NSF-BSF: RI: Small: Structured Distributions in Deep Nets

这项研究的目标是开发改善人工智能系统与人类之间互动的方法。人工智能系统在决策过程中越来越普遍，例如，在避免交通堵塞的导航系统中。然而，虽然人们可以向同行寻求建议及其背后的原因，但今天的人工智能系统通常无法为其输出提供理由。因此，用户必须1)盲目地相信系统推荐，2)单独验证合理性，或者3)忽略推荐。为了解决这三种选择都不可取的限制，研究开发了可以解释其输出的模型，研究开发了可以控制的算法，研究开发了允许与模型交互的方法。受人类在提出建议时关注数据子集的启发，该研究试图通过提取数据中提供最多证据的部分来获得可解释性、可控性和交互性。为此，我们在AI系统中使用概率分布。此外，这项研究将支持伊利诺伊大学厄巴纳-香槟分校的一群博士和本科生的发展，在当地社区开展外展活动，并开发两个课程：一个关于入门级机器学习的新型本科课程和一个关于人工智能系统分布的新型研究生课程。从技术上讲，AI系统内部的分布通常被称为注意力。注意提供了一个令人信服的框架1)来解释在判别网络中形成的决策；2)控制生成模型的采样过程；3)在强化学习系统中进行交互。本研究的技术目标分为三个重点。第一个重点是将注意力机制扩展到包含多种模式的数据，并开发算法，以便更好地捕捉那些高维环境中的概率分布。第二个推力将这些算法推广到更复杂的数据结构，并将这些结果用于生成高维输出的人工智能系统，例如，图像的描述。第三个重点是研究人类与利用分布的人工智能系统之间的互动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this research is to develop methods that improve interaction between AI systems and humans. AI systems are increasingly more prevalent in decision making processes, for instance, in navigation systems for avoiding traffic jams. However, while one could ask a peer for a recommendation and the reason behind it, today’s AI systems often cannot provide a justification for their output. Hence, users have to 1) trust the system recommendation blindly, 2) verify plausibility individually, or 3) ignore the recommendation. To address the limitation that none of those three options is desirable, the research develops models which can explain their output, the research develops algorithms which can be controlled, and the research develops methods which permit interaction with the model. Inspired by a human focusing on subsets of the data when making a recommendation, the research seeks to obtain explain-ability, control-ability and interact-ability by extracting which parts of the data provided most evidence. For this we use probability distributions inside AI systems. Furthermore, this research will support development of a cohort of PhD and undergraduate students at the University of Illinois at Urbana-Champaign, outreach activities in the local neighborhood and development of two classes: a novel undergrad class on entry-level machine learning and a novel grad class on distributions in AI systems.Technically, distributions inside AI systems are often referred to as attention. Attention provides a compelling framework 1) to explain the decisions formed in discriminative networks; 2) to control the sampling process in generative models; and 3) to interact in reinforcement learning systems. The technical aims of this research are divided into three thrusts. The first thrust scales attention mechanisms to data that comprises multiple modalities and develops algorithms which better capture probability distributions in those high-dimensional settings. The second thrust generalizes those algorithms to more complex data structures and leverages those results for AI systems which generate high-dimensional output, e.g., a description of an image. The third thrust studies interaction between humans and AI systems that leverage distributions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.