The desire to predict discoveries--to have some idea, in advance, of what will be discovered, by whom, when, and where--pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the "science of science" and what it teaches us about the predictability of scientific discovery. We then discuss future opportunities for improving predictions derived from the science of science and its potential impact, positive and negative, on the scientific community.
A major challenge for using data to make predictions is distinguishing what is meaningful from noise. As this special section explores, prediction is now a developing science. Social scientists and the machine learning community are acquiring new analytical tools to distinguish meaningful patterns from noise. Several authors in this special section describe the importance of realistic goals that seek to balance machine learning approaches with the human element.
Phenomena such as placebo analgesia or pain relief through distraction highlight the powerful influence cognitive processes and learning mechanisms have on the way we perceive pain. Although contemporary models of pain acknowledge that pain is not a direct readout of nociceptive input, the neuronal processes underlying cognitive modulation are not yet fully understood. Modern concepts of perception--which include computational modeling to quantify the influence of cognitive processes--suggest that perception is critically determined by expectations and their modification through learning. Research on pain has just begun to embrace this view.
This edition of Innovation Nation focuses on the people behind digital disruption at Capgemini. We've assembled a number of articles in this issue, starting with "Next generation Global Business Services" that looks at how the human-machine relationship can be optimized to exceed individual customer expectations. Divya Kumar and Christopher Stancombe explore this relationship further in their respective articles on incremental artificial intelligence (AI) implementation and robotic process automation (RPA).
In the last decade, in the Semantic Web field, knowledge bases have attracted tremendous interest from both academia and industry and many large knowledge bases are now available. In order to cope with this issue, the availability of automatic methods for schema aware generation and population of knowledge bases results fundamental. The primary goal of the special issue is to provide novel machine learning/data mining methods for knowledge base generation, population, enrichment, evolution showing advances in the Semantic Web field. Please indicate in the cover letter that it is for the Special Issue on Machine Learning for Knowledge Base Generation and Population.
In 2008, Political Analysis published a groundbreaking special issue on the analysis of political text, examining some of the initial e fforts in political science to consider text as a data source and to develop methods for analyzing text data. Answering their call, in the last eight years, the fi eld of "text as data" in social science has grown dramatically. As the number of sources and types of textual data documenting social science phenomenon has exploded, so too have methods for, and the use of, text analysis in social science research. The articles included in this virtual issue of Political Analysis showcase how the study of text analysis in political science has built on these initial political science approaches.