Скачать или смотреть AI Book Review | Machine Intelligence: From Turing Test to Large Language | Mohamed Medhat Gaber

In Machine Intelligence: From Turing Test to Large Language Models, readers embark on an in-depth exploration of the methods and milestones that have defined the evaluation of artificial intelligence over the decades. The journey begins with the seminal ideas of Alan Turing, including his renowned Turing Test, delving into its various interpretations, criticisms, and its evolving relevance in contemporary AI research. The narrative then transitions to the Lovelace Test, inspired by Ada Lovelace's pioneering vision of creativity in machines, and expands on philosophical inquiries into the nature of machine intelligence. The text also provides a detailed examination of methodologies for assessing the reasoning capabilities of large language models (LLMs), focusing on innovative concepts like zero-shot learning and transfer learning, and introduces the ARC Benchmark as a cutting-edge standard for evaluating AI performance.

Further, the book investigates the metrics for quantifying creativity and novelty in AI-generated content, emphasising techniques in divergent thinking and novelty detection. It also addresses the critical ethical dimensions of AI evaluation, scrutinising issues of bias, fairness, explainability, and accountability to promote responsible AI development. By measuring robustness and generalisation, the book discusses how AI systems can be fortified against adversarial attacks and overfitting, ensuring their applicability in real-world scenarios. Looking ahead, it explores the promising intersections of quantum computing with AI, the integration of vision and language in multimodal LLMs, and the indispensable role of human evaluators in advancing AI technologies. This comprehensive examination provides a multifaceted perspective on the past, present, and future of machine intelligence evaluation.