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Изворен канал @pythonotes · Post #309 · 2 фев.

Метод строки split() разделяет строку на несколько строк по указанному символу >>> "a_b_c".split('_') ['a', 'b', 'c'] Можно указать максимальное количество разделений >>> "a_b_c".split('_', 1) ['a', 'b_c'] Или резать с другой стороны с помощью rsplit() (right split) >>> "a_b_c".rsplit('_', 1) ['a_b', 'c'] А что будет если оставить аргументы пустыми? >>> "a_b_c".split() ['a_b_c'] Получаем список с одним элементом, потому что по умолчанию используется пробельный символ. >>> "a b c".split() ['a', 'b', 'c'] То есть это равнозначно такому вызову? >>> "a b c".split(" ") ['a', 'b', 'c'] Кажется да, но нет! Давайте попробуем добавить пробелов между буквами >>> "a b c".split(" ") ['a', '', '', 'b', '', '', 'c'] И вот картина уже не так предсказуема 😕 А вот что будет по умолчанию >>> "a b c".split() ['a', 'b', 'c'] Всё снова красиво! 🤩 По умолчанию в качестве разделителя используется любой пробельный символ, будь то табуляция или новая строка. Включая несколько таких символов идущих подряд. А также игнорируются пробельные символы по краям строки. >>> "a\t b\n c ".split() ['a', 'b', 'c'] Аналогичный способ можно собрать с помощью регулярного выражения. Но пробелы по краям строки придется обрабатывать дополнительно. >>> import re >>> re.split(r"\s+", ' a b c '.strip()) ['a', 'b', 'c'] Здесь тоже можно указать количество разделений >>> re.split(r"\s+", 'a b c', 1) ['a', 'b c'] А что если мы хотим написать красиво, то есть split() без аргументов, но при этом указать количество разделений? В этом случае первым аргументом передаём None >>> "a\n b c".split(None, 1) ['a', 'b c'] Данный метод не учитывает строки с пробелами, взятые в кавычки 'a "b c" '.split() ['a', '"b', 'c"'] Но для таких случаев есть другие способы. #tricks#basic

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AI & Law

@ai_and_law · Post #732 · 26.12.2025 г., 08:04

🇬🇧UK Publishes First Evidence-Based Assessment of Frontier AI Capabilities The UK AI Security Institute released its inaugural "Frontier AI Trends Report", presenting a public, data-driven assessment of how the most advanced AI systems are evolving. Based on two years of testing across cyber security, software engineering, biology, and chemistry, the report provides quantified evidence on AI capabilities, replacing speculation with measurable benchmarks. The findings show rapid capability growth. In cyber security, success on apprentice-level tasks rose from under 9% in 2023 to about 50% in 2025, and for the first time a model completed an expert-level task requiring up to 10 years of experience. In software engineering, models now complete hour-long tasks over 40% of the time, up from below 5% two years ago. In biology and chemistry, systems outperform PhD-level researchers on knowledge tests and enable non-experts to conduct advanced lab work. Safeguards are improving but remain imperfect. The time needed to discover a “universal jailbreak” increased from minutes to several hours between model generations, around a 40-fold improvement, though all tested systems remain vulnerable to some bypasses. The report makes no policy recommendations, but aims to improve transparency and inform regulators and policymakers globally about what frontier AI systems can actually do. #AIRegulation#AISafety#UKAI#FrontierAI#AIGovernance#TechPolicy

AI & Law

@ai_and_law · Post #153 · 31.10.2023 г., 08:04

UK Government Unveils Report on Frontier AI Risks Hello AI & Law community! UK Prime Minister Rishi Sunak has issued a report to address AI's potential risks and harness its benefits. The report focuses on the rapid advancements in frontier AI and comprises three key sections: 1️⃣Capabilities and Risks from Frontier AI: This section discusses the current state of AI capabilities, potential improvements, and associated risks, including societal harms, misuse, and loss of control. 2️⃣Safety and Security Risks of Generative AI to 2025: It outlines global benefits of generative AI while emphasizing increased safety and security risks, particularly in enhancing threat actor capabilities and the effectiveness of attacks. 3️⃣Future Risks of Frontier AI: This section explores uncertainties in AI development, future system risks, and potential scenarios for AI up to 2030. The report, based on declassified information, raises concerns about generative AI being exploited by terrorists to plan biological or chemical attacks, posing a serious global security threat. Although some experts have questioned the UK Government's approach, the report highlights the need for collaborative measures to manage AI risks. An upcoming AI Safety Summit aims to foster discussions around these challenges, including misuse for cyberattacks or bioweapon design, AI systems acting autonomously, and broader societal impacts. #UKGovernmentAI#FrontierAI#AIRisks#AISafety#AIChallenges#UKAIReport#AIandLaw#AIPolicy#AIRegulation