Investigators from Cedars-Sinai have created an artificial intelligence-enabled tool that may make it easier to predict if a person will have a heart attack. The tool, described in The Lancet Digital Health, accurately predicted which patients would experience a heart attack in five years based on the amount and composition of plaque in arteries that supply blood to the heart. Plaque buildup can cause arteries to narrow, which makes it difficult for blood to get to the heart, increasing the likelihood of a heart attack. A medical test called a coronary computed tomography angiography (CTA) takes 3D images of the heart and arteries and can give doctors an estimate of how much a patient's arteries have narrowed. Until now, however, there has not been a simple, automated and rapid way to measure the plaque visible in the CTA images.

Ukraine is using facial recognition software to help identify the bodies of Russian soldiers killed in combat and track down their families to inform them of their deaths, Ukraine's vice-prime minister told the Reuters news service. Mykhailo Fedorov, Ukraine's vice-prime minister who also runs the ministry of digital transformation, told Reuters his country had been using software facial recognition provider Clearview AI to find the social media accounts of dead Russian soldiers. "As a courtesy to the mothers of those soldiers, we are disseminating this information over social media to at least let families know that they they've lost their sons and to then enable them to come to collect their bodies," Fedorov said in an interview, speaking via a translator. Ukraine's Ministry of Defense this month began using technology from Clearview, which scrapes images on the web to match with faces featured in uploaded photos. Reuters first reported Ukraine's use of Clearview earlier this month, but it was not clear at that time how the technology would be used.

The autonomous driving industry in China has enjoyed a boom over the past two years, with both institutional and corporate investors pouring money into a driverless future. Companies thriving in the downstream, those offering robotaxi services, operating robo buses, or dispatching delivery bots, have been particularly popular with investors, raising hundreds of millions of dollars and reaching massive valuations. The prospects of becoming cash-rich and a household name have lured some of their upstream suppliers to start building end solutions as well. One of these ambitious self-driving hardware suppliers is PIX Moving, a Chinese company specializing in automotive skateboards -- a type of chassis that houses the batteries, drive units and other key components, and can be adapted to various kinds of self-driving scenarios because of its modular architecture -- similar to what Canoo does. Founded by former architect Chuan Yu in 2014, PIX recently secured 72 million yuan ($11 million) from a Series pre-A round, lifting its capital raised to $20 million, it told TechCrunch.

It's been years since developers found that Nvidia's main product, the GPU, was useful not just for rendering video games but also for high-performance computing of the kind used in 3D modeling, weather forecasting, or the training of AI models--and it's on enterprise applications such as those that CEO Jensen Huang will focus his attention at the company's GTC 2022 conference this week. Nvidia is hoping to make it easier for CIOs building digital twins and machine learning models to secure enterprise computing, and even to speed the adoption of quantum computing with a range of new hardware and software. Digital twins, numerical models that reflect changes in real-world objects useful in design, manufacturing, and service creation, vary in their level of detail. For some applications, a simple database may suffice to record a product's service history--when it was made, who it shipped to, what modifications have been applied--while others require a full-on 3D model incorporating real-time sensor data that can be used, for example, to provide advanced warning of component failure or of rain. It's at the high end of that range that Nvidia plays.

Isolation Forest is a simple yet incredible unsupervised algorithm that is able to spot outliers or anomalies in a data set very quickly. I should say understanding this tool is a must for any aspiring data scientist. In this article, I will briefly go through the theories behind the algorithm and also its implementation. Its Python implementation from Scitkit Learn has been gaining tons of popularity due to its capabilities and ease of use. But before we jump right into the implementation, it's always best practice for us to study about its use cases and the theory behind it.

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Affirm, Inc. proudly includes Affirm, PayBright, and Returnly. Affirm's Machine Learning team solves problems critical to our business model - personalizing shopping experiences, detecting fraud, optimizing interest rates, and assessing creditworthiness in real time. Our innovative products necessitate the creation of novel machine learning solutions to drive both existing and new products. Affirm is proud to be a remote-first company!

Ukraine is using facial recognition software to identify the bodies of Russian soldiers killed in combat and to trace their families to inform them of their deaths, Ukraine's vice prime minister has said. Reuters exclusively reported that Ukraine's Ministry of Defense began this month to use technology from Clearview AI, a New York-based facial recognition provider that finds images on the web that match faces from uploaded photos. It was not clear at that time how the technology would be used. Mykhailo Fedorov, Ukraine's vice prime minister who also runs the ministry of digital transformation, said Ukraine had been using Clearview AI software to find the social media accounts of dead Russian soldiers. From there, authorities are messaging relatives to make arrangements to collect the body, he said.

Named entity recognition (NER) is the process of recognising and classifying important information (entities) in text. Proper nouns, such as a person's name, an organization's name, or a location's name, are examples of entities. The NER is one of the important modules in applications like human resources, customer support, search engines, content classification, and academia. In this work, we consider NER for low-resource Indian languages like Hindi and Marathi. The transformer-based models have been widely used for NER tasks. We consider different variations of BERT like base-BERT, RoBERTa, and AlBERT and benchmark them on publicly available Hindi and Marathi NER datasets. We provide an exhaustive comparison of different monolingual and multilingual transformer-based models and establish simple baselines currently missing in the literature. We show that the monolingual MahaRoBERTa model performs the best for Marathi NER whereas the multilingual XLM-RoBERTa performs the best for Hindi NER. We also perform cross-language evaluation and present mixed observations.

Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction. Enabling humans to effectively interact and team with a swarm of autonomous cognitive agents is an open research challenge in Human-Swarm Teaming research, partially due to the focus on developing the enabling architectures to support these systems. Typically, bi-directional transparency and shared semantic understanding between agents has not prioritised a designed mechanism in Human-Swarm Teaming, potentially limiting how a human and a swarm team can share understanding and information\textemdash data through concepts and contexts\textemdash to achieve a goal. To address this, we provide a formal knowledge representation design that enables the swarm Artificial Intelligence to reason about its environment and system, ultimately achieving a shared goal. We propose the Ontology for Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming between humans and teams through the biologically-inspired approach of shepherding.

The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models, which however are not appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we propose the first machine unlearning algorithm for MCMC. We first convert the MCMC unlearning problem into an explicit optimization problem. Based on this problem conversion, an MCMC influence function is designed to provably characterize the learned knowledge from data, which then delivers the MCMC unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not compromise the generalizability of the MCMC models. Experiments on Gaussian mixture models and Bayesian neural networks confirm the effectiveness of the proposed algorithm. "The right to be forgotten" refers to the right of individuals to request data controllers such as tech giants to delete the data collected from them. It has been recognized in many countries through legislation, including the European Union's General Data Protection Regulation (2016) and the California Consumer Privacy Act (2018).