Infrared night-vision systems that see in colour could be a reality thanks to researchers in the US, who have used machine learning to create colour images of photographs that are illuminated with just infrared light. The team hope their technique could be further developed to create imaging systems that operate where the use of visible light is impossible, such as retinal surgery. Traditional night vision systems work by illuminating an area with near infrared radiation and detecting the reflections or by using ultrasensitive cameras to detect the small amount of light present even at night. Both, however, usually produce monochromatic images, so researchers are seeking ways to produce multi-colour images of objects without having to bathe them in visible light. Computer scientist Pierre Baldi of University of California, Irvine (UCI), explains that this would be very useful in medical applications where use of visible light is problematic.

DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state -- each of six sides displaying a solid color -- which apparently can't be found through random moves. For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban. "Artificial intelligence can defeat the world's best human chess and Go players, but some of the more difficult puzzles, such as the Rubik's Cube, had not been solved by computers, so we thought they were open for AI approaches," said senior author Pierre Baldi, UCI Distinguished Professor of computer science.

Intelligence: We all think we know it when we see it. But do we really understand that elusive quality? It's clear that our ideas about intelligence have evolved over time as the skills deemed necessary for survival and success have changed. Just think about the way kids roll their eyes when their parents have a hard time understanding technology. Those young folks instinctively grasp what to us seems foreign and hopelessly confounding.

"Our AI takes about 20 moves, most of the time solving it in the minimum number of steps," Baldi says. "Right there, you can see the strategy is different, so my best guess is that the AI's form of reasoning is completely different from a human's." The ultimate goal of projects such as this one is to build the next generation of AI systems, Baldi says. Whether they know it or not, artificial intelligence touches people every day through apps such as Siri and Alexa and recommendation engines working behind the scenes of their favorite online services. "But these systems are not really intelligent; they're brittle, and you can easily break or fool them," Baldi says.

Few things reveal the limits of someone's problem-solving skills faster than a Rubik's Cube, the multicolored, three-dimensional puzzle that has befuddled so many since the 1970s. Though the cube has furrowed countless human brows over the years, it's not much of a challenge for an emerging group of hyper-intelligent machines, as it turns out. This week, the University of California at Irvine announced that an artificial intelligence system solved the puzzle in just over a second, besting the current human world record by more than two seconds. The system, known as DeepCubeA -- a reinforcement-learning algorithm programmed by UCI computer scientists and mathematicians -- solved the puzzle without prior knowledge of the game or coaching from its human handlers, according to the university. The feat is even more impressive considering that there are billions of potential moves available to a Rubik's Cube player, with the puzzle's six sides and nine sections, but only one goal: each of the cube's six sides displaying a solid color.

Since its invention by a Hungarian architect in 1974, the Rubik's Cube has furrowed the brows of many who have tried to solve it, but the 3-D logic puzzle is no match for an artificial intelligence system created by researchers at the University of California, Irvine. DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state--each of six sides displaying a solid color--which apparently can't be found through random moves. For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.

Researchers have developed an AI algorithm which can solve a Rubik's cube in a fraction of a second, according to a study published in the journal Nature Machine Intelligence. The system, known as DeepCubeA, uses a form of machine learning which teaches itself how to play in order to crack the puzzle without being specifically coached by humans. "Artificial intelligence can defeat the world's best human chess and Go players, but some of the more difficult puzzles, such as the Rubik's Cube, had not been solved by computers, so we thought they were open for AI approaches," Pierre Baldi, one of the developers of the algorithm and computer scientist from the University of California, Irvine, said in a statement. According to Baldi, the latest development could herald a new generation of artificial intelligence (AI) deep-learning systems which are more advanced than those used in commercially available applications such as Siri and Alexa. "These systems are not really intelligent; they're brittle, and you can easily break or fool them," Baldi said.

"These characteristics are shared by many other problems in robotics and other domains that require some kind of planning," added Baldi. "Imagine a robot tasked with cleaning up your kitchen: there is an astronomical number of sequences of moves, but only very few lead to a clean kitchen. And randomly moving dirty dishes around is not going to do it." "More broadly, this work is part of a general effort to bridge machine learning AI and symbolic AI to address complex problems that humans solve through planning and reasoning," added Baldi. In the study, researchers wanted to understand how and why the AI made its moves and how long it took to perfect its method.

Human genes are not continuous but rather consist of short coding regions (exons) interspersed with highly variable non-coding regions (introns). We apply HMMs to the problem of modeling exons, introns and detecting splice sites in the human genome. Our most interesting result so far is the detection of particular oscillatory patterns, with a minimal period ofroughly 10 nucleotides, that seem to be characteristic of exon regions and may have significant biological implications.

Human genes are not continuous but rather consist of short coding regions (exons) interspersed with highly variable non-coding regions (introns). We apply HMMs to the problem of modeling exons, introns and detecting splice sites in the human genome. Our most interesting result so far is the detection of particular oscillatory patterns, with a minimal period ofroughly 10 nucleotides, that seem to be characteristic of exon regions and may have significant biological implications.