Japanese toy-maker MegaHouse Corp. said Wednesday it will launch the world's smallest working Rubik's Cube, weighing about 2 grams and measuring 0.99 centimeter on each side. On the same day, the Bandai Namco Holdings Inc. subsidiary started accepting orders for the product online. It is priced at ¥198,000 in Japan, including delivery costs. Delivery will start in late December. The Rubik's Cube, invented by Erno Rubik from Hungary in 1974, hit store shelves across the world in 1980. In Japan, MegaHouse has shipped out over 14 million cubes.
In-person competition is a no-go in many disciplines amid the COVID-19 pandemic, but speed cubers will be still able to battle opponents remotely in the Rubik's Cube World Cup. Rubik's has revealed the Connected Cube, which links to your phone or tablet and tracks your solve times and progress in real-time. It's more of a traditional cube than GoCube, which is largely a STEM-focused toy. Both use the same platform and can connect to the Rubik's Arena community, which has almost 47,000 players. As such, amateur and professional cubers can take part in this year's World Cup without having to travel, as long as they have a Connected Cube or GoCube. Qualifiers start August 15th and run through October 10th.
Speedcubing is the sport of solving a classic Rubik's Cube -- or a related combination puzzle -- in the shortest amount of time possible. And, no, it is not for the faint of heart. The new Netflix documentary on this subject, The Speed Cubers, dives headfirst into the friendly but competitive speedcubing culture. The 40-minute film is one of three new documentary shorts debuting on Netflix this summer. The Speed Cubers centers on a couple of professional competitors who go head-to-head at the World Cube Association World Championship in Melbourne, Australia, in 2019.
In problem solving, the paths towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. Using dimensionality reduction, these trajectories can be visualized in lower dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but so far, almost exclusively the self-similarity of single trajectories has been analyzed. In contrast, we describe patterns emerging from drawing many trajectories---for different initial conditions, end states, or solution strategies---in the same embedding space. We argue that general statements about the problem solving tasks and solving strategies can be made by interpreting these patterns. We explore and characterize such patterns in trajectories resulting from human and machine-made decisions in a variety of application domains: logic puzzles (Rubik's cube), strategy games (chess), and optimization problems (neural network training). In the context of Rubik's cube, we present a physical interactive demonstrator that uses trajectory visualization to provide immediate feedback to users regarding the consequences of their decisions. We also discuss the importance of suitably chosen representation spaces and similarity metrics for the embedding.
Last year we were amazed by the level of dexterity achieved by OpenAI's Dactyl system which was able to learn how to manipulate a cube block to display any commanded side/face.If you missed that article, read about it here. OpenAI then set themselves a harder task of teaching the robotic hand to solve a Rubik's cube. Quite a daunting task made no easier by the fact that it would use one hand which most humans would find it hard to do. OpenAI harnessed the power of neural networks which are trained entirely in simulation. However, one of the main challenges faced was to make the simulations as realistic as possible because physical factors like friction, elasticity etc. are very hard to model.
Over a year ago, OpenAI, the San Francisco–based for-profit AI research lab, announced that it had trained a robotic hand to manipulate a cube with remarkable dexterity. That might not sound earth-shattering. But in the AI world, it was impressive for two reasons. First, the hand had taught itself how to fidget with the cube using a reinforcement-learning algorithm, a technique modeled on the way animals learn. Second, all the training had been done in simulation, but it managed to successfully translate to the real world.
Fox News Flash top headlines for Oct. 24 are here. Check out what's clicking on Foxnews.com The owner of the Rubik's Cube has lost an appeal to regain the European Union trademark rights to the classic puzzle's iconic shape in a new twist to the ongoing legal drama. Rubik's Brand Ltd. lost the protection rights to the puzzle's shape in 2017, after the EU's top court ruled that law prevents the firm from having "a monopoly on technical solutions or functional characteristics of a product," Bloomberg reported. The EU General Court in Luxembourg upheld that decision on Thursday.
OpenAI, a non-profit co-founded by Elon Musk, recently unveiled its newest trick: A robot hand that can'solve' Rubik's Cube. Whether this is a feat of science or mere prestidigitation is a matter of some debate in the AI community right now. In case you missed it, OpenAI posted an article on its blog last week titled "Solving Rubik's Cube With a Robot Hand." Based on this title, you'd be forgiven if you thought the research discussed in said article was about solving Rubik's Cube with a robot hand. Don't get me wrong, OpenAI created a software and machine learning pipeline by which a robot hand can physically manipulate a Rubik's Cube from an'unsolved' state to a solved one.
Once again, a robot can do something I cannot do. Researchers at the artificial intelligence lab OpenAI just revealed that its humanoid robotic hand can solve a Rubik's cube. The researchers utilized a pair of neural networks to make it happen. The team has been working on this project, named Dactyl, since the middle of 2017, and they felt showing their robotic hand could solve a Rubik's cube would show it had adequate dexterity. It can now solve the cube about 60 percent of the time.
Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle.