PricewaterhouseCoopers (PwC) told 1,500 U.S. employees on Monday that their roles were eliminated with immediate effect—about 2 percent of its national headcount. In the company-wide e-mail obtained by industry blog Going Concern, leadership cited “historically low attrition” and a need to “align the firm for the future.” Publicly, a spokesperson framed the move as a […]
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The future of human longevity just got a $500billion booste thanks to Trump’s bold new AI vision! In this video, we explore the recently announced Stargate AI Infrastructure Project—a staggering $500 billion initiative unveiled by President Donald Trump on January 21, 2025, alongside tech titans OpenAI, Oracle, and SoftBank. Designed to build colossal data centers and supercharge AI development, could this ambitious plan hold the key to curing aging? We dive into the cutting-edge science, the potential of AI to revolutionize healthcare, and how Stargate might reshape humanity’s fight against time. Buckle up for a wild ride into the future!
🔍 What You’ll Discover:
The Stargate Project: Trump’s massive AI infrastructure gamble.
How AI could unlock breakthroughs in aging research.
The players: OpenAI, Oracle, SoftBank, and their bold vision.
The big question: Can this tech triumph over mortality?
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An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments.
In their study published in the journal Nature Machine Intelligence, Sheffield computer scientists in collaboration with AstraZeneca and the University of Southampton have developed a new machine learning framework that has shown the potential to be more accurate at inverse protein folding than existing state-of-the-art methods.
Inverse protein folding is a critical process for creating novel proteins. It is the process of identifying amino acid sequences, the building blocks of proteins, that fold into a desired 3D protein structure and enable the protein to perform specific functions.
A study in China found that AI can spontaneously develop human-like understanding of natural objects without being trained to do so.
Commerce Secretary Howard Lutnick on Thursday touted the use of robotics in his pitch for an American “manufacturing renaissance.”
The big picture: While President Trump’s tariffs are meant to boost American manufacturing and jobs, U.S. manufacturers will likely hurt from these tariffs, at least in the short run. Whether they lead to more jobs in the long term remains an open question.
What he’s saying: Lutnick made the case in several TV interviews this week that tariffs will bring jobs and factories back to the U.S., saying they’ll utilize robotics to make American workers “more efficient.”
Understanding the electrical activity of neurons is key to unlocking insights into neurological diseases. Yale researchers have unveiled a high-throughput automated method that captures the electrical activity of large numbers of neurons simultaneously and without bias.
This cutting-edge approach provides a powerful “functional fingerprint” of neuron populations in their natural state, opening new doors to understanding and treating neurological diseases. The work was published June 13 in Nature Protocols.
The patch-clamp technique has long been a gold standard for studying the electrical activity of neurons, the fundamental units of the nervous system. However, the manual execution of this approach is slow and labor-intensive. Recent advances in robotic patch-clamp technologies have improved speed and efficiency, but they are limited to artificially grown neurons rather than neurons in their native unmanipulated state.
Back in 2019, the Event Horizon Telescope (EHT) team revealed the first-ever image of a supermassive black hole in the galaxy M87. In 2022, they followed up with the iconic image of Sagittarius A at the heart of the Milky Way. While these images were groundbreaking, the data behind them held even deeper insights that were hard to decode.
Neural Networks Meet Black Hole Physics
Previous studies by the EHT Collaboration used only a handful of realistic synthetic data files. Funded by the National Science Foundation (NSF) as part of the Partnership to Advance Throughput Computing (PATh) project, the Madison-based CHTC enabled the astronomers to feed millions of such data files into a so-called Bayesian neural network, which can quantify uncertainties. This allowed the researchers to make a much better comparison between the EHT data and the models.
IN A NUTSHELL 🌟 Anthropic is on a mission to develop artificial general intelligence that remains ethical and benevolent. 🎯 The company’s strategy, known as the Race to the Top, aims to set global standards for safe AI development. 🤖 Claude, Anthropic’s AI model, is designed to embody ethical principles and serve as a constant.