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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 #732 · 26.12.2025 г., 08:04

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🇬🇧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

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AI & Law

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

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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

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