Abiodun Ogunyemi has been an Uber Eats delivery driver since February 2020. But since March he has been unable to work due to what a union supporting drivers claims is a racially-biased algorithm. Ogunyemi, who is Black, had submitted a photograph of himself to confirm his identity on the app, but when the software failed to recognize him, he was blocked from accessing his account for "improper use of the Uber application." Ogunyemi is one of dozens of Uber drivers who have been prevented from working due to what they say is "racist" facial verification technology. Uber uses Microsoft Face API software on its app to verify drivers' identification, asking drivers to submit new photos on a regular basis.

An international group of researchers has successfully applied AI technology to real-world retinal imagery to detect possible diseases more accurately and on a larger scale. Retinal examinations can detect a number of diseases that affect the eye. Fundus photography is a process of taking photographs of the interior of the eye through the pupil and is a way to screen and monitor such retinal diseases. The introduction of artificial intelligence (AI) technology to fundus photography has improved the platform and enabled it to detect and monitor retinal diseases on a large scale. The Comprehensive AI Retinal Expert (CARE) system was developed by an international group of researchers from Sun Yat-sen University, Beijing Eaglevision Technology (Airdoc), Monash University, University of Miami Miller School of Medicine, Beijing Tongren Eye Centre and Capital Medical University.

It's Ada Lovelace Day and once again we're delighted to introduce you to "50 women in robotics you need to know about"! From the Afghanistan Girls Robotics Team to K.G.Engelhardt who in 1989 founded, and was the first Director of, the Center for Human Service Robotics at Carnegie Mellon, these women showcase a wide range of roles in robotics. We hope these short bios will provide a world of inspiration, in our ninth Women in Robotics list! They are researchers, industry leaders, and artists. Some women are at the start of their careers, while others have literally written the book, the program or the standards.

The effects of climate change could already be impacting 85% of the world's population, an analysis of tens of thousands of scientific studies said Monday. A team of researchers used machine learning to comb through vast troves of research published between 1951 and 2018, and found some 100,000 papers that potentially documented evidence of climate change's effects on the Earth's systems. "We have overwhelming evidence that climate change is affecting all continents -- all systems," study author Max Callaghan said in an interview. He added that there was a "huge amount of evidence" showing the ways in which these impacts are being felt. The researchers taught a computer to identify climate-relevant studies, generating a list of papers on topics from disrupted butterfly migration to heat-related human deaths and forestry cover changes.

Speech summarization is typically performed by using a cascade of speech recognition and text summarization models. End-to-end modeling of speech summarization models is challenging due to memory and compute constraints arising from long input audio sequences. Recent work in document summarization has inspired methods to reduce the complexity of self-attentions, which enables transformer models to handle long sequences. In this work, we introduce a single model optimized end-to-end for speech summarization. We apply the restricted self-attention technique from text-based models to speech models to address the memory and compute constraints. We demonstrate that the proposed model learns to directly summarize speech for the How-2 corpus of instructional videos. The proposed end-to-end model outperforms the previously proposed cascaded model by 3 points absolute on ROUGE. Further, we consider the spoken language understanding task of predicting concepts from speech inputs and show that the proposed end-to-end model outperforms the cascade model by 4 points absolute F-1.

In my previous Nextgov column, I got to interview the general manager of a company that is using artificial intelligence to help the military plan out its maintenance schedules. That program uses AI in a really good way that plays to its strengths, namely its ability to consider thousands of data points, much more than a human ever could, to come up with an action plan for maintenance that maximizes both efficiency and safety. However, when it comes time to actually perform the maintenance, those tasks must be delegated back to a human. But what happens if those physical tasks are also extremely complicated? The CV-22 Osprey is a perfect example of a military aircraft that is both revolutionary and complicated to maintain.

Artificial intelligence has made a disheartening discovery – 85 percent of the world's population has already been affected by human-induced climate change. The findings were made by German scientists, led by Max Callaghan from the Mercator Research Institute on Global Commons and Climate Change, who, according to the study, trained the system to'identify, evaluate and summarize scientific publications on climate change and its consequences.' Researchers used machine learning to sift through data published from 1951 through 2018 and found more than 100,000 studies with evidence that shows 80 percent of Earth's inhabited land has been impacted by climate change. The results also uncovered an'attribution gap' around the globe, where evidence is is distributed unequally across countries - 'evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries,' according to the study. Artificial intelligence has made a disheartening discovery – 85 percent of the world's population has already been affected by human-induced climate change.

Climate change could already be affecting 85 percent of the world's population, an analysis of tens of thousands of scientific studies has found. The analysis, released on Monday, was carried out by a team of researchers that used machine learning to comb through vast troves of research published between 1951 and 2018 and found some 100,000 papers that potentially documented evidence of climate change's effects on the Earth's systems. "We have overwhelming evidence that climate change is affecting all continents, all systems," study author Max Callaghan told the AFP news agency in an interview. He added there was a "huge amount of evidence" showing the ways in which these effects are being felt. The researchers taught a computer to identify climate-relevant studies, generating a list of papers on topics from disrupted butterfly migration to heat-related human deaths to forestry cover changes.

Societal ideas and trends dictate media narratives and cinematic depictions which in turn influences people's beliefs and perceptions of the real world. Media portrayal of culture, education, government, religion, and family affect their function and evolution over time as people interpret and perceive these representations and incorporate them into their beliefs and actions. It is important to study media depictions of these social structures so that they do not propagate or reinforce negative stereotypes, or discriminate against any demographic section. In this work, we examine media representation of professions and provide computational insights into their incidence, and sentiment expressed, in entertainment media content. We create a searchable taxonomy of professional groups and titles to facilitate their retrieval from speaker-agnostic text passages like movie and television (TV) show subtitles. We leverage this taxonomy and relevant natural language processing (NLP) models to create a corpus of professional mentions in media content, spanning more than 136,000 IMDb titles over seven decades (1950-2017). We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics. We observe increased media mentions of STEM, arts, sports, and entertainment occupations in the analyzed subtitles, and a decreased frequency of manual labor jobs and military occupations. The sentiment expressed toward lawyers, police, and doctors is becoming negative over time, whereas astronauts, musicians, singers, and engineers are mentioned favorably. Professions that employ more people have increased media frequency, supporting our hypothesis that media acts as a mirror to society.

As an important problem in causal inference, we discuss the identification and estimation of treatment effects (TEs) under limited overlap; that is, when subjects with certain features belong to a single treatment group. We use a latent variable to model a prognostic score which is widely used in biostatistics and sufficient for TEs; i.e., we build a generative prognostic model. We prove that the latent variable recovers a prognostic score, and the model identifies individualized treatment effects. The model is then learned as \beta-Intact-VAE--a new type of variational autoencoder (VAE). We derive the TE error bounds that enable representations balanced for treatment groups conditioned on individualized features. The proposed method is compared with recent methods using (semi-)synthetic datasets.