A new algorithm is enabling deep learning that is more collaborative and communication-efficient than traditional methods. Army researchers developed algorithms that facilitate distributed, decentralized and collaborative learning capabilities among devices, avoiding the need to pool all data at a central server for learning. "There has been an exponential growth in the amount of data collected and stored locally on individual smart devices," said Dr. Jemin George, an Army scientist at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory. "Numerous research efforts as well as businesses have focused on applying machine learning to extract value from such massive data to provide data-driven insights, decisions and predictions." However, none of these efforts address any of the issues associated with applying machine learning to a contested, congested and constrained battlespace, George said.

To help autonomous vehicles and robots potentially spot objects that lie just outside a system's direct line-of-sight, Stanford, Princeton, Rice, and Southern Methodist universities researchers developed a deep learning-based system that can detect objects, including words and symbols, around corners. "Compared to other approaches, our non-line-of-sight imaging system provides uniquely high resolutions and imaging speeds," said Stanford University's Chris Metzler, on the Rice University post, Cameras see around corners in real time with deep learning. "These attributes enable applications that wouldn't otherwise be possible," he added. To achieve this, the system relies on a laser that can capture detailed images of objects around corners in real time. Specifically, a light from a high-speed laser is beamed onto a wall, the light from the hidden area bounces back to the wall, and that light is reflected to a camera.

Starpoint Gemini 3 has reached a stage in which we'd love to get players involved. There's a lot You can do in the game and a lot more is on the way, but what we're really looking forward to is Your Feedback, with a capital F. Our previous successful endeavors in Early Access have shown us just how crucial that is to create, not a good game, but a great one. We'd like to invite everyone to join us in this.

The use of artificial intelligence has been a hot topic in cardiology for the past few years. For example, deep neural networks can be used to analyze ECG tracings and may be more accurate than human experts. Even with these advantages, there may be some hesitation on completely relying on this technology. In a recent research letter published in Nature Medicine, researchers developed a way to integrate smoothed adversarial examples for single-lead ECGs. Researchers found that when subtle adversarial perturbations that are indistinguishable to the human eye were added to ECG tracings, the misdiagnosis rate of the deep learning algorithm was 74%.

In this tutorial, you will learn how to automatically detect COVID-19 in a hand-created X-ray image dataset using Keras, TensorFlow, and Deep Learning. Like most people in the world right now, I'm genuinely concerned about COVID-19. I find myself constantly analyzing my personal health and wondering if/when I will contract it. At first, I didn't think much of it -- I have pollen allergies and due to the warm weather on the eastern coast of the United States, spring has come early this year. My allergies were likely just acting up. But my symptoms didn't improve throughout the day. I'm actually sitting here, writing the this tutorial, with a thermometer in my mouth; and glancing down I see that it reads 99.4 Fahrenheit. My body runs a bit cooler than most, typically in the 97.4 F range.

As in other fields of artificial intelligence and prior to the emergence of Deep Learning, especially deep neural networks, artificial vision research was focused on a traditional Machine Learning approach. The traditional machine learning approach relies on developers massaging the data to extract the most salient or significant aspects from the data they are dealing with; that is, time sequences of frames, or videos. In this case, both scientific research and application development have been centered around identifying the most significant image elements that would allow, for example, facial and body recognition of the people who appear in the images, tracking them from one frame to another, or classifying the vehicles that move through a given area. After extracting this meaningful data, statistical methods are then employed to transform the representation into a so-called "understanding" of the real visual environment by using clustering, support-vector machines (SVMs), and filtering algorithms (linear, non-linear, regression), among others. This means that the merits of any given application lie in how well researchers and developers are able to source and generate data from the raw processed frames and transform it into useful structured data.

As claimed by the authors, the bigger the models, the better it generalizes to perform better on downstream tasks, especially in the low-data regime. This also means that the technology stack, including libraries and hardware, used to train these enormous language models have to be optimized, which is the innovative part proposed by T-NLG. Optimal hardware setup and a reduction in model-parallelism degree allow T-NLG to be trained more efficiently with fewer GPUs (256) than the state-of-the-art at the time, Megatron-ML (1024 NVIDIA GPUs). It will be interesting to see how Microsoft researchers apply T-NLG in production across their different products and build more fluent chatbots and digital assistants for improving customer experience. Transformer models efficiently extend the context window that helps to improve language understanding while at the same time requiring less computation and speeding up training via a self-attention mechanism.

Today, the search for biological mechanisms at molecular scale can leverage an unprecedented amount of information. With the recent development of high-throughput technologies, data collection has received an enormous impulse that has radically changed the perspective toward molecular biology. The main protagonist of this shift is omic data--namely, experimental profiles with large coverage over multiple biological domains. Several levels of knowledge have become associated with emerging omic technologies [1–3]. The most widespread to date include DNA sequencing (genomics), microarrays and RNA sequencing (transcriptomics), DNA methylation and histone modifications (epigenomics), and protein or metabolite mass spectrometry (proteomics and metabolomics). As technology moves forward, its associated costs decrease, and a growing wealth of data is being generated.