The spread of modern humans across the globe has led to genetic adaptations to diverse local environments. Recent developments in genomic technologies, statistical analyses, and expanded sampled populations have led to improved identification and fine-mapping of genetic variants associated with adaptations to regional living conditions and dietary practices. Ongoing efforts in sequencing genomes of indigenous populations, accompanied by the growing availability of "-omics" and ancient DNA data, promises a new era in our understanding of recent human evolution and the origins of variable traits and disease risks.
The American presidency can be a bit of a popularity contest with the public. Oftentimes, praise and criticism of the country's commander in chief splits along party lines, with Republicans and Democrats favoring presidents who share their political ideologies, and moderate political figures finding more success in drawing praise from across the political spectrum. Such partisanship can be seen in a University of Virginia Center for Politics/Ipsos poll released in advance of Presidents Day. The online survey asked 1,004 adult respondents to rate 12 recent presidents on a 1 to 10 rating, with 1 being terrible and 10 being excellent. Here are 12 recent presidents, ranked by popularity.
The report explained: "While fewer than 20% of hiring managers said that recent graduates lacked the math skills needed for the work, more than half said that recent graduates lacked attention to detail. About equal shares of hiring managers saw deficiencies in writing proficiency and communication – the cognitive and non-cognitive aspects, respectively, of a single skill. About a third of hiring managers said recent college graduates lacked data analysis and teamwork skills."
Basic decisions, such as judging a person as a friend or foe, involve categorizing novel stimuli. Recent work finds that people's category judgments are guided by a small set of examples that are retrieved from memory at decision time. This limited and stochastic retrieval places limits on human performance for probabilistic classification decisions. In light of this capacity limitation, recent work finds that idealizing training items, such that the saliency of ambiguous cases is reduced, improves human performance on novel test items. One shortcoming of previous work in idealization is that category distributions were idealized in an ad hoc or heuristic fashion.
My collaborators and I have recently reported in domain science journals several human-computer discoveries in biology, chemistry, and physics. My conclusion is that each finding involves a new representation of the scientific task: The problem spaces searched were unlike previous task problem spaces. Such new representations need not be wholly new to the history of science; rather, they can draw on useful representational pieces from elsewhere in natural or computer science. My analysis also suggests a broader potential role for (AI) computer scientists in the practice of natural science.