New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
One of the most promising applications of deep learning is image analysis (as part of computer vision), e.g. for image segmentation or classification. Whereas segmentation yields a probability distribution (also known as mask) for each class per pixel (i.e. each pixel belongs to 1 of K classes), classification does so for the whole image (i.e. each image belongs to 1 of K classes). Software solutions can be encountered nearly everywhere nowadays, for example in medical image analysis. In clinical research, where novel medications are tested, sometimes it is of interest if a drug can change the condition of a tissue, e.g. Medical images are created by imaging techniques such as medical ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), or even regular microscopes.
Since our recent release of Transformers (previously known as pytorch-pretrained-BERT and pytorch-transformers), we've been working on a comparison between the implementation of our models in PyTorch and in TensorFlow. We've released a detailed report where we benchmark each of the architectures hosted on our repository (BERT, GPT-2, DistilBERT, ...) in PyTorch with and without TorchScript, and in TensorFlow with and without XLA. We benchmark them for inference and the results are visible in the following spreadsheet. We would love to hear your thoughts on the process.
The field of learning has evolved drastically over the years. With the advent of e-learning and learning management systems, the process of learning has gone beyond the traditional model of classroom training. Now it is possible for instructors and teachers to reach a wider, international audience