Portland, OR, Aug. 19, 2022 (GLOBE NEWSWIRE) -- According to the report published by Allied Market Research, the global disability insurance market amassed a revenue of $3.3 billion in 2021, and is expected to hit $9.2 billion by 2031, registering a CAGR of 11.2% from 2022 to 2031. The market research study provides a detailed analysis of changing industry trends, top-most segments, value chain analysis, key investment business scenarios, regional space, and competitive space. The study is a key information source for giant players, entrepreneurs, shareholders, and owners in generating new strategies for the future and taking steps to enhance their market position. The report offers a detailed segmentation of the global disability insurance market based on insurance type, coverage type, end user, and region. It provides an in-depth analysis of every segment and sub-segment in tables and figures through which consumers can derive a conclusion about market trends and insights.

Round particles and their properties are easy to describe mathematically. But the less round or spherical the shape, the harder it becomes to make predictions about their behavior. In his doctoral thesis at the Technical University of Kaiserslautern (TUK), Robert Hesse has trained a neural network to automatically determine the packing density and flowability of non-spherical particles. Few particles in nature or in industrial production are exactly round; instead, there are a multitude of variants and shape characteristics. This is exactly what makes it so complicated to describe non-spherical particles and optimize their handling based on the description.

Al Tayer added: "In accordance with the wise leadership vision and directives, we continue to develop world-class infrastructure to keep pace with the growing demand for electricity and water in Dubai. The total production capacity of DEWA's desalinated water has reached 490 million imperial gallons per day (MIGD). We are keen to apply the best international practices in all our projects and adopt the latest technologies in the generation, transmission, distribution and control of electricity and water networks to raise production and operational efficiency.

In this article, you learn how to create and run machine learning pipelines by using the Azure Machine Learning SDK. Use ML pipelines to create a workflow that stitches together various ML phases. Then, publish that pipeline for later access or sharing with others. Track ML pipelines to see how your model is performing in the real world and to detect data drift. ML pipelines are ideal for batch scoring scenarios, using various computes, reusing steps instead of rerunning them, and sharing ML workflows with others. For guidance on creating your first pipeline, see Tutorial: Build an Azure Machine Learning pipeline for batch scoring or Use automated ML in an Azure Machine Learning pipeline in Python.

You are free to share this article under the Attribution 4.0 International license. Cognitive behavioral therapy for chronic pain supported by artificial intelligence can yield the same results as programs delivered by therapists, a new study shows. Cognitive behavioral therapy (CBT) is an effective alternative to opioid painkillers for managing chronic pain. But getting patients to complete those programs is challenging, especially because psychotherapy often requires multiple sessions and mental health specialists are scarce. AI-supported therapy requires substantially less clinician time, making it more accessible to patients, the researchers report.

If you're finding it harder to get access to GPUs in the cloud to train your AI model, you're not alone. The combination of a global chip shortage and increased demand for AI model training may be leading to longer wait times for some cloud GPU users. Some GPU users are waiting longer to access cloud-based GPUs than they are accustomed to waiting, according Gigaom AI analyst Anand Joshi. While Joshi doesn't have any firsthand knowledge of cloud platform's GPU expansion plans, he said the wait times customers are experiencing are an indication that the cloud platforms have not been able to obtain new GPUs at the pace they had expected or wanted. That, he says, may be impacting their ability to expand GPU cloud environments to keep up with increasing demand for model training, which is the most computational demanding component of the AI lifecycle.

Researchers at Google LLC have devised a new way for robots to understand what people want by teaching them how language fits with the real world. People already interact daily speaking naturally with chatbots on their phones to do internet searches, set alarms and even order pizzas. But what if you could call to your Roomba and say, "Hey, I'm thirsty, get me something to drink," and have a Coke arrive from the fridge? To make this sort of thing happen, Google Research is combining efforts with Everyday Robots, a helper robot maker, to do exactly that. The research, announced Tuesday, is called PaLM-SayCan, It uses PaLM, or Pathways Language Model, with an Everyday Robots helper robot to do ordinary tasks around a micro-kitchen on a Google campus.

Lastly, our optimiser is wrapped by Horovod's implementation for distributed optimisation (which handles the all-gather and all-reduce MPI operations). We next assign training callbacks to GPU processors based on the processor's (unique) global rank. By default, rank-0 is designated as the root node. There are some operations we only need executing on a single node (for example, using a model checkpoint to save model weights to file). Each processor will effectively run their own training job which optionally prints training accuracy, loss, and custom metrics to CloudWatch.

As software engineers ourselves, we know how critical it is to know about a new error before it makes its way to production and is discovered by your customers. Not all errors should be treated equally. Some errors are critical and should be addressed immediately, and others can wait. What we have learned along the way is that first-time errors are business-critical. To quickly jump right in and have all the error context at your fingertips is something that can help provide excellent customer service and user experience.

To evaluate the ability of fine-grained annotations to overcome shortcut learning in deep learning (DL)–based diagnosis using chest radiographs. Two DL models were developed using radiograph-level annotations (disease present: yes or no) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. A total of 34 501 chest radiographs obtained from January 2005 to September 2019 were retrospectively collected and annotated regarding cardiomegaly, pleural effusion, mass, nodule, pneumonia, pneumothorax, tuberculosis, fracture, and aortic calcification. The internal classification performance and lesion localization performance of the models were compared on a testing set (n 2922); external classification performance was compared on National Institutes of Health (NIH) Google (n 4376) and PadChest (n 24 536) datasets; and external lesion localization performance was compared on the NIH ChestX-ray14 dataset (n 880). The models were also compared with radiologist performance on a subset of the internal testing set (n 496).