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  2. Codecademy - Wikipedia

    en.wikipedia.org/wiki/Codecademy

    Code Year was a free incentive Codecademy program intended to help people follow through on a New Year's Resolution to learn how to program, by introducing a new course for every week in 2012. [32] Over 450,000 people took courses in 2012, [33] [34] and Codecademy continued the program into 2013. Even though the course is still available, the ...

  3. Deep learning super sampling - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_super_sampling

    The data DLSS 2.0 collects includes: the raw low-resolution input, motion vectors, depth buffers, and exposure / brightness information. [13] It can also be used as a simpler TAA implementation where the image is rendered at 100% resolution, rather than being upsampled by DLSS, Nvidia brands this as DLAA (deep learning anti-aliasing). [24]

  4. Grammar checker - Wikipedia

    en.wikipedia.org/wiki/Grammar_checker

    As of 2019, grammar checkers are built into systems like Google Docs and Sapling.ai, [6] browser extensions like Grammarly and Qordoba, desktop applications like Ginger, free and open-source software like LanguageTool, [7] and text editor plugins like those available from WebSpellChecker Software.

  5. Google DeepMind’s new Veo 2 AI video generator trounces ...

    www.aol.com/finance/google-deepmind-veo-2-ai...

    Just seven months after it unveiled its Veo AI video generator, Alphabet division Google DeepMind has announced Veo 2.. The new tool can generate videos of up to 4K resolution, whereas the first ...

  6. Artificial general intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_general...

    They define five levels of AGI: emerging, competent, expert, virtuoso, and superhuman. For example, a competent AGI is defined as an AI that outperforms 50% of skilled adults in a wide range of non-physical tasks, and a superhuman AGI (i.e. an artificial superintelligence) is similarly defined but with a threshold of 100%.

  7. Probably approximately correct learning - Wikipedia

    en.wikipedia.org/wiki/Probably_approximately...

    In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant . [ 1 ]

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