GECKA3D: A 3D Game Engine for Commonsense Knowledge Acquisition

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in

bAIoimage analysis: elevating the rate of scientific discovery -- as a community

The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it will require our multidisciplinary community to work together, establish FAIR data sharing and deliver usable, reproducible analytical tools.Comment: 5 pages, 1 figure, opinio

Open Data

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data’s integrity and transparency. This book describes how data retrieved from public open data repositories can improve the learning qualities of digital networking, particularly performance and reliability. Chapters address such topics as knowledge extraction, Open Government Data (OGD), public dashboards, intrusion detection, and artificial intelligence in healthcare

Interaction with smart assistants using alternative and augmentative communication

Smart assistants utilize speech recognition, sensing, artificial intelligence, and networking technologies to enable improved human-machine interaction. However, there remain use cases where smart assistants are not easily usable by humans. For example, voice-activated assistants are not accessible to those that are hearing or speech impaired. Touchscreen based assistants are not usable by those who lack fine motor skills and/or reading ability. This disclosure adds to the modalities by which humans can control and communicate with smart assistants by enabling use of physical objects, facial expressions, gross motor skills, body movements, etc. to provide commands. Collectively, these techniques of control and communication are referred to as alternative and augmentative communication (AAC)

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

A Human-centric AI-driven Framework for Exploring Large and Complex Datasets

Human-Centered Artificial Intelligence (HCAI) is a new frontier of research at the intersection between HCI and AI. It fosters an innovative vision of human-centred intelligent systems, which are systems that take advantage of computer features, such as powerful algorithms, big data management, advanced sensors and that are useful and usable for people, providing high levels of automation and enabling high levels of human control. This position paper presents our ongoing research aiming to extend the HCAI framework for better supporting designers in creating AI-based systems