Accelerating progress in Artificial General Intelligence: Choosing a benchmark for natural world interaction
Measuring progress in the field of Artificial General Intelligence (AGI) can be difficult without commonly accepted methods of evaluation. An AGI benchmark would allow evaluation and comparison of the many computational intelligence algorithms that have been developed. In this paper I propose that a benchmark for natural world interaction would possess seven key characteristics: fitness, breadth, specificity, low cost, simplicity, range, and task focus. I also outline two benchmark examples that meet most of these criteria. In the first, the direction task, a human coach directs a machine to perform a novel task in an unfamiliar environment. The direction task is extremely broad, but may be idealistic. In the second, the AGI battery, AGI candidates are evaluated based on their performance on a collection of more specific tasks. The AGI battery is designed to be appropriate to the capabilities of currently existing systems. Both the direction task and the AGI battery would require further definition before implementing. The paper concludes with a description of a task that might be included in the AGI battery: the search and retrieve task.
Matteo Leonetti, Petar Kormushev and Simone Sagratella
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are a few studies that consider the impact of situational factors, but they either take situational factors as the research background or just investigated the direct impact of situational factors on search intention. For instance, Wu and Li (2015) analyzed the elderly’s diversity of emotion and cognition, and their different ways of dealing with information in online information retrieval processes in different health situations via user experiment, questionnaire survey, and interview methods. But this study only considered different situations as the research
Xianlei Dong, Jian Xu, Ying Ding, Chenwei Zhang, Kunpeng Zhang and Min Song
and comments about natural disasters and political uprisings travel at breakneck speed in Twitter or Google. Social media are becoming an important channel that can also greatly influence our perception of a certain topic. In economics, social media can have a significant effect on a firm’s reputation, sales, and even survival ( Kietzmann et al., 2011 ). Altmetrics ( Priem et al., 2010 ) use social media to estimate the early impact of publications or researchers. Dong and Bollen (2015) applied Google search-engine query data to detect consumer confidence indexes
individually stored temporarily by job ID so users can go back without the need to re-run the search each time. Thus, carrying out a one-node search is simply a matter of carrying out a series of two-node searches, one for each MeSH term within the category of interest ( Smalheiser, 2012b ). This greatly simplifies the computational issues involved.
Examples from the Front Lines of Scientific Investigation
A variety of investigators have used literature-based discovery (LBD) methods to propose specific hypotheses which were then tested experimentally. Some of these
Stephen F. Carley, Alan L. Porter and Jan L. Youtie
perform disambiguation by comparing citation records using some type of similarity function.” In their analysis, a “major gap in the field is the lack of direct comparisons among the methods under the same circumstances: e.g., same collections (e.g., many methods used different versions of collections such as DBLP).” DBLP, which originally stood for DataBase systems and Logic Programming, is a scholarly digital library which was launched at the University of Trier, Germany, in 1993. It tracks all major computer science journals. Another drawback of this approach is