Musculoskeletal disorders (MSD) are the most common cause of work-related injuries and lost production involving approximately 1.7 billion people worldwide and mainly affect low back (more than 50%) and upper limbs (more than 40%). It has a profound effect on both the workers affected and the company. This paper provides an ergonomic assessment of different work activities in a horse saddle-making company, involving 5 workers. This aim guides the design of a wrist exoskeleton to reduce the risk of musculoskeletal diseases wherever it is impossible to automate the production process. This evaluation is done either through subjective and objective measurement, respectively using questionnaires and by measurement of muscle activation with sEMG sensors.

Although intended to foster spontaneous interactions among workers, a typical open-plan office layout cannot mitigate visual, acoustic, or privacy-related distractions that originate from unplanned meetings. As office workers often refrain from tackling these issues by manually demarcating or physically relocating to a more suitable subspace that is enclosed by movable partitions, we hypothesise that these subspaces could instead be robotically manifested. This study therefore evaluated the perceived impact of two mobile robotic partitions that were wizarded to jointly manifest an enclosed subspace, to: 1) either `mitigate' or `intervene' in the distractions caused by spontaneous face-to-face or remote meetings; or 2) either `gesturally' or `spatially' nudge a distraction-causing worker to relocate. Our findings suggest how robotic furniture should interact with office workers with and through transient space, and autonomously balance the distractions not only for each individual worker but also for multiple workers sharing the same workspace.

Higher education plays a critical role in driving an innovative economy by equipping students with knowledge and skills demanded by the workforce. While researchers and practitioners have developed data systems to track detailed occupational skills, such as those established by the U.S. Department of Labor (DOL), much less effort has been made to document skill development in higher education at a similar granularity. Here, we fill this gap by presenting a longitudinal dataset of skills inferred from over three million course syllabi taught at nearly three thousand U.S. higher education institutions. To construct this dataset, we apply natural language processing to extract from course descriptions detailed workplace activities (DWAs) used by the DOL to describe occupations. We then aggregate these DWAs to create skill profiles for institutions and academic majors. Our dataset offers a large-scale representation of college-educated workers and their role in the economy. To showcase the utility of this dataset, we use it to 1) compare the similarity of skills taught and skills in the workforce according to the US Bureau of Labor Statistics, 2) estimate gender differences in acquired skills based on enrollment data, 3) depict temporal trends in the skills taught in social science curricula, and 4) connect college majors' skill distinctiveness to salary differences of graduates. Overall, this dataset can enable new research on the source of skills in the context of workforce development and provide actionable insights for shaping the future of higher education to meet evolving labor demands especially in the face of new technologies.

Let's take a look at how OpenAI and its chatbot have impacted consumer electronics in 2023 and where they might lead the industry in the new year. "Meteoric" doesn't do justice to OpenAI's rise this year. The company released ChatGPT on November 30, 2022. Within five days, the program had passed 1 million users; by January, 100 million people a month were logging on to use it. It took Facebook four and a half years to reach those sorts of engagement numbers.

Machines with human-level intelligence should be able to do most economically valuable work. This aligns a major economic incentive with the scientific grand challenge of building a human-like mind. Here we describe our approach to building and testing such a system. Our approach comprises a physical humanoid robotic system; a software based control system for robots of this type; a performance metric, which we call g+, designed to be a measure of human-like intelligence in humanoid robots; and an evolutionary algorithm for incrementally increasing scores on this performance metric. We introduce and describe the current status of each of these. We report on current and historical measurements of the g+ metric on the systems described here.

The amount of time we spend doing repetitive work is mind-boggling, with manual computer tasks and data entry taking up a good portion of an office worker's day. A recent survey indicates that people estimate they waste five hours each week on tasks that should be automated. According to McKinsey, the number is even higher, with at least one-third of job activities deemed automatable in about 60% of occupations. Whether it's data collection, approvals, or updates, many tasks don't require creativity or intuition, essential attributes that serve to increase job satisfaction. Instead, the monotony of the work lowers satisfaction, leading to lower productivity and other inefficiencies.

Publicly accessible benchmarks that allow for assessing and comparing model performances are important drivers of progress in artificial intelligence (AI). While recent advances in AI capabilities hold the potential to transform medical practice by assisting and augmenting the cognitive processes of healthcare professionals, the coverage of clinically relevant tasks by AI benchmarks is largely unclear. Furthermore, there is a lack of systematized meta-information that allows clinical AI researchers to quickly determine accessibility, scope, content and other characteristics of datasets and benchmark datasets relevant to the clinical domain. To address these issues, we curated and released a comprehensive catalogue of datasets and benchmarks pertaining to the broad domain of clinical and biomedical natural language processing (NLP), based on a systematic review of literature and online resources. A total of 450 NLP datasets were manually systematized and annotated with rich metadata, such as targeted tasks, clinical applicability, data types, performance metrics, accessibility and licensing information, and availability of data splits. We then compared tasks covered by AI benchmark datasets with relevant tasks that medical practitioners reported as highly desirable targets for automation in a previous empirical study. Our analysis indicates that AI benchmarks of direct clinical relevance are scarce and fail to cover most work activities that clinicians want to see addressed. In particular, tasks associated with routine documentation and patient data administration workflows are not represented despite significant associated workloads. Thus, currently available AI benchmarks are improperly aligned with desired targets for AI automation in clinical settings, and novel benchmarks should be created to fill these gaps.

A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions. Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals. We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g., gathering data, analyzing data, communicating to stakeholders) to complete those projects. Today's post will focus on the broad work activities (or projects) that make up their roles at work, including "Build prototypes to explore applying machine learning to new areas" and "Analyze and understand data to influence product or business decisions".

The robots are taking over. It seems like a common refrain these days as more and more Artificial Intelligence (AI) and machine learning operations are incorporated into everyday work processes and activities. Many people fear the loss of their jobs to AI and the impact of new technologies on their future career growth. While there's no doubt that AI and machine learning are disrupting the way we live and work, the reality is that AI will create more jobs and opportunities than it destroys. From transforming businesses and boosting productivity to addressing "moonshot" societal challenges, there are many positive applications of AI.

While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what should be automated. We quantitatively investigate these research questions by utilizing probabilistic machine learning models trained on thousands of ratings, provided by both healthcare practitioners and automation experts. Based on our findings, we present an analytical tool (Automatability-Desirability Matrix) to support policymakers and organizational leaders in developing practical strategies on how to harness the positive power of automation technologies, while accompanying change and empowering stakeholders in a participatory fashion.