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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.

 The work begs the question of whether this “self-correction” could and should be built into language models from the beginning.

The third examined the impact of race on the likelihood that a prospective candidate would be accepted to a law school if a language model were used to make the selection.and Gemini won hands down How to use ChatGPTapos;s file analysis capability (and what it can do for you) I tried Copilot Notebook: Microsoftapos;s new AI tool offers two handy prompt features What to know about Mistral AI: The company behind the latest GPT-4 rival I asked Gemini and GPT-4 to explain deep learning AI.

Our pretraining dataset uses data from web documents.although not audio (unlike Gemini 1.

They continue: We believe that our released model will provide a foundation for future work on developing longer context models.it appears possible that Liu and team were able to achieve results equivalent to Gemini 1.