Work is step toward advanced AI systems that can think, reason, plan and make decisions
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 3D 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.
“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. “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.”
The researchers were interested in understanding how and why the AI made its moves and how long it took to perfect its method. They started with a computer simulation of a completed puzzle and then scrambled the cube. Once the code was in place and running, DeepCubeA trained in isolation for two days, solving an increasingly difficult series of combinations.
“It learned on its own,” Baldi noted.
There are some people, particularly teenagers, who can solve the Rubik’s Cube in a hurry, but even they take about 50 moves.
“Our AI takes about 20 moves, most of the time solving it in the minimum number of steps,” Baldi said. “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 veteran computer scientist said the ultimate goal of projects such as this one is to build the next generation of AI systems. Whether they know it or not, people are touched by artificial intelligence 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 said. “How do we create advanced AI that is smarter, more robust and capable of reasoning, understanding and planning? This work is a step toward this hefty goal.”
The Latest on: Deep learning algorithm
via Google News
The Latest on: Deep learning algorithm
- Reinforcement Learning: The Algorithms Changing How Computers Make Decisionson March 22, 2020 at 12:49 am
My prediction that in the 2020s, we shall see this inequity broken down. This shall be due to the rise of Deep Reinforcement Learning (RL) as a prominent algorithm for such problems. RL, in essence, ...
- Top 6 Baselines For Reinforcement Learning Algorithms On Gameson March 21, 2020 at 12:33 am
Games like chess, GO, and Atari have become testbeds of testing deep reinforcement learning algorithms. Companies like DeepMind and OpenAI have done a tremendous amount of research into this field and ...
- Learning to synthesize: Robust phase retrieval at low photon countson March 20, 2020 at 8:31 am
Previous deep learning-based algorithms did improve over traditional methods under low light conditions, but displayed a tendency to over-represent the low spatial frequencies in the reconstructions, ...
- Deep Learning Predicts Stroke-Lesion Changes at 1 Weekon March 20, 2020 at 6:47 am
A deep learning algorithm is comparable or even superior to common clinical measures for predicting infarct size and location up to a week following acute ischemic stroke, new research suggests.
- A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissueon March 20, 2020 at 3:12 am
While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in ...
- Synergy emergence in deep reinforcement motor learningon March 19, 2020 at 6:15 am
Now, researchers at Tohoku University have observed a similar concept in robotic agents using deep reinforcement learning (DRL) algorithms. DRL allows robotic agents to learn the best action possible ...
- Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectureson March 19, 2020 at 3:08 am
Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few ...
- BADGR uses deep learning to plan out and traverse obstacle-free pathson March 17, 2020 at 11:22 pm
Projects and researches in the past have shown that deep learning is a potent technique for training robots to do specific things. To name a few, we've seen OpenAI use neural networks to train Dactyl ...
- Deep Learning and A.I have Multiple Applications in Finance and Financial Sector, says Deltec Bank, Bahamason March 13, 2020 at 6:45 pm
“Deltec Bank” Deltec Bank – Deep Learning and A.I have Multiple Applications in Finance and Financial Sector Deep learning is an extension to the ...
- Deep Learning Market – overview and scope, Industry Outlook, Size & Forecast 2020-2025on March 11, 2020 at 11:17 pm
New York, March 12, 2020: The scope of the report includes a detailed study of Deep Learning Market with the reasons given for variations in the growth of the industry in certain regions. The Deep ...
via Bing News