人工智能杂志AI(Artificial Intelligence)是目前公认的发表该领域最新研究成果的主要国际论坛。该期刊欢迎有关AI广泛方面的论文，这些论文构成了整个领域的进步，也欢迎介绍人工智能应用的论文，但重点应该放在新的和新颖的人工智能方法如何提高应用领域的性能，而不是介绍传统人工智能方法的另一个应用。关于应用的论文应该描述一个原则性的解决方案，强调其新颖性，并对正在开发的人工智能技术进行深入的评估。
官网地址：http://dblp.uni-trier.de/db/journals/ai/
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报告题目：
Perception, Prediction, and Large Scale Data Collection for Autonomous Cars
报告简介：
预计自动驾驶汽车将极大地重新定义交通运输的未来。当这项技术完全实现时，它将带来巨大的社会、环境和经济效益。我们很高兴能分享一个全面的、大规模的数据集，其中包括原始传感器摄像头和激光雷达输入，这是一个由多辆高端自主车辆组成的车队在有限的地理区域内所感知到的。该数据集还包括高质量的、带有人类标签的交通代理的3D边界框，一个底层的HD空间语义图。因此，我们的目标是赋予社区权力，促进进一步发展，并从先进的工业自主车辆计划的角度分享我们对未来机遇的见解。
嘉宾介绍：
Luc Vincent,Peter Ondruska, Ashesh Jain,Sammy Omari，Vinay Shet。
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A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques, serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.
