人工智能：背景、风险和政策

English | ASIN: B0CT37NXBT | 2024 | 268 pages | True PDF | 24 MB

The field of artificial intelligence (AI) has gone through multiple waves of advancement over the decades. Today, AI can broadly be thought of as computerized systems that work and react in ways commonly thought to require intelligence, such as the ability to learn, solve problems, and achieve goals under uncertain and varying conditions. The field encompasses a range of methodologies and application areas, including machine learning (ML), natural language processing, and robotics. AI holds potential benefits and opportunities, but also challenges and pitfalls. For example, AI technologies can accelerate and provide insights into data processing; augment human decision-making; optimize performance for complex tasks and systems; and improve safety for people in dangerous occupations. On the other hand, AI systems may perpetuate or amplify bias, may not yet be fully able to explain their decision-making, and often depend on vast datasets that are not widely accessible to facilitate research and development (R&D). Further, stakeholders have questioned the adequacy of human capital in both the public and private sectors to develop and work with AI, as well as the adequacy of current laws and regulations for dealing with societal and ethical issues that may arise. Together, such challenges can lead to an inability to fully assess and understand the operations and outputs of AI systems.

中文|亚洲：B0CT37NXBT | 2024 | 268页|真PDF | 24 MB 几十年来，人工智能（AI）领域经历了多次发展浪潮。今天，人工智能可以广泛地被认为是一种计算机化系统，其工作和反应方式通常被认为需要智能，例如在不确定和变化的条件下学习、解决问题和实现目标的能力。该领域涵盖了一系列方法和应用领域，包括机器学习（ML）、自然语言处理和机器人技术。人工智能既有潜在的好处和机遇，也有挑战和陷阱。例如，人工智能技术可以加速数据处理并提供洞察；增强人类决策能力；优化复杂任务和系统的性能；提高危险职业人员的安全。另一方面，人工智能系统可能会延续或放大偏见，可能还不能完全解释其决策，并且往往依赖于大量数据集，而这些数据集并不能广泛用于促进研发。此外，利益相关者质疑公共和私营部门的人力资本是否足以开发和使用人工智能，以及现行法律法规是否足以应对可能出现的社会和道德问题。总之，这些挑战可能会导致无法充分评估和理解人工智能系统的操作和输出。

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