The world of technology, which often breaks down barriers, can significantly promote more integration of people with disabilities into social and work contexts. In particular, artificial intelligence solutions may allow the removal of accessibility barriers. For those who develop technology, it is essential not only to think about usability but increasingly about accessibility. Especially those who deal with AI have the opportunity to create systems and solutions that can really break down barriers for people with disabilities of various kinds. This opens up an important debate that must involve both the world of technology and all those involved in ethical issues.

Israeli startup Binah.ai says it has developed technology that turns smartphones into health monitoring devices that can check vital signs including heartrate, oxygen saturation and respiratory rate. The new technology comes as medical care worldwide has been stretched thin by the pandemic and other, longer-term trends, spurring demand for telemedicine and cheaper, more convenient health monitoring solutions. The user just needs to look into the camera to let the company's system measure their vital signs. Our skin is constantly undergoing rapid changes in color, too subtle for us to notice, that reflect our body's physical state and functioning. "Basically we're following around the tiny color changes that are happening to the skin and the tiny color changes indicate the blood flow that is happening below the skin surface," Maman said.

Robots in our daily surroundings are increasing day by day. Their usability and acceptability largely depend on their explicit and implicit interaction capability with fellow human beings. As a result, social behavior is one of the most sought-after qualities that a robot can possess. However, there is no specific aspect and/or feature that defines socially acceptable behavior and it largely depends on the situation, application, and society. In this article, we investigate one such social behavior for collocated robots. Imagine a group of people is interacting with each other and we want to join the group. We as human beings do it in a socially acceptable manner, i.e., within the group, we do position ourselves in such a way that we can participate in the group activity without disturbing/obstructing anybody. To possess such a quality, first, a robot needs to determine the formation of the group and then determine a position for itself, which we humans do implicitly. The theory of f-formation can be utilized for this purpose. As the types of formations can be very diverse, detecting the social groups is not a trivial task. In this article, we provide a comprehensive survey of the existing work on social interaction and group detection using f-formation for robotics and other applications. We also put forward a novel holistic survey framework combining all the possible concerns and modules relevant to this problem. We define taxonomies based on methods, camera views, datasets, detection capabilities and scale, evaluation approaches, and application areas. We discuss certain open challenges and limitations in current literature along with possible future research directions based on this framework. In particular, we discuss the existing methods/techniques and their relative merits and demerits, applications, and provide a set of unsolved but relevant problems in this domain.

Global demand for donated blood far exceeds supply, and unmet need is greatest in low- and middle-income countries; experts suggest that large-scale coordination is necessary to alleviate demand. Using the Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. While measuring actual donation rates remains a challenge, we measure donor action (e.g., making a donation appointment) as a proxy for actual donation. We develop automated policies for matching donors with donation opportunities, based on an online matching model. We provide theoretical guarantees for these policies, both regarding the number of expected donations and the equitable treatment of blood recipients. In simulations, a simple matching strategy increases the number of donations by 5-10%; a pilot experiment with real donors shows a 5% relative increase in donor action rate (from 3.7% to 3.9%). When scaled to the global Blood Donation tool user base, this corresponds to an increase of around one hundred thousand users taking action toward donation. Further, observing donor action on a social network can shed light onto donor behavior and response to incentives. Our initial findings align with several observations made in the medical and social science literature regarding donor behavior.

This paper presents the Spanish RoBERTa-base and RoBERTa-large models, as well as the corresponding performance evaluations. Both models were pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain from 2009 to 2019. We extended the current evaluation datasets with an extractive Question Answering dataset and our models outperform the existing Spanish models across tasks and settings.

On closer inspection, the patent is anything but mundane. That's because the inventor is not a human being – it is an artificial intelligence (AI) system …

Researchers in the UK have developed a machine learning biometric system capable of identifying individuals from the shape of their hands. The intent of the work is to aide in identifying offenders, particularly in cases of sexual offenders that have recorded their crimes, where hand information is often the only biometric signal available. The paper, entitled Hand-based Person Identification Using Global and Part-aware Deep Feature Representation Learning, and proposes a new ML framework called Global and Part-Aware Network (GPA-Net). In GPA-Net, two distinct 3D tensors (global and local) are obtained by passing the source image through stacked convolutional layers on the ResNet50 backbone network. Each of the analytical avenues will make an identity prediction.

Text classification is common among the application that we use on daily basis. For example, email providers use text classification to filter out spam emails from your inbox. The other most common use of text classification is in customer care where they use sentimental analysis to differentiate bad reviews from good reviews ADDI AI 2050. In recent years the English language text classification has come a long way, but training classification models on low resource language and varying lengths still pose difficulties. In this Zindi competition, we are provided with news articles written in the Chichewa language and we have to train our model on multi-label classification as there are 19 categories of news.

At first glance, a recently granted South African patent relating to a "food container based on fractal geometry" seems fairly mundane. The innovation in question involves interlocking food containers that are easy for robots to grasp and stack. On closer inspection, the patent is anything but mundane. That's because the inventor is not a human being – it is an artificial intelligence (AI) system called DABUS. DABUS (which stands for "device for the autonomous bootstrapping of unified sentience") is an AI system created by Stephen Thaler, a pioneer in the field of AI and programming.

Useful information (UI) is an elusive concept in neural networks. A quantitative measurement of UI is absent, despite the variations of UI can be recognized by prior knowledge. The communication bandwidth of feature maps decreases after downscaling operations, but UI flows smoothly after training due to lower Nyquist frequency. Inspired by the low-Nyqusit-frequency nature of UI, we propose the use of spectral roll-off points (SROPs) to estimate UI on variations. The computation of an SROP is extended from a 1-D signal to a 2-D image by the required rotation invariance in image classification tasks. SROP statistics across feature maps are implemented as layer-wise useful information estimates. We design sanity checks to explore SROP variations when UI variations are produced by variations in model input, model architecture and training stages. The variations of SROP is synchronizes with UI variations in various randomized and sufficiently trained model structures. Therefore, SROP variations is an accurate and convenient sign of UI variations, which promotes the explainability of data representations with respect to frequency-domain knowledge.