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Изворен канал @pythonotes · Post #402 · 22 дек.

Отдельно разберём TaskGroup, который пришел на замену gather в Python 3.11. Ключевые отличия ▫️create_task() возвращает объект asyncio.Task, у которого есть соответствюущие методы управления. То есть у нас больше контроля ▫️это контекстный менеджер, который гарантирует что все таски будут остановлены по выходу из контекста ▫️ошибка автоматически отменяет незавершенные задачи, ▫️except* передает нам ExceptionGroup, в котором каждую ошибку можно обработать отдельно import asyncio import random async def do_it() -> str: if random.random() < 0.1: raise ValueError('Oops') delay = random.uniform(0.5, 1.5) await asyncio.sleep(delay) return delay async def main(): try: async with asyncio.TaskGroup() as tg: for _ in range(10): tasks.append(tg.create_task(do_it())) for t in tasks: print(t.result()) except *ValueError as e: for err in e.exceptions: print(err) asyncio.run(main()) Рекомендую изучить страницу Coroutines and Tasks из документации, где представлено больше интересных примеров и механизмов - таймауты - отмена задач - создание задач из другого потока #async

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AI & Law

@ai_and_law · Post #428 · 25.10.2024 г., 07:04

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NYDFS Issues Guidance on AI-Related Cybersecurity Risks The New York Department of Financial Services (NYDFS) released guidance highlighting the rising cybersecurity risks associated with the use of artificial intelligence by its licensees, including insurers and virtual currency businesses. The guidance focuses on threats such as AI-enabled social engineering, where deepfakes and other AI tools are used to obtain sensitive information and bypass biometric security measures. It also addresses the growing concern over AI-enhanced cyberattacks that increase the potency, scale, and speed of threats, as well as the risk of exposure or theft of vast amounts of nonpublic data. The guidance emphasizes the critical need for organizations to integrate AI-specific considerations into their existing risk assessments, third-party vendor management, and data management practices. While the NYDFS guidance is aimed at businesses under its regulation, the outlined risks and mitigation strategies are applicable to any organization navigating the complexities of AI-related cybersecurity. With the proliferation of AI technology, businesses must prioritize not only the protection of personally identifiable information but also safeguard confidential business information like trade secrets, which can have a more significant impact if compromised. The guidance reinforces the importance of robust due diligence when working with third-party vendors that use or provide AI solutions, as well as the necessity of maintaining effective data inventory and minimization practices. #Cybersecurity#AICompliance#NYDFS#RiskManagement#AIRegulation

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