known 2D materials with composition-dependent optical, electronic, and catalytic properties. By integrating a computer-driven, hobbyist-level pump system with a laboratory-scale synthesis setup, we could grow up to 20 REOCl shells in any sequence on a REOCl nanoparticle core. Reagent injection sequences were programmed to introduce composition gradients, luminescent dopants, and binary through high-entropy solid solutions, which expands the library to a near-infinite scope. We also used ChatGPT to randomly select several core@multishell nanoparticle targets within predefined constraints and then direct the automated setup to synthesize them. This platform, which includes both massively generalizable nanochemical reactions and laboratory-scale automated synthesis, is poised for plug-and-play integration into autonomous materials discovery workflows to expand the translation of prediction to realization through efficient synthesis.
