One of the world’s most ambitious neutrino experiments, the Deep Underground Neutrino Experiment (DUNE), is being built to catch some of the universe’s most elusive particles, but finding those fleeting signals may soon involve artificial intelligence. 

As the massive detector takes shape a mile underground in South Dakota, researchers are developing AI systems to sort through huge amounts of data, identify neutrino interactions, spot signs of a stellar explosion and even detect problems inside the detector.

This could fundamentally change how scientists run a particle-physics experiment at DUNE’s scale. 

For instance, instead of waiting for conventional analysis to work through enormous amounts of data, AI could identify important events in near real time and direct researchers toward the signals most worth investigating.

“These approaches are going to really accelerate DUNE in terms of the commissioning process and the sensitivity of the experiment to reach our full discovery potential,” Sowjanya Gollapinni, a representative from DUNE, said.

Can AI read ghost-particle tracks?

Neutrinos are notoriously difficult to study. Trillions pass through our bodies every second, yet they interact so rarely that detecting them requires an enormous detector and an intense neutrino beam. 

Fermilab will generate a powerful neutrino beam that will travel to a detector at the Sanford Underground Research Facility in South Dakota, where huge chambers filled with liquid argon will record the tiny traces left when neutrinos collide with argon atoms.

Keeping so much argon in the right state is itself a huge engineering challenge. Each of DUNE’s two planned far-detector cryostats will eventually house about 17,000 tons of liquid argon, kept at roughly −300°F. The massive cryogenic containers must keep the argon cold enough for the detector to work.

However, the problem does not end when a neutrino is detected. Every interaction can produce several particles, each leaving a trail through the argon.

Scientists must work backward through those tracks to determine where the collision occurred, what particles were created, and what the original neutrino’s energy and direction were. 

Fermilab researchers have already spent years testing whether machine learning can make this process faster. The MicroBooNE experiment was among the first high-energy physics experiments to use deep neural networks to analyze images from a liquid-argon detector. DUNE is now taking that approach much further.

“We are going to have very high resolution, almost photographic-quality images of these interactions. Which is great, but also brings challenges such as making reconstruction more difficult because we see such fine detail.” ,” Leigh Whitehead, an AI expert at DUNE, said.

An AI lookout for exploding stars

DUNE’s AI will also be searching for something far rarer—a supernova in our galaxy.

When a massive star collapses and explodes, neutrinos escape from its interior and can reach Earth before the burst of light becomes visible. This gives scientists an unusual opportunity to study the violent processes occurring inside a dying star. 

DUNE’s giant liquid-argon detector is being built in part to capture neutrinos from astrophysical events such as nearby supernovae.

An AI trigger will continuously monitor DUNE’s detector for the telltale pattern of a supernova neutrino burst. If it finds a promising signal, the system is designed to preserve data from 10 seconds before to 100 seconds after the candidate event. 

This could give astronomers an early warning, allowing telescopes to turn toward the exploding star while its light is still arriving.

The neutrinos themselves could reveal what the explosion leaves behind — potentially a neutron star or a black hole. This makes DUNE part particle detector, part cosmic early-warning system.

AI could become DUNE’s technician

There is another problem hidden inside DUNE’s enormous scale, which is keeping the detector running. Thousands of components must operate together underground, and operators need to respond quickly when something goes wrong. 

Researchers are therefore investigating whether a large language model could search through records of previously solved problems and point operators toward relevant fixes.

Machine learning could eventually go a step further by recognizing patterns that indicate a detector anomaly before an actual failure occurs. 

This approach, therefore, reflects a broader push to use AI throughout particle-physics experiments, where increasingly complex facilities are producing more data than conventional methods can easily handle.

Building an AI-powered physics experiment

DUNE will eventually produce petabytes of data, making fast and reliable analysis essential. Researchers are developing AI tools alongside the detector while working with national laboratories and universities to build the computing infrastructure needed to handle that flood of information. 

The collaboration now includes more than 1,500 scientists and engineers from over 35 countries and CERN, while also training the next generation of researchers. 

