The High-Performance Computing Center Stuttgart (HLRS) has contracted with supercomputer manufacturer Cray, a Hewlett-Packard Enterprise company, to install a new Cray CS-Storm GPU-accelerated supercomputer. The new machine will complement HLRS's current infrastructure for high-performance computing by addressing user demand for processing-intensive applications like machine learning and deep learning. The system is designed for artificial intelligence (AI) applications and includes the Cray Urika-CS AI and Analytics suite, enabling HLRS to accelerate AI workloads, arm users to address complex computing problems, and process more data with higher accuracy. The machine will be used in developing AI models in engineering, automotive, energy, and environmental industries and academia. "As we extend our service portfolio with AI, we require an infrastructure that can support the convergence of traditional high-performance computing applications and AI workloads to better support our users and customers," said Prof. Dr. Michael Resch, director at HRLS. "We've found success working with our current Cray Urika-GX system for data analytics, and we are now at a point where AI and deep learning have become even more important as a set of methods and workflows for the HPC community. Our researchers will use the new CS-Storm system to power AI applications to achieve much faster results and gain new insights into traditional types of simulation results."

How much credit do you get if you're "pretty right" -- meaning, more right than wrong? If you're an artificial intelligence algorithm, you're given a lot of credit. AI programs don't have to have a definitive answer, just a probabilistic one, a percentage likelihood of the right answer, whether the task is performing natural-language translation or diagnosing cancer. The latest example of AI's probabilistic achievements is in this week's issue of Nature magazine, titled "International evaluation of an AI system for breast cancer screening," and is authored by an army of 31 scholars from Google's Google Health unit, its DeepMind unit, and the Imperial College of London, led by authors Scott Mayer McKinney, Marcin T. Sieniek, Varun Godbole, and Jonathan Godwin (DeepMind CEO Demis Hassabis is among the authors). In addition, a blog post gives commentary by Google's Google Health scholars Shravya Shetty, M.S., and Daniel Tse, M.D. Google's Google Health team, its DeepMind unit, and London's Imperial College used a trio of three different deep learning neural networks, consisting of, from the top, Facebook AI's "RetinaNet," combined with Google's "MobileNetV2," followed by the now standard ResNet-v2-50 in the middle section, and lastly a ResNet-v1-50 on the bottom layer.

No other technology was more important over the past decade than artificial intelligence. Stanford's Andrew Ng called it the new electricity, and both Microsoft and Google changed their business strategies to become "AI-first" companies. In the next decade, all technology will be considered "AI technology." And we can thank deep learning for that. Deep learning is a friendly facet of machine learning that lets AI sort through data and information in a manner that emulates the human brain's neural network.

Artificial Intelligence (AI) powered by Google's DeepMind algorithm may be more accurate at spotting breast cancer than real life doctors. The findings, published in Nature.com on Wednesday, come after researchers from Imperial College London and Google Health "trained" a computer to spot abnormalities on X-ray images of nearly 29,000 women. Separate studies used imagery from U.K. and U.S. women and concluded that in both countries the computer reduced instances where a cancer was either incorrectly identified or incorrectly missed. In the United States, the improvement was more noticeable -- offering a reported reduction of 5.7% in false positives, where a mammogram is wrongly diagnosed as abnormal. There was also a reduction of 9.4% in false negatives, where a cancer is missed. "In an independent study of six radiologists, the AI system outperformed all of the human readers," claimed the report.

The year 2019 saw unprecedented growth in AI research, development and deployment. Great technical progress has been achieved in image recognition, image generation, natural language understanding and other fields; while challenges remain with data management, efficiency measurement, computational capacity and other issues. To welcome 2020 with some fresh AI perspectives, Synced spoke with global researchers from Google Brain, Sony AI, Alibaba affiliate Ant Financial (formerly known as Alipay), Israel-based AI processor company Habana (recently acquired by Intel), Russian tech giant Yandex, Vietnam's newly established research lab VinAI Research, French deep learning inference acceleration startup Mipsology, and China-based remote sensing data platform TerraQuanta. Colin Raffel, Senior Research Scientist, Google Brain In 2019 the community made huge progress on learning from limited labels. MixMatch, UDA, S4L, and ReMixMatch produced huge gains on standard semi-supervised learning benchmarks.

A summary/gallery of some of the awesome student projects students in this class worked on. Without exception, we had amazing project presentations this semester.

We hear a lot about language translation with deep learning where the neural network learns a mapping from one language to another. In fact, Google translate uses one to translate to more than 100 languages. But, can we do a similar task with images? If it's possible to capture the intricacies of languages, it'll surely be possible to translate an image to another. Indeed, this shows the power of deep learning.

Human teams are able to easily perform collaborative manipulation tasks. However, for a robot and human to simultaneously manipulate an extended object is a difficult task using existing methods from the literature. Our approach in this paper is to use data from human-human dyad experiments to determine motion intent which we use for a physical human-robot co-manipulation task. We first present and analyze data from human-human dyads performing co-manipulation tasks. We show that our human-human dyad data has interesting trends including that interaction forces are non-negligible compared to the force required to accelerate an object and that the beginning of a lateral movement is characterized by distinct torque triggers from the leader of the dyad. We also examine different metrics to quantify performance of different dyads. We also develop a deep neural network based on motion data from human-human trials to predict human intent based on past motion. We then show how force and motion data can be used as a basis for robot control in a human-robot dyad. Finally, we compare the performance of two controllers for human-robot co-manipulation to human-human dyad performance.

Small satellite constellations provide daily global coverage of the earth's landmass, but image enrichment relies on automating key tasks like change detection or feature searches. For example, to extract text annotations from raw pixels requires two dependent machine learning models, one to analyze the overhead image and the other to generate a descriptive caption. We evaluate seven models on the previously largest benchmark for satellite image captions. We extend the labeled image samples five-fold, then augment, correct and prune the vocabulary to approach a rough min-max (minimum word, maximum description). This outcome compares favorably to previous work with large pre-trained image models but offers a hundred-fold reduction in model size without sacrificing overall accuracy (when measured with log entropy loss). These smaller models provide new deployment opportunities, particularly when pushed to edge processors, on-board satellites, or distributed ground stations. To quantify a caption's descriptiveness, we introduce a novel multi-class confusion or error matrix to score both human-labeled test data and never-labeled images that include bounding box detection but lack full sentence captions. This work suggests future captioning strategies, particularly ones that can enrich the class coverage beyond land use applications and that lessen color-centered and adjacency adjectives ("green", "near", "between", etc.). Many modern language transformers present novel and exploitable models with world knowledge gleaned from training from their vast online corpus. One interesting, but easy example might learn the word association between wind and waves, thus enriching a beach scene with more than just color descriptions that otherwise might be accessed from raw pixels without text annotation.

In this paper, we generalize the idea from the method called "PCANet" to achieve a new baseline deep learning model for image classification. Instead of using principal component vectors as the filter vector in "PCANet", we use basis vectors in discrete Fourier analysis and wavelets analysis as our filter vectors. Both of them achieve comparable performance to "PCANet" in benchmark datasets. It is noticeable that our algorithms do not require any optimization techniques to get those basis.