CCF DL Focus On Artificial Intelligence

当前的人工智能存在诸多局限性，这些局限性来自何处？我们将从目前人工智能与人类智能的差异出发，分析产生这些差异的本质原因。并进一步讨论，这些差异有没有可能消除，怎样来消除。我们有没有可能达到真正的人工智能？

那种在自然人思维中可见的因果关系推理，可以用算法表示出来，用于帮助建立类人机器智能。

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning.This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.

The two fields of machine learning and graphical causality arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.

Yoshua Bengio, one of the three pioneers of deep learning, delivered a keynote speech that shed light on possible directions that can bring us closer to human-level AI. Titled, “From System 1 Deep Learning to System 2 Deep Learning,” Bengio’s presentation is very technical and draws on research he and others have done in recent years.

机器学习的核心任务是从数据中自动地发现相关性信息以便预测未来。目前，大部分机器学习算法都是建立在统计相关性的基础上，这样极大地限制了机器学习的适用范围。因此，需要更进一步考虑统计相关性背后更本质的因果结构。因为因果模型对现实世界中数据的变化更加鲁棒，所以拥有了因果推断能力的机器学习算法可以更好地预测未来。主要讨论因果推断在机器学习各个方面的前沿进展，包括半监督学习、无监督学习、强化学习、迁移学习、元学习、公平学习等，以及它们在各个实际场景中的具体应用。