If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. After HRP-1, HRP-2, HRP-3, HRP-4, and HRP-4C, Japan's AIST is finally ready to unveil HRP-5P. Looks very impressive, and we'll be getting more details at IROS next week.
A self-taught artificial intelligence (AI) system called DeepCube has mastered solving the Rubik's Cube puzzle in just 44 hours without human intervention. The system's inventors have detailed their design in a paper titled'Solving the Rubik's Cube Without Human Knowledge'. "A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision," write the paper's authors. "Indeed, if we're ever going to achieve a general, human-like machine intelligence, we'll have to develop systems that can learn and then apply those learnings to real-world applications." While many AI systems have been taught to play games, mastering the complexity of a Rubik's Cube posed a unique set of challenges.
Two algorithms, collectively called Deep Cube, typically can solve the 3-D combination puzzle within 30 moves, which is less than or equal to systems that use human knowledge, according to the research paper. Less than 5.8% of the world's population can solve the Rubik's Cube, according to the Rubik's website.
Anyone who has lived through the 1980s knows how maddeningly difficult it is to solve a Rubik's Cube, and to accomplish the feat without peeling the stickers off and rearranging them. Apparently the six-sided contraption presents a special kind of challenge to modern deep learning techniques that makes it more difficult than, say, learning to play chess or Go. That used to be the case, anyway. Researchers from the University of California, Irvine, have developed a new deep learning technique that can teach itself to solve the Rubik's Cube. What they come up with is very different than an algorithm designed to solve the toy from any position.
Incredibly, the system learned to dominate the classic 3D puzzle in just 44 hours and without any human intervention. "A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision," write the authors of the new paper, published online at the arXiv preprint server. Indeed, if we're ever going to achieve a general, human-like machine intelligence, we'll have to develop systems that can learn and then apply those learnings to real-world applications. Recent breakthroughs in machine learning have produced systems that, without any prior knowledge, have learned to master games like chess and Go. But these approaches haven't translated very well to the Rubik's Cube.
OK, let's break this down. The Rubik's Cube is pretty difficult, right? But you'd imagine it might be pretty easy for an artificial intelligence to break down and solve consistently, right? Creating an algorithm that can solve the Rubik's Cube is relatively simple -- the kind of algorithms that allow AI to beat humans at chess or Go or even DOTA 2! But creating a machine that can solve the Rubik's Cube without algorithms hand-crafted by human beings? Stephen McAleer and his colleagues at the University of California think they have solved the problem, with a process called "autodidactic iteration".
Deep-learning machines have figured out how to master games like chess or Mortal Kombat. Now, computer scientists at the University of California, Irvine taken things to the third dimension by creating an algorithm that can figure out how to solve a Rubik's Cube, a surprisingly difficult change. "Our algorithm is able to solve 100 percent of randomly scrambled cubes while achieving a median solve length of 30 moves - less than or equal to solvers that employ human domain knowledge," say the scientists in the abstract to their paper, up on Arvix. The algorithm, called DeepCube, uses what's known as "autodidactic iteration," a form of machine learning developed by the authors of the paper. The big challenge of autodidactic iteration was to allow machines to find their own rewards in solving a puzzle, a goal they can reach.
The Rubik's Cube is a three-dimensional puzzle developed in 1974 by the Hungarian inventor Erno Rubik, the object being to align all squares of the same color on the same face of the cube. It became an international best-selling toy and sold over 350 million units. The puzzle has also attracted considerable interest from computer scientists and mathematicians. One question that has intrigued them is the smallest number of moves needed to solve it from any position. The answer, proved in 2014, turns out to be 26.