The carbon emissions of writing and illustrating are lower for AI than for humans.

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.

Building personal maps from GPS data.

In this article we discuss an assisted cognition information technology system that can learn personal maps customized for each user and infer his daily activities and movements from raw GPS data. The system uses discriminative and generative models for different parts of this task. A discriminative relational Markov network is used to extract significant places and label them; a generative dynamic Bayesian network is used to learn transportation routines, and infer goals and potential user errors at real time. We focus on the basic structures of the models and briefly discuss the inference and learning techniques. Experiments show that our system is able to accurately extract and label places, predict the goals of a person, and recognize situations in which the user makes mistakes, such as taking a wrong bus.

Serum phosphate levels and mortality risk among people with chronic kidney disease.

Elevated serum phosphate levels have been linked with vascular calcification and mortality among dialysis patients. The relationship between phosphate and mortality has not been explored among patients with chronic kidney disease (CKD). A retrospective cohort study was conducted from eight Veterans Affairs' Medical Centers located in the Pacific Northwest. CKD was defined by two continuously abnormal outpatient serum creatinine measurements at least 6 mo apart between 1999 and 2002. Patients who received chronic dialysis, those with a present or previous renal transplant, and those without a recent phosphate measurement were excluded. The primary end point was all-cause mortality. Secondary end points were acute myocardial infarction and the combined end point of myocardial infarction plus death. A total of 95,619 veterans with at least one primary care or internal medicine clinic contact from a Northwest VA facility and two or more outpatient measurements of serum creatinine, at least 6 mo apart, between January 1, 1999, and December 31, 2002, were identified. From this eligible population, 7021 patients met our definition of CKD. After exclusions, 6730 CKD patients were available for analysis, and 3490 had a serum phosphate measurement during the previous 18 mo. After adjustment, serum phosphate levels >3.5 mg/dl were associated with a significantly increased risk for death. Mortality risk increased linearly with each subsequent 0.5-mg/dl increase in serum phosphate levels. Elevated serum phosphate levels were independently associated with increased mortality risk among this population of patients with CKD.

Pre-mRNA secondary structure prediction aids splice site prediction.

Accurate splice site prediction is a critical component of any computational approach to gene prediction in higher organisms. Existing approaches generally use sequence-based models that capture local dependencies among nucleotides in a small window around the splice site. We present evidence that computationally predicted secondary structure of moderate length pre-mRNA subsequencies contains information that can be exploited to improve acceptor splice site prediction beyond that possible with conventional sequence-based approaches. Both decision tree and support vector machine classifiers, using folding energy and structure metrics characterizing helix formation near the splice site, achieve a 5-10% reduction in error rate with a human data set. Based on our data, we hypothesize that acceptors preferentially exhibit short helices at the splice site.