The AI systems will need to be developed and tested alongside the experiment, but if they work as intended, DUNE could become a model for how AI helps scientists operate massive experiments and find rare signals hidden in overwhelming amounts of data.

They’re agile, they’re creepy, and they may be coming for human jobs.

Robot dogs affixed with AI-integrated cameras are becoming the latest dystopian fixation of the security industry, especially in the US, where they’re being deployed to patrol data centers, safeguard cash crops, and police sports stadiums.

Though expensive, they’re gradually becoming a more attractive option over human security guards. At least, that’s according to new reporting by Business Insider, which notes that the cost to cover one 24/7 guard shift is anywhere from $80,000 to $130,000 cheaper per year when contracting robot dogs over human security officers.

That’s bad news for the nation’s security guards, an essential role providing low-barrier employment for uneducated workers. Often deployed to help property owners reduce insurance premiums, the role of security guard is an important source of income for some 1.3 million people. According to a report by the UC Berkeley Labor Center, about 60.8 percent of those roles are filled by people of color, while 27.8 percent of guards are 55 or older.

It is, however, a difficult job to make a living on, with the median annual wage of just $38,390 as of 2024. That being the case, the industry faces a high degree of turnover, which tech companies are all too happy to exploit.

“Training them and staffing them is super inconsistent, and it’s a big burden on the business,” Damon Henry, CEO of robot dog company Asylon, told BI.

So far, Henry says he has about 50 robots deployed across 25 customers nationwide. Compared to a workforce of 1.3 million humans, that might not sound like much. But if robotic production lines get more efficient and the bots themselves become cheaper to roll out, it’s hard to imagine the nation’s businesses keeping low-paid security guards on the payroll out of charity (just look at what happened with Walmart greeters.)

Still, you probably haven’t bumped into your last mall cop quite yet. As director of the UC Berkeley Labor Center’s Low-Wage Work program and co-author of the report referenced above Enrique Lopezlira told BI, the role is likely to experience pressures similar to that of other workers facing automation: security jobs won’t disappear entirely, but they will get much more demanding.

“The realistic near-term effect is not that guards are replaced,” Lopezlira said. “The human role is going to be pushed toward those hardest, higher-stakes parts of the work.”

More on robots: Startup Sued After Delivery Bot Clobbers 73-Year Old Woman

The post Robot Dogs Suddenly Taking Jobs From Human Security Guards appeared first on Futurism.

Boston Dynamics trained its Atlas humanoid robot for millions of simulated hours before it successfully lifted and carried a mini-fridge.

Elon Musk says robots could build SpaceX factories on the Moon, but the ambitious lunar manufacturing plan has no disclosed budget or timeline.

DeepSeek invests $20.8 million in Unitree’s Shanghai IPO, deepening a partnership to develop AI models and applications for humanoid robots in China.

Unitree Robotics wants to raise about $900 million in an I.P.O. that tests market interest in humanoid robots, a technology that wows but has yet to prove its viability.

It’s a regrettable reality that there is never enough time to cover all the interesting scientific stories we come across. So every month, we highlight a handful of the best stories that nearly slipped through the cracks. July’s list includes outfitting cyborg cockroaches with a tiny diving suit for navigating flooded terrain; confirmation that a Medici duke died of malaria, not poisoning; the best evidence to date that Betelgeuse is orbited by a companion star; and evidence that some ancient Egyptian princesses were skilled archers.

Sacrificial Incan boy died of blunt force trauma

Credit: Administrative Archive, Regional Museum of Iquique–CORMUDESI

It's known that the Incas practiced a human sacrifice ritual known as Capachoca, in which children were offered to the mountains and believed to become divine themselves, serving as intermediaries with the gods. This often involved pilgrimages to the remote sacrificial sites, with colonial accounts describing strangulation, asphyxiation, and blunt force trauma as the most common methods of sacrifice. Archaeologists have reanalyzed the remains of one such victim and concluded the boy died from blunt force trauma, rather than freezing to death as previously believed. They described their findings in a paper published in the journal Science Advances.

Read full article

Comments

© National Museum of Natural History of Chile