Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents' actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals.
In 2014, the average SAT test taker correctly answered answered 49 percent of the test's math questions. Today, a new software program is now close to doing the same. In a paper published Monday, researchers at the Allen Institute for Artificial Intelligence (AI2) and the University of Washington revealed that their artificial intelligence (AI) system, known as GeoSolver, or GeoS for short, is able to answer "unseen and unaltered" geometry problems on par with humans. According to a report released by College Board, the average SAT math score in 2014 was 513. Though GeoS has only been tested on geometry questions, if the system's accuracy was extrapolated, GeoS would have scored a 500.
Scientists have revealed an artificial intelligence (AI) system that can solve SAT geometry questions as well as the average American 11th-grade student. Called GeoS, it uses a combination of computer vision to interpret diagrams, natural language processing to read and understand text and a geometric solver to achieve 49 percent accuracy on official SAT test questions. If these results were extrapolated to the entire Math SAT test, the computer achieved an SAT score of 500 (out of 800), the average test score for 2015, the team behind it say. The system uses a combination of computer vision, natural language processing and a geometric solver to achieve 49 percent accuracy on official SAT test questions. GeoS is the first end-to-end system that solves SAT plane geometry problems.
Protein design typically selects a protein topology and then identifies the geometries (secondary-structure lengths and orientations) that give the most stable structures. A challenge for this approach is that functional sites in natural proteins often adopt nonideal geometries. Pan et al. addressed this issue by exploring the diversity of geometries that can be sampled by a given topology. They developed a computational method called LUCS that systematically samples geometric variation in loop-helix-loop elements and applied it to two different topologies. This method generated families of well-folded proteins that include structures with non-native geometries. The ability to tune protein geometry may enable the custom design of new functions. Science , this issue p.  Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature’s ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions. : /lookup/doi/10.1126/science.abc0881
A small group of photographers have turned their lenses on the urban landscape, seeking to capture the beauty of the architecture around us. The images explore the idea of sacred geometries, the perfect mix of proportion and mathematical ratios that are pleasing to the eye and a reflection of those found in nature. The pictures can be seen at the Anise Gallery in London until 15 April 2017.