Do horror writers have worse childhoods than other writers? Though biographical details are known about many writers, quantitatively exploring such a qualitative hypothesis requires significant human effort, e.g. to sift through many biographies and interviews of writers and to iteratively search for quantitative features that reflect what is qualitatively of interest. This paper explores the potential to quickly prototype these kinds of hypotheses through (1) applying LLMs to estimate properties of concrete entities like specific people, companies, books, kinds of animals, and countries; (2) performing off-the-shelf analysis methods to reveal possible relationships among such properties (e.g. linear regression); and towards further automation, (3) applying LLMs to suggest the quantitative properties themselves that could help ground a particular qualitative hypothesis (e.g. number of adverse childhood events, in the context of the running example). The hope is to allow sifting through hypotheses more quickly through collaboration between human and machine. Our experiments highlight that indeed, LLMs can serve as useful estimators of tabular data about specific entities across a range of domains, and that such estimations improve with model scale. Further, initial experiments demonstrate the potential of LLMs to map a qualitative hypothesis of interest to relevant concrete variables that the LLM can then estimate. The conclusion is that LLMs offer intriguing potential to help illuminate scientifically interesting patterns latent within the internet-scale data they are trained upon.

Tokyo AI company Exawizards is using deep learning to analyze unstructured nursing care data such as audio and video recordings. Faced with a rapidly aging population, Japan is turning to new solutions for a many-faceted problem. Amid a low birthrate, more than one-fifth of Japanese are now 70 or older, according to government data. Meanwhile, there are not enough people to care for this cohort. By 2025, there will be a shortfall of 340,000 nursing care workers. Under the Abe administration, Japan has actively welcomed foreign professionals as one measure to address this issue.

Tokyo AI company Exawizards is using deep learning to analyze unstructured nursing care data such as audio and video recordings. Faced with a rapidly aging population, Japan is turning to new solutions for a many-faceted problem. Amid a low birthrate, more than one-fifth of Japanese are now 70 or older, according to government data. Meanwhile, there are not enough people to care for this cohort. By 2025, there will be a shortfall of 340,000 nursing care workers. Under the Abe administration, Japan has actively welcomed foreign professionals as one measure to address this issue.

As our population ages, these caregiver robots will also be useful for the old as well as the young. This is not a rhetorical question. So-called robot nannies are already a hit in Japan and China, and are now beginning to appear stateside. The numbers show why this is an attractive proposition. According to the U.S. Center for Disease Control and Prevention, approximately 4 million babies are born annually in the United States. What's more, 62% percent of women who gave birth within the last year work outside of the home, according to the U.S. Department of Labor.

Amid Japan's rapidly aging population, a burgeoning industry is targeting families expecting a death in the near future. Services on offer range from seminars on funerals and inheritance arrangements to a board game that prepares players for the financial implications of age-related decline. With more and more elderly Japanese living alone, consultations are also offered on how to obtain guardianship needed for time in hospital, as well as how to bequeath assets to individuals and organizations other than legal heirs. Pip Robot Technology Co.'s "Kokorozumori" ("Making Preparations in One's Head") is a dice board game that gets families to think about the costs and implications of caring for aging, ailing relatives. The Osaka-based manufacturer of robot dolls intended as companions for elderly people created the game in 2015, in collaboration with a research team at the University of Tsukuba. Players form pairs -- one elderly and the other a younger family member -- and earn an income in the form of pension and salary as they progress on the board.