Protein imaging is multiplexed using combinatorial staining and deep learning decompression.
Category: robotics/AI – Page 104
AI information sorting mirrors human cognition, claims Chinese study
A study in China found that AI can spontaneously develop human-like understanding of natural objects without being trained to do so.
Tech jobs, robots are Lutnick’s vision for America’s “manufacturing renaissance”
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.”
Advancing neuroscience research with high-speed, automated electrophysiology
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.
AI Uncovers Wild Spin of the Milky Way’s Supermassive Black Hole
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.
“If Anthropic Succeeds, Everything Changes”: A Nation of Benevolent AI Geniuses Could Rise and Reshape the Future of Human Civilization
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.
OpenAI, Google Ask The Government To Let Them Train AI On Content They Don’t Own
OpenAI argues it needs access to ‘avoid forfeiting’ the lead in AI to China.