"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.
In recent years, a growing number of researchers have explored the use of robotic arms or dexterous hands to solve a variety of everyday tasks. While many of them have successfully tackled simple tasks, such as grasping or basic manipulation, complex tasks that involve multiple steps and precise/strategic movements have so far proved harder to address. A team of researchers at the Chinese University of Hong Kong and Tencent AI Lab has recently developed a deep learning-based approach to solve a Rubik's Cube using a multi-fingered dexterous hand. Their approach, presented in a paper pre-published on arXiv, allows a dexterous hand to solve more advanced in-hand manipulation tasks, such as the renowned Rubik's Cube puzzle. A Rubik's Cube is a plastic cube covered in multi-colored squares that can be shifted into different positions.
Creating an autonomous system to make scientific discoveries at a Nobel Prize level by 2050. With the system, AI would formulate hypotheses from enormous amounts of existing experimental data, and robots would conduct experiments to prove them. Achieving artificial hibernation technology by 2050, to help extend healthy human life spans.
Humans can manipulate Rubik's cubes with relative ease, but robots have historically had a tougher go of it. That's not to suggest there aren't exceptions to the rule -- an MIT invention recently solved a cube in a record-breaking 0.38 seconds -- but they typically involve purpose-built motors and controls. Encouragingly, a group of researchers at Tencent and the Chinese University of Hong Kong say they've designed a Rubik's cube manipulator that uses multi-fingered hands. "Dexterous in-hand manipulation is a key building block for robots to achieve human-level dexterity, and accomplish everyday tasks which involve rich contact," wrote the researchers. "Despite concerted progress, reliable multi-fingered dexterous hand manipulation has remained an open challenge, due to its complex contact patterns, high dimensional action space, and fragile mechanical structure."
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.
What do we mean when we say'context'? In essence, context is the information that frames something to give it meaning. Taken on its own, a shout could be anything from an expression of joy to warning. In the context of a structured piece of on-stage Grime, it's what made Stormzy's appearance at Glastonbury the triumph it was. The problem is that context doesn't come free – it has to be discovered.
"The solution to the Rubik's Cube involves more symbolic, mathematical and abstract thinking, so a deep learning machine that can crack such a puzzle is getting closer to becoming a system that can think, reason, plan and make decisions." An expert system designed for a narrow task, such as only solving a Rubik's Cube will forever be limited to that domain. But a system like DeepCubeA, boasting an adaptable neural net, can be used for other tasks, such as solving complex scientific, mathematical, and engineering problems. Stephen McAleer, a co-author of the new paper, told Gizmodo how this system "is a small step toward creating agents that are able to learn how to think and plan for themselves in new environments." Reinforcement learning works the way it sounds.
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.
The human record for solving a Rubik's Cube has been smashed by an artificial intelligence. The bot, called DeepCubeA, completed the popular puzzle in a fraction of a second - much faster than the quickest humans. While algorithms have previously been developed specifically to solve the Rubik's Cube, this is the first time it has done without any specific domain knowledge or in-game coaching from humans. It brings researchers a step closer to creating an advanced AI system that can think like a human. "The solution to the Rubik's Cube involves more symbolic, mathematical and abstract thinking," said senior author Professor Pierre Baldi, a computer scientist at the University of California, Irvine.
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.