Robots with truly humanlike dexterity are far from becoming reality, but progress accelerated by AI has brought us closer to achieving this vision than ever before. In a research paper published in September, a team of scientists at Google detailed their tests with a robotic hand that enabled it to rotate Baoding balls with minimal training data. And at a computer vision conference in June, MIT researchers presented their work on an AI model capable of predicting the tactility of physical things from snippets of visual data alone. Now, OpenAI -- the San Francisco-based AI research firm cofounded by Elon Musk and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman -- says it's on the cusp of solving something of a grand challenge in robotics and AI systems: solving a Rubik's cube. Unlike breakthroughs achieved by teams at the University of California, Irvine and elsewhere, which leveraged machines tailor-built to manipulate Rubik's cubes with speed, the approach devised by OpenAI researchers uses a five-fingered humanoid hand guided by an AI model with 13,000 years of cumulative experience -- on the same order of magnitude as the 40,000 years used by OpenAI's Dota-playing bot.
The IJCAI-09 Workshop on Learning Structural Knowledge from Observations (STRUCK-09) took place as part of the International Joint Conference on Artificial Intelligence (IJCAI-09) on July 12 in Pasadena, California. The workshop program included paper presentations, discussion sessions about those papers, group discussions about two selected topics, and a joint discussion. As a result, many cognitive architectures use structural models to represent relations between knowledge of different complexity. Structural modeling has led to a number of representation and reasoning formalisms including frames, schemas, abstractions, hierarchical task networks (HTNs), and goal graphs among others. These formalisms have in common the use of certain kinds of constructs (for example, objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations.
We propose that a planner should be provided with an explicit model of its own planning mechanism, and show that linking a planner's expectations about the performance of its plans to such a model, by means of explicit justification structures, enables the planner to determine which aspects of its planning are responsible for observed performance failures.
A robot has just set a new record for the fastest-solved Rubik's Cube, according to its makers. The Sub1 Reloaded robot took just 0.637 seconds to analyse the toy and make 21 moves, so that each of the cube's sides showed a single colour. That beats a previous record of 0.887 seconds, which was achieved by an earlier version of the same machine using a different processor. Infineon provided its chip to highlight advancements in self-driving car tech. But one expert has questioned the point of the stunt.
BPS, the Bayesian Problem Solver, applies probabilistic inference and decision-theoretic control to flexible, resource-constrained problem-solving. This paper focuses on the Bayesian inference mechanism in BPS, and contrasts it with those of traditional heuristic search techniques. By performing sound inference, BPS can outperform traditional techniques with significantly less computational effort. Empirical tests on the Eight Puzzle show that after only a few hundred node expansions, BPS makes better decisions than does the best existing algorithm after several million node expansions