An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence. Save this story Save this story Even the smartest artificial intelligence ...

Abstract: Convolution neural networks (CNNs) have been extensively used in machine learning applications. The most time-consuming part of CNNs are convolution operations. A common approach to ...

CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...

The Wachowskis once asked Hideo Kojima to make a Matrix game, but Konami said no, and history ended up taking a very different turn. By the late 1990s, Hideo Kojima was one of Konami’s most valuable ...

Clevert, D.-A., Untertiner, T., and Hochreiter, S. (2016). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv [Preprint]. Available ...

In case you've faced some hurdles solving the clue, Repetitive learning method, we've got the answer for you. Crossword puzzles offer a fantastic opportunity to engage your mind, enjoy leisure time, ...

In this episode, Xiaomanyc shares his approach to learning languages quickly and naturally. Known for surprising strangers with their own languages, he breaks down the simple habits and mindsets that ...

High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, traditional methods like the R-ratio ...

Purpose: This study aimed to explore the effect of the order of two learning methods (one based on implicit and another on explicit learning) on students' enjoyment and ability to acquire motor skills ...

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