人工智能安全治理框架 1.0(b)训练数据含不当内容、被 “投毒” 风险。训练数据中含有虚假、偏见、 侵犯知识产权等违法有害信息,或者来源缺乏多样性,导致输出违法的、不良 的、偏激的等有害信息内容。训练数据还面临攻击者篡改、注入错误、误导数 据的“投毒”风险,“污染”模型的概率分布,进而造成准确性、可信度下降。 (c)训练数据标注不规范风险。训练数据标注过程中,存在因标注规则 不完备、标注人员能力不够、标注错误等问题,不仅会影响模型算法准确度、0 码力 | 20 页 | 3.79 MB | 1 月前3
【周鸿祎清华演讲】DeepSeek给我们带来的创业机会-360周鸿祎-202502大模型不是泡沫,而是新一轮工业革命的驱动引擎 蒸汽革命 电气革命 信息革命 以大模型为代表的 人工智能革命 人工智能是新质生产力的关键支撑技术,人工智能+百业千行将带动新一轮工业革命,为高质量发展注入强大动能 大模型的进一步突破将引领人类社会进入智能化时代,对我们的生活方式、生产方式带来巨大变革 重塑经济图景 解决复杂问题 7政企、创业者必读 8 AI不仅是技术革新,更是思维方式和社会结构的变革0 码力 | 76 页 | 5.02 MB | 5 月前3
Real-Time Unified Data Layers:
A New Era for Scalable Analytics,
Search, and AIacross distributed systems. High-performance querying for analytics, search, and AI workloads at scale. SQL simplicity to unify access across divers data types, reducing complexity in querying distributed datasets execute complex queries on very large data sets in the sub-second range. All with the simplicity of SQL. Optimizing for AI & Search is Difficult Optimizing for both structured analytics, full-text search complexity, storage costs, and maintenance overhead by consolidating disparate systems. The native SQL support also makes it simple to use as it provides a single and easy way to query the data. 4. Why0 码力 | 10 页 | 2.82 MB | 5 月前3
DevOps Meetuptechnology under the sun Solaris, Windows, Linux Apache, IIS, TCServer, etc. Oracle, DB2, SQL Server How we got better We read and we studied. Created a self-improvement project 2 week0 码力 | 2 页 | 246.04 KB | 5 月前3
Rust 程序设计语言 简体中文版 1.85.0一个类 函数宏例子是可以像这样被调用的 sql! 宏: let sql = sql!(SELECT * FROM posts WHERE id=1); 这个宏会解析其中的 SQL 语句并检查其是否是句法正确的,这是比 macro_rules! 可以做到的 更为复杂的处理。sql! 宏会被定义为类似如此: #[proc_macro] pub fn sql(input: TokenStream)0 码力 | 562 页 | 3.23 MB | 24 天前3
Trends Artificial Intelligence
Azure AI Foundry expansion • NLWeb • Model Context Protocol (MCP) integration • Entra Agent ID • SQL Server 2025 • Windows Subsystem for Linux Open- Source • GitHub Copilot Chat Extension • Aurora0 码力 | 340 页 | 12.14 MB | 4 月前3
julia 1.10.10missing object, which is the singleton instance of the type Missing. missing is equivalent to NULL in SQL and NA in R, and behaves like them in most situations. 20.1 Propagation of Missing Values missing This follows the well-established rules of three-valued logic which are implemented by e.g. NULL in SQL and NA in R. This abstract definition corresponds to a relatively natural behavior which is best explained Nothing} arguments or fields. To represent missing data in the statistical sense (NA in R or NULL in SQL), use the missing object. See the Missing Values section for more details.CHAPTER 37. FREQUENTLY ASKED0 码力 | 1692 页 | 6.34 MB | 3 月前3
Julia 1.10.9missing object, which is the singleton instance of the type Missing. missing is equivalent to NULL in SQL and NA in R, and behaves like them in most situations. 20.1 Propagation of Missing Values missing This follows the well-established rules of three-valued logic which are implemented by e.g. NULL in SQL and NA in R. This abstract definition corresponds to a relatively natural behavior which is best explained Nothing} arguments or fields. To represent missing data in the statistical sense (NA in R or NULL in SQL), use the missing object. See the Missing Values section for more details.CHAPTER 37. FREQUENTLY ASKED0 码力 | 1692 页 | 6.34 MB | 3 月前3
Julia 1.11.4missing object, which is the singleton instance of the type Missing. missing is equivalent to NULL in SQL and NA in R, and behaves like them in most situations. 21.1 Propagation of Missing Values missing This follows the well-established rules of three-valued logic which are implemented by e.g. NULL in SQL and NA in R. This abstract definition corresponds to a relatively natural behavior which is best explained Nothing} arguments or fields. To represent missing data in the statistical sense (NA in R or NULL in SQL), use the missing object. See the Missing Values section for more details. In some languages, the0 码力 | 2007 页 | 6.73 MB | 3 月前3
Julia 1.11.5 Documentationmissing object, which is the singleton instance of the type Missing. missing is equivalent to NULL in SQL and NA in R, and behaves like them in most situations. 21.1 Propagation of Missing Values missing This follows the well-established rules of three-valued logic which are implemented by e.g. NULL in SQL and NA in R. This abstract definition corresponds to a relatively natural behavior which is best explained Nothing} arguments or fields. To represent missing data in the statistical sense (NA in R or NULL in SQL), use the missing object. See the Missing Values section for more details. In some languages, the0 码力 | 2007 页 | 6.73 MB | 3 月前3
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