"Search is a problem-solving technique that systematically explores a space of problem states, i.e., successive and alternative stages in the problem-solving process. Examples of problem states might include the different board configurations in a game or intermediate steps in a reasoning process. This space of alternative solutions is then searched to find an answer. Newell and Simon (1976) have argued that this is the essential basis of human problem solving. Indeed, when a chess player examines the effects of different moves or a doctor considers a number of alternative diagnoses, they are searching among alternatives."
– from Section 1.2 of Chapter One of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005).
"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.
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.
An artificial intelligence system created by researchers at the University of California has solved the Rubik's Cube in just over a second. DeepCubeA, as the algorithm was called, completed the 3D logic puzzle which has been taxing humans since it was invented in 1974. "It learned on its own," said report author Prof Pierre Baldi. The researchers noted that its strategy was very different from the way humans tackle the puzzle. "My best guess is that the AI's form of reasoning is completely different from a human's," said Prof Baldi, who is professor of computer science at University of California, Irvine.
A deep-learning algorithm has been developed which can solve the Rubik's cube faster than any human can. It never fails to complete the puzzle, with a 100 per cent success rate and managing it in around 20 moves. Humans can beat the AI's mark of 18 seconds, the world record is around four seconds, but it is far more inefficient and people often require around 50 moves. It was created by University of California Irvine and can be tried out here. Given an unsolved cube, the machine must decide whether a specific move is an improvement on the existing configuration.
"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.
Among the many achievements of machine learning in recent years, some of the most striking are the victories of the machine against human players in games, such as Google's DeepMind group's conquest of Go in 2016. In such milestones, researchers are often guided by theoretical math that says there can be an optimal strategy to be found, given a good algorithm and enough compute. But what do you do when theory breaks down? Two researchers at Carnegie Mellon University and Facebook went back to the drawing board to solve "heads-up no-limit Texas hold'em," the most popular form of multiplayer poker in the world. Theory isn't computable for this form of the card game, so they designed some elegant search strategies for their computer program, "Pluribus," to beat the best human players in 10,000 hands of poker.
To succeed in machine learning, we must do a decent amount of prep work. Just adding data, data, data can lead to false signals and invalid correlations. We can end up missing the signal in all the noise. In "Why Machine Learning Works," computer scientist George Montañez walks the reader through the prerequisites for successful machine learning. He notes that, at its core, machine learning is a form of search algorithm.
When building a deep learning project the most common problem we all face is choosing the correct hyperparameters (often known as optimizers). This is critical as the hyperparameters determine the expertise of the machine learning model. In Machine Learning (ML hereafter), a hyperparameter is a configuration variable that's external to the model and whose value is not estimated from the data given. Hyperparameters are an essential part of the process of estimating model parameters and are often defined by the practitioner. When an ML algorithm is used for a specific problem, for example when we are using a grid search or a random search algorithm, then we are actually tuning the hyperparameters of the model to discover the values that help us to achieve the most accurate predictions.