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Изворен канал @pythonotes · Post #32 · 7 фев.

Скорее всего уже слышали, что складывать строки через + это плохая практика. Падение производительности, и всё такое. Без лишних слов, давайте измерять: from timeit import timeit def t1(): # складываем 10 строк через + из переменной t = 'text' for _ in range(1000): s = t + t + t + t + t + t + t + t + t def t2(): # склеиваем список строк через метод join arr = ['text'] * 10 for _ in range(1000): s = ''.join(arr) def t3(): # складываем через + но не из переменной а непосредственно инлайн объекты for _ in range(1000): s = 'text' + 'text' + 'text' + ... # всего 10 раз Теперь каждую строку склейки запустим по 10М раз >>> timeit(t1, number=10000) 0.21951690399964718 >>> timeit(t2, number=10000) 1.4978306379998685 >>> timeit(t3, number=10000) 0.2213820789993406 Хм, а нам говорили что через "+" это плохо и медленно ))) 😁 Тут стоит учитывать, что речь идёт о склейке множества длинных строк. Давайте изменим условия: def t4(): t = 'text'*100 for _ in range(1000): s = t + t + t + t + t + t + t + t + t def t5(): arr = ['text'*100] * 10 for _ in range(1000): s = ''.join(arr) def t6(): for _ in range(1000): s = 'text'*100 + 'text'*100 + ... # всего 10 раз >>> timeit(t4, number=10000) 12.795130728000004 >>> timeit(t5, number=10000) 2.642637542999182 >>> timeit(t6, number=10000) 0.2184546610005782 Вот, уже другой разговор, сразу видна разница, в среднем в 6 раз. Но погодите, почему последний тест t6() по скорости такой же как и t3()? Ведь строки теперь в 100 раз длиннее! Это вопросы оптимизации кода, какие простые изменения ускоряют или замедляют выполнение программы. Мы столкнулись с примером обхода обращения к переменной. Например, именно так работает директива #define в С++, во время компиляции подставляя значение переменной вместо ссылки на неё. В Python это тоже работает, но часто ли вы сможете встретить такой способ работы со строками? К сожалению, способ почти только теоретический. В целом, тесты показали то, что мы хотели. Делаем выводы самостоятельно. Полный листинг 🌍 #tricks

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AI & Law

@ai_and_law · Post #174 · 27.11.2023 г., 08:04

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OpenAI Faces Fifth Copyright Lawsuit Over AI Models Greetings AI & Law Community! OpenAI finds itself entangled in a fifth copyright lawsuit, with Microsoft also implicated. Filed by US writer Julian Sancton, the lawsuit alleges a blatant disregard for copyrights in training AI models, specifically ChatGPT. Julian Sancton, a New York Times best-selling author, spent five years and extensive resources researching and writing "Madhouse at the End of the Earth." This lawsuit represents him and other writers who feel their work has been misused. The crux of the matter is OpenAI and Microsoft using copyrighted works without compensating or seeking permission from authors. The lawsuit contends that these companies not only reproduced millions of works but also built a model that mimics the style and themes of these copyrighted materials. Unlike previous lawsuits against OpenAI, this one includes Microsoft as a defendant. The claim suggests a close collaboration between the two companies in creating and monetizing AI models like GPT-3 and GPT-4, which underpin various Microsoft services. The plaintiffs demand compensation for their work and seek a court order prohibiting OpenAI and Microsoft from training their models by infringing on copyrighted material. This marks the fifth lawsuit against OpenAI by writers, emphasizing a growing legal concern within the AI community. #AILaw#CopyrightLawsuit#OpenAI#Microsoft#AICommunity

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AI & Law

@ai_and_law · Post #273 · 29.03.2024 г., 08:04

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Bloomberg Asserts Fair Use Defense in AI Copyright Lawsuit Bloomberg LP has moved to dismiss a lawsuit from Arkansas governor Mike Huckabee and other authors, arguing that its use of copyrighted works for AI research falls within the bounds of fair use. The authors, including best-selling Christian writer Lysa TerKeurst, allege that Bloomberg misused their books to train its AI system without permission. Bloomberg contends that the authors' claims lack specificity regarding infringement and which books were utilized for BloombergGPT, describing the system as an internal research project. In its filing, Bloomberg emphasized that its use of copyrighted material was limited, private, and not for commercial purposes, asserting that such use does not constitute copyright infringement. The lawsuit is part of a broader trend where copyright holders challenge tech companies over alleged misuse of content for training AI models. Bloomberg's fair use defense is expected to be pivotal in this dispute. #Bloomberg#CopyrightLawsuit#FairUse#AIResearch

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AI & Law

@ai_and_law · Post #161 · 10.11.2023 г., 08:04

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Artist Lawsuit Over AI Image Generators Faces Challenges Hello, dear subscribers! In the ongoing lawsuit against AI image generators by artists, several claims were dismissed by US district Judge William H. Orrick. Notably, two of the three plaintiffs in the case had not registered their disputed works with the Copyright Office, leading to the dismissal of their claims. However, the lead plaintiff, Sarah Andersen, has 30 days to amend her complaint and continue the copyright dispute. The artists allege that AI image generators infringe on their copyrights, claiming direct and vicarious infringement, violations of the Digital Millennium Copyright Act, and California laws related to unfair competition and rights to publicity. They argue that AI-generated images should be considered derivative works based on copyrighted content. While the case proceeds, the judge has called for clarification on how image generators work, especially regarding "compressed copies" of images and the operation of AI systems like Stable Diffusion. The lawsuit remains complex, with the core claim of direct copyright infringement proceeding against Stability AI but not against DeviantArt and Midjourney. The artists will need to address the many issues identified in their claims, including specifics regarding copyright management information and rights of publicity. #AIImageGenerators#CopyrightLawsuit#AIandArt#LegalAction

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