One reason deep learning exploded over the last decade was the availability of programming languages that could automate the math -- college-level calculus -- that is needed to train each new model. Neural networks are trained by tuning their parameters to try to maximize a score that can be rapidly calculated for training data. The equations used to adjust the parameters in each tuning step used to be derived painstakingly by hand. Deep learning platforms use a method called automatic differentiation to calculate the adjustments automatically. This allowed researchers to rapidly explore a huge space of models, and find the ones that really worked, without needing to know the underlying math.

From Threads to The Day After, 'nuclear winter' has been portrayed in science fiction blockbusters for years - but what would the cataclysmic aftermath of a nuclear attack really be like? Smoke from the fires started by nuclear weapons would rise into the atmosphere, blocking out the sun. The resulting perpetual darkness would mean freezing temperatures and crop failure, followed by mass starvation and death. While it sounds very much like a fictional scenario, an expert describes a nuclear winter as a real and'horribly contemporary' risk due to Russia's war on Ukraine. It follows scientific advice of how best to survive a nuclear attack, after Putin has made a series of nuclear threats since the start of Russia's war on Ukraine last year.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. military announced Tuesday it shot down a drone believed to have been manufactured by Iran as it was allegedly conducting surveillance over an oil site in northeastern Syria. The drone was taken down at around 2:30 p.m. local time, according to U.S. Central Command. "US forces in Syria engaged and shot down an Iranian-manufactured UAV attempting to conduct reconnaissance of Mission Support Site Conoco, a patrol base in northeast Syria," U.S. Central Command said in a statement.

The tanks and trench warfare in Ukraine may seem old-school, but US experts say the conflict has provided strategic insights into future possible conflicts from the Middle East to Taiwan. They range from the mundane -- the need for bigger weapons stockpiles -- to the high-tech, with Ukraine a proving ground for artificial intelligence and robotic warfare. Ukraine has been a test for "sensor fusion," triangulating diverse sources of information to create a fuller picture of the battlefield, said Stephen Biddle, a defense expert at Columbia University. US firm Palantir has provided Kyiv with artificial intelligence-powered tools that sort through gigabytes of data to help commanders understand the war in real time: Russian troop movements, positions and targets. Drone warfare came of age in Ukraine, but now both sides are roughly matched in capabilities, and armies around the world are catching up.

Artificial intelligence (AI) is both omnipresent and conceptually slippery, making it notoriously hard to regulate. Fortunately for the rest of the world, two major experiments in the design of AI governance are currently playing out in Europe and China. The European Union (EU) is racing to pass its draft Artificial Intelligence Act, a sweeping piece of legislation intended to govern nearly all uses of AI. Meanwhile, China is rolling out a series of regulations targeting specific types of algorithms and AI capabilities. For the host of countries starting their own AI governance initiatives, learning from the successes and failures of these two initial efforts to govern AI will be crucial.

Pentagon boffins have for the first time used AI algorithms to automatically control a real F-16 fighter jet mid-flight. Well, OK, at least for the first time they can talk about. The aircraft flown in the experiment, dubbed X-62A or VISTA, was modified and equipped with the right hardware components to run the software developed under the Air Combat Evolution (ACE) program run by the US Dept of Defense's science nerve-center DARPA. Launched in 2019, the program's goal is to revamp aircraft combat by enabling F-16s to automatically dogfight. DARPA envisions machine-learning algorithms assisting pilots with flying and perform tactical maneuvers, with our humans focusing on battle commands, strategy, and firing weapons.

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Fox News correspondent Lucas Tomlinson joins'Fox News Live' with the latest on a Chinese spy balloon being shot down by the U.S. military while hovering off the South Carolina coast. An artificial intelligence algorithm developed by DARPA's Air Combat Evolution (ACE) program flew a modified F-16 fighter jet for over 17 hours in December, officials announced this week. The jet, a Variable In-flight Simulation Test Aircraft (VISTA), took off from Edwards Air Force Base in California, marking the first time that artificial intelligence has been used to fly a tactical aircraft. A Variable In-flight Simulation Test Aircraft being controlled by artificial intelligence in a flight over California. "We conducted multiple sorties [takeoffs and landings] with numerous test points performed on each sortie to test the algorithms under varying starting conditions, against various simulated adversaries, and with simulated weapons capabilities," Air Force Lt. Col. Ryan "Hal" Hefron, the DARPA program manager for ACE, said in a statement.

Move over, Maverick: artificial intelligence (AI) may soon be the next hot fighter jock in the skies. An AI agent recently flew Lockheed Martin's VISTA X-62A for more than 17 hours at the U.S. Air Force Test Pilot School (USAF TPS) at Edwards Air Force Base in California--the first time AI was used on a tactical aircraft. The experimental training aircraft is expected to lay the groundwork for a coming wave of jets piloted entirely by computers. The VISTA, developed by Lockheed's Skunk Works and Calspan Corporation, is fitted with software that allows it to mimic the performance characteristics of other aircraft. The test plane is a modified F-16D Block 30 Peace Marble Il aircraft upgraded with Block 40 avionics.

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