دانلود کتاب استحکام رقیب برای یادگیری ماشین

With the recent advances in machine learning theory and algorithms, the design of high-capacity and scalable models such as neural networks, abundant datasets, and sufficient computing resources, machine learning (ML), or more broadly, artificial intelligence (AI), has been transforming our industry and society at an unprecedented speed. While we are anticipating positive impacts enabled by machine learning technology, we may often overlook potential negative effects, which may bring considerable ethical concerns and even setbacks due to law regulations and catastrophic failures, especially for mission-critical and high-stakes decision making tasks. Therefore, beyond accuracy, trustworthy machine learning is the last milestone for ML-based technology to achieve and thrive. Trustworthy machine learning encompasses a broad set of essential topics such as adversarial robustness, fairness, explainability, accountability, and ethics. This book focuses on fulfilling the endeavor of evaluating, improving, and leveraging adversarial robustness of machine learning algorithms, models, and systems toward better and more trustworthy versions. Exploiting untrusted machine learning as vulnerabilities create unattended gateways for intended parties to manipulate machine predictions while evading human’s attention to gain their own benefits. No matter what one’s role is in ML, as a model developer, a stakeholder, or a user, we believe it is essential for everyone to understand adversarial robustness for machine learning, just like knowing the capabilities and limitations of your own vehicle before driving. For model developers, we advocate proactive in-house robustness testing of your own models and systems for error inspection and risk mitigation. For stakeholders, we advocate acknowledgment of possible weaknesses in products and services, as well as honest and thorough risk and threat assessment in a forward-thinking manner to prevent revenue/reputation loss and catastrophic damage to the society and environment. For users using machine learning byproducts, we advocate active understanding of their limitations for safe use and gaining awareness about possible misuses. These aspects related to adversarial robustness, along with the available techniques and tools, are elucidated in this book.