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Harnessing Machine Learning to Guide Scientific Understanding

A clever use of machine learning guides researchers to a missing term that’s needed to accurately describe the dynamics of a complex fluid system.

Physical theories and machine-learning (ML) models are both judged on their ability to predict results in unseen scenarios. However, the bar for the former is much higher. To become accepted knowledge, a theory must conform to known physical laws and—crucially—be interpretable. An interpretable theory is capable of explaining why phenomena occur rather than simply predicting their form. Having such an interpretation can inform the scope of a new theory, allowing it to be applied in new contexts, while also connecting it to and incorporating prior knowledge. To date, researchers have largely struggled to get ML models (or any automated optimization process) to produce new theories that meet these standards. Jonathan Colen and Vincenzo Vitelli of the University of Chicago and their colleagues now show success at harnessing ML not as a stand-in for a researcher but rather as a guide to aid building a model of a complex system [1].

After Years of Chasing Money, OpenAI Reportedly Giving Up on Being a “Nonprofit”

Billions of dollars worth of investment rounds later, the Financial Times is now reporting that the company is finally looking to shed its nonprofit status once and for all.

The company is reportedly in talks to raise further new funds, giving it a valuation of north of $100 billion and potentially making it one of the most valuable Silicon Valley firms ever.

OpenAI has since denied the reporting, arguing in a statement to the FT that “the nonprofit is core to our mission and will continue to exist.”

AGI: significant milestone achieved for this global-scale super artificial intelligence

Artificial intelligence (AI) is on the brink of reaching a new significant milestone. A team of researchers aims to develop artificial general intelligence (AGI), capable of surpassing human intelligence in various fields, by establishing a global network of ultra-powerful supercomputers. This project, led by SingularityNET, will commence in September with the launch of the first supercomputer specifically designed for this purpose.

Chinese Company Busted Showing Off Humanoid Robots That Actually Have Humans Inside

Footage making the rounds on social media shows what appear to be astonishingly lifelike humanoid robots posing at the World Robot Conference in Beijing last week.

But instead of showing off the latest and greatest in humanoid robotics, two of the “robots” turned out to be human women cosplaying as futuristic gynoids, presumably hired by animatronics company Ex-Robots.

“Many people think these are all robots without realizing they’re actually two human beings cosplayed as robots among the animatronics,” reporter Byron Wan tweeted.

Physics beyond the imaginable

As an undergraduate he was drawn to theory, but he quickly switched to experiment.

“Theory was good, but I was driven to experimental particle physics because even if I write a theory, someone has to test it anyway,” says Gandrakota, who is now a postdoc at the US Department of Energy’s Fermi National Accelerator Laboratory. “I’d rather be the person who tests and finds stuff than the person who predicts it.”

But he never lost his soft spot for theoretical physics. Today, Gandrakota and his colleagues on the CMS experiment are developing a machine-learning tool that will allow theorists even more freedom and creativity.