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Изворен канал @pythonotes · Post #61 · 2 апр.

Ранее я уже упоминал о другой фишке из ˍˍfutureˍˍ , это оператор деления. from __future__ import division Суть проста. Раньше сложность типа данных результата поределялась типом самого сложного операнда. Например: int/int => int int/float => float В первом случае оба операнда int, значит и результат будет int. Во втором float более сложный тип, поэтому результат будет float. Если нам требуется получить дробное значение при делении двух int то приходилось форсированно один из операндов конверировать в float. 12/float(5) => float Но с новой "философией" это не требуется. В Python3 "floor division" заменили на "true division" а старый способ теперь работает через оператор "//". >>> 3/2 1.5 >>> 3//2 1 То есть теперь деление int на int даёт float если результат не целое число. В классах теперь доступны методы __floordiv__() и __truediv__() для определения поведения с этими операторами. Данный переход описан в PEP238. #pep#2to3#basic

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AI & Law

@ai_and_law · Post #147 · 25.10.2023 г., 07:04

Proposed Chinese AI Safety Standards: A Closer Look Hey there, AI & Law community! On October 11, the National Information Security Standardization Technical Committee in China released a draft document outlining precise regulations for evaluating generative AI models. Unlike the often vague AI regulations, this document provides a clear blueprint for compliance. This standards proposal sets forth rigorous criteria for assessing AI data sources and their content. The document covers topics like training data diversity, moderation, and prohibited content. It emphasizes the need for diversified training corpora and the assessment of data quality. If more than 5% of data is "illegal and negative information," the corpus is flagged for future training. The proposal also suggests that AI companies employ moderators to enhance generated content quality, aligning with national policies and third-party complaints. This implies a potential expansion of the human-driven moderation and censorship workforce in the AI era. Companies are tasked with identifying hundreds of keywords for flagging unsafe or banned content, with separate categories for political and discriminative content. They must also generate more than 2,000 prompts, ensuring fewer than 10% of responses breach the rules. Interestingly, the document encourages subtler censorship measures, such as not refusing to answer sensitive prompts but allowing AI models to respond to specific, non-sensitive inquiries. It's crucial to clarify that these standards are not laws, and non-compliance doesn't result in penalties. However, proposals like these can significantly influence future regulations or work alongside them. The standards receive input from tech experts hired by companies, giving corporations like Huawei, Alibaba, and Tencent a say in shaping these regulations. Their influence could have far-reaching implications for the global AI industry and how AI technologies are regulated worldwide. #AISafety#AIRegulations#GenerativeAI#ContentModeration#ChineseTech#AIInfluence#GlobalAI