Low-mass stars and brown dwarfs -- spectral types (SpTs) M0 and later -- play a significant role in studying stellar and substellar processes and demographics, reaching down to planetary-mass objects. Currently, the classification of these sources remains heavily reliant on visual inspection of spectral features, equivalent width measurements, or narrow-/wide-band spectral indices. Recent advances in machine learning (ML) methods offer automated approaches for spectral typing, which are becoming increasingly important as large spectroscopic surveys such as Gaia, SDSS, and SPHEREx generate datasets containing millions of spectra. We investigate the application of ML in spectral type classification on low-resolution (R $\sim$ 120) near-infrared spectra of M0--T9 dwarfs obtained with the SpeX instrument on the NASA Infrared Telescope Facility. We specifically aim to classify the gravity- and metallicity-dependent subclasses for late-type dwarfs. We used binned fluxes as input features and compared the efficacy of spectral type estimators built using Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) models. We tested the influence of different normalizations and analyzed the relative importance of different spectral regions for surface gravity and metallicity subclass classification. Our best-performing model (using KNN) classifies 95.5 $\pm$ 0.6% of sources to within $\pm$1 SpT, and assigns surface gravity and metallicity subclasses with 89.5 $\pm$ 0.9% accuracy. We test the dependence of signal-to-noise ratio on classification accuracy and find sources with SNR $\gtrsim$ 60 have $\gtrsim$ 95% accuracy. We also find that zy-band plays the most prominent role in the RF model, with FeH and TiO having the highest feature importance.

While official reports of eerie drone-like UFOs dropped over the holidays, New Jersey residents are still coming forward with bizarre encounters. Two witnesses in Manalapan Township, for example, videotaped a bus-sized, 25- to 50-foot-long black triangle UFO that they saw'pull off a high g [force] maneuver over a residential area' just days before Christmas. The sighting, which lasted at least one minute, ended with the object zooming'in the general direction of McGuire [Joint Base McGuire-Dix-Lakehurst]' -- matching a persistent pattern of'drone' UFO incursions over US bases in recent years. Another New Jersey skywatcher recorded what they described as a classic'flying saucer' with an'aura or haze around object' just three miles off the coast of Atlantic City. And still more Garden State witnesses now say they saw as many as 20 to 30 drones just this Wednesday night, which'kind of hovered and all looked like miniature aircraft,' in an account posted to Facebook.

A former NASA astronaut has come forward to reveal that he personally witnessed'two metallic spherical orbs' whizz by his plane this August while flying above Texas. Leroy Chiao, who served as the commander of Expedition 10 to the International Space Station (ISS) in 2004 and 2005, was 9,000 feet in the air when objects'zipped' on the left side of his airplane. He said one flew on top of the other and each was about three feet in diameter. 'It's just kinda dumb luck that they didn't hit me,' said Chiao. The former NASA astronaut estimates that the orbs were only'about 20 feet away.' 'It could've been a bad result, if they had actually hit me,' Chiao said.

The Pentagon's former UFO chief has revealed his conclusion to one of the most famous UFO cases of the modern era: the Navy's baffling'cube in a sphere' UFO was just a super high-tech drone. US Navy fighter pilots had reported seeing these other-worldly craft near the Atlantic coast between 2014 and 2015, which nearly tore the wing off an F/A-18 Super Hornet that was flying with the USS Roosevelt during one incident. Now Dr. Sean Kirkpatrick, the Pentagon's recently retired UFO chief, says that the objects were likely'next generation,' 'spherical' drones that move'very accurately.' While not confirmed, his description matches a drone-prototype made public by Chinese researchers in 2022 -- a silver orb with eight thrusters configured at the tips of an internal cube, making it capable of unprecedented mid-air twists and turns. The case highlights why UFOs must be taken seriously and not be subject to ridicule, Kirkpatrick suggested.

An unknown object with flashing lights appeared to hover over Marine base in Twentynine Palms, California, in 2021. A Stanford University pathology professor said, "Aliens have been on Earth for a long time and are still here," and claims there are experts working on reverse engineering unknown crashed crafts. Dr. Garry Nolan made the bold statements during last week's SALT iConnections conference in Manhattan during a session called, "The Pentagon, Extraterrestrial Intelligence and Crashed UFOs." The host, Alex Klokus, said that's tough to believe and asked him to assign a probability to that statement that extraterrestrial life visited Earth. "I think it's an advanced form of intelligence that using some kind of intermediaries," Nolan said.

From Elon Musk to Mark Zuckerberg, the titans of tech today have more notoriety than movie stars of yore and, often, the egos to match. But at what point does idolatry--within the culture of tech and beyond it--and internal self-regard get in the way of progress rather than bolster it? This was the subject of Techonomy founder David Kirkpatrick's conversation with Autodesk CEO Andrew Anagnost at Techonomy 22, which took place November 13-15 at the Fairmont Sonoma Mission Inn & Spa. Within moments of welcoming Anagnost to the stage, Kirkpatrick dove right in. "Do you think tech has an ego problem?" "I do," replied Anagnost, who used the phrase "celebrity technologist" to characterize leaders known as much for their personalities as innovations--and who may prioritize image over achievements.

Once you trust a self-driving car with your life, you pretty much will trust Artificial Intelligence with anything--Dave Waters. Keith Kirkpatrick is the author of an interesting CACM article on self-driving cars. It is titled "Still Waiting For Self-Driving Cars" and appears in the news section of this month's issue. Today we discuss why it has been so difficult to get self-driving cars started. Over the past decade, technology and automotive pundits have predicted the "imminent" arrival of fully autonomous vehicles that can drive on public roads without any active monitoring or input from a human driver.

The AI predicts the distribution of electrons within a molecule (illustration) and uses it to calculate physical properties.Credit: DeepMind A team led by scientists at the London-based artificial-intelligence company DeepMind has developed a machine-learning model that suggests a molecule's characteristics by predicting the distribution of electrons within it. The approach, described in the 10 December issue of Science1, can calculate the properties of some molecules more accurately than existing techniques. "To make it as accurate as they have done is a feat," says Anatole von Lilienfeld, a materials scientist at the University of Vienna. The paper is "a solid piece of work", says Katarzyna Pernal, a computational chemist at Lodz University of Technology in Poland. But she adds that the machine-learning model has a long way to go before it can be useful for computational chemists.

Machine vision, natural language processing, data analytics and other deep learning applications will propel global AI software revenues over the next five years via a growing list of industry segments spanning automotive and health care to financial services and retail. Market tracker Omdia forecasts AI software revenues will surge through 2025 to $126 billion, a 12-fold increase over a $10.1 billion industry in 2018. "The narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level," said Keith Kirkpatrick, principal analyst with Omdia. "AI is likely to trigger major transformations in industries where there is a clear case for incorporating AI, rather than in pie-in-the-sky use cases that may not generate a return on investment for many years," Kirkpatrick added. Omdia estimates that more than half of AI revenues will be generated by machine vision and language applications, with deep learning deployments driving the AI market.

At more than seven decades old, the United Nations has often been criticized for being too slow to respond to crises. But behind the scenes, a high-tech team is harnessing the power of big data and artificial intelligence to predict, monitor and respond to emergencies. CGTN's U.N. correspondent Liling Tan has an inside look at how U.N. Global Pulse is keeping the organization up to speed in the 21st century. Three blocks from the United Nations headquarters in New York, a veritable geek squad of data scientists, analysts and engineers are using big data and artificial intelligence for global good. Or, as U.N. Global Pulse's Director Robert Kirkpatrick puts it, "Our job is to help superheroes find out where people are in trouble, so they can rescue them."