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We couldnt test the keyboard for long.

Informed by this characterization we propose a method of using Gaussian mixture models to extract the clusters of the design space that map to these different behaviors.The team made the robot arm construct and fly 500 paper planes to observe the true probabilistic and stochastic nature of the flight behavior.

meaning that the paper planes had a longer flight distance than those that nose-dived and glided in the air for a bit.The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory forms and flight distances.This was the result of a study by a group of engineers from the Swiss Federal Institute of Technology Lausanne (EPFL).

the design space can be characterized and explored… we demonstrate how developing these models can be used to accelerate real-world robotic optimization of a design—to identify wing shapes that fly a given distance.See Also Paper planes aren’t just a child’s toy; we can learn a lot about aerial vehicle design by modelling & optimizing their unique & unpredictable flight behavior.

when they decided to build a robotic arm that could fly paper planes to test the trajectory of these flying objects

 To incorporate this “self-correction” in language models without the need to prompt them.as well as encourage more challenging benchmarks that contain difficult long-range tasks that require higher levels of synthesis.

which is at the heart of the controversy over copyright infringement.which can handle as many as 10 million tokens in its input.

running on less computing power -- able to achieve similar results to Gemini 1.which is a process the researchers call image chat.