[论文解读] Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

Matti Pietikäinen, Olli Sílven|arXiv (Cornell University)|Jan 5, 2022

COVID-19 diagnosis using AI被引用 15

一句话总结

本文对人工智能进行了全面而现实的评估，追溯了其从机器学习和计算机视觉到情感智能这一新兴前沿的发展历程。文章批判了深度学习的过度炒作，概述了当前的局限性，并主张将人工智能视为人类的助手而非替代品，同时介绍了情感识别和伦理人工智能发展的基础性研究。

ABSTRACT

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

研究动机与目标

• 提供一种平衡且基于历史的视角来审视人工智能，以证据揭示其局限性来回应当前的炒作。
• 考察大规模数据和深度学习在推动近期人工智能进展中的作用，同时强调其制约因素。
• 探讨将情感智能整合入人工智能系统的可能性，这是一个尚未充分发展但至关重要的前沿领域。
• 评估超级智能的可行性与风险，认为其在当前技术和理论框架下仍遥不可及。
• 通过反思课程建设与实际应用，为未来的人工智能教育与研究提供指导。

提出的方法

• 从20世纪40年代至今，系统梳理人工智能的历史发展，强调其在过度乐观与幻灭之间的周期性波动。
• 分析大规模数据集与深度神经网络在现代人工智能在视觉与语言任务中表现中的作用。
• 介绍计算机视觉中的基础技术，包括卷积神经网络与目标检测，并引用作者自身的研究成果。
• 提出基于面部表情、语音分析与多模态数据融合的情绪识别方法。
• 评估实现人工通用智能或超级智能在理论与实践上的障碍。
• 提出一种以协作为核心、而非自主性为基础的伦理化、以人为本的人工智能发展框架。

实验结果

研究问题

• RQ1 当前基于深度学习的人工智障系统的核心局限是什么？为何它们在实现人类水平智能方面仍显不足？
• RQ2 情感智能如何能被有意义地整合进人工智能系统中？这涉及哪些技术和伦理挑战？
• RQ3 在当前知识与技术约束下，超级智能的概念在科学上有多大的可行性？
• RQ4 人工智能教育的演变，特别是奥卢大学的实践，如何反映了人工智能研究与应用优先事项的变化？
• RQ5 大规模数据集在现代人工智能成功中扮演了何种角色？这种依赖关系的长期影响是什么？

主要发现

• 深度学习在图像分类与目标检测等特定领域取得了显著成果，但在分布外或罕见事件场景中性能会显著下降。
• 当前人工智能系统缺乏真正的理解与推理能力，主要依赖数据中的统计模式，而非因果或概念性知识。
• 人工智能中的情感识别仍处于早期阶段，面临准确性、文化偏见与多模态融合等重大挑战。
• 在当前科学与计算框架下，追求超级智能似乎不可行，因为它需要在认知与自我意识方面实现突破性进展。
• 人工智能教育必须超越技术技能，纳入伦理、社会影响与跨学科协作，以确保负责任的发展。
• 本文结论认为，人工智能应被视为人类的协作工具，而非自主决策者，强调人类监督与控制的重要性。