Autonomous cars are predicted to improve fuel efficiency for everyone on the road – an idea that will be put to the test on routes around Nashville, Tennessee, later this year.

The technology consists of sensors that use WiFi signals to help the robot map where it's going. Most systems rely on optical light sensors such as cameras and LiDARs. In this case, the so-called "WiFi sensors" use radio frequency signals rather than light or visual cues to see, so they can work in conditions where cameras and LiDARs struggle -- in low light, changing light, and repetitive environments such as long corridors and warehouses. And by using WiFi, the technology could offer an economical alternative to expensive and power hungry LiDARs, the researchers noted. A team of researchers from the Wireless Communication Sensing and Networking Group, led by UC San Diego electrical and computer engineering professor Dinesh Bharadia, will present their work at the 2022 International Conference on Robotics and Automation (ICRA), which will take place from May 23 to 27 in Philadelphia.

This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society's most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench "bias," are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI's long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.

The motivation to integrate AI into mental health services has grown during the pandemic. The Kaiser Family Foundation reported an increase in adults experiencing symptoms of anxiety and depression, from 1 in 10 adults pre-pandemic to 4 in 10 adults in early 2021. Coupled with a national shortage of mental health professionals as well as limited opportunities for in-person mental health support, AI-powered tools could be used as an entry point to care by automatically and remotely measuring and intervening to reduce mental health symptoms. Many mental health startups are integrating AI within their product offerings. Woebot Health developed a chatbot that delivers on-demand therapy to users through natural language processing (NLP).

Machine-learning algorithms that generate fluent language from vast amounts of text could change how science is done -- but not necessarily for the better, says Shobita Parthasarathy, a specialist in the governance of emerging technologies at the University of Michigan in Ann Arbor. In a report published on 27 April, Parthasarathy and other researchers try to anticipate societal impacts of emerging artificial-intelligence (AI) technologies called large language models (LLMs). These can churn out astonishingly convincing prose, translate between languages, answer questions and even produce code. The corporations building them -- including Google, Facebook and Microsoft -- aim to use them in chatbots and search engines, and to summarize documents. They sometimes parrot errors or problematic stereotypes in the millions or billions of documents they're trained on.

Shobita Parthasarathy says that LLMs could help to advance research, but their use should be regulated. Machine-learning algorithms that generate fluent language from vast amounts of text could change how science is done -- but not necessarily for the better, says Shobita Parthasarathy, a specialist in the governance of emerging technologies at the University of Michigan in Ann Arbor. In a report published on 27 April, Parthasarathy and other researchers try to anticipate societal impacts of emerging artificial-intelligence (AI) technologies called large language models (LLMs). These can churn out astonishingly convincing prose, translate between languages, answer questions and even produce code. The corporations building them -- including Google, Facebook and Microsoft -- aim to use them in chatbots and search engines, and to summarize documents. They sometimes parrot errors or problematic stereotypes in the millions or billions of documents they're trained on.

HPE has released a pair of systems designed to broaden the uptake and speed deployment of machine learning among enterprises. Swarm Learning is aimed at bringing the wisdom of crowds to machine learning modeling without sacrificing security, while the Machine Learning Development System is meant to offer a one-box training solution for companies that would otherwise have had to design and build their own machine learning infrastructure. The Machine Learning Development System is available in physical footprints of several different sizes, but the company says a "small configuration" uses an Apollo 6500 Gen10 compute server to provide the horsepower for machine learning training, HPE ProLiant DL325 servers and Aruba CX 6300 switches for management of system components, and NVIDIA's Quantum InfiniBand networking platform, along with HPE's specialist Machine Learning Development Environment and Performance Cluster management software suites. According to IDC research vice president Peter Rutten, it's essentially bringing HPC (high performance computing) capabilities to enterprise machine learning, something that would usually require enterprises to architect their own systems. "It is the kind of system that businesses are really looking for, now that AI is more mature," he said.

In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we're thrilled to share our conversation with Dr. Varshita Sher. Dr. Sher is currently working as a data scientist at the Alan Turing Institute's Applied Research Centre, leveraging deep-learning technology to solve problems in the NLP and Computer Vision domains. She has a Master's degree in Computer Science from the University of Oxford and a Ph.D. in Learning Analytics from Simon Fraser University. Her work in the last eight years has focused on the intersection of research and implementation of AI/ML algorithms in myriad sectors, including Edtech, Fintech, and Healthcare.

Artificial intelligence (AI) is helping prevent overfishing in a bid to protect the world's rapidly dwindling supply of edible marine species. A new project uses AI to improve the identification and measurement of fish species in Africa's Nile Basin. The software can help scientists understand fish population density more quickly than human observers. It's part of a larger effort to harness AI to improve sustainability across a wide range of industries. "The promising thing about AI is that it now allows us to do tasks that would be time-consuming or impossibly complex using traditional methods, with considerably more speed and efficiency," Andrew Dunckelman, head of impact and insights at Google.org, the search giant's charitable arm, told Lifewire in an email interview.

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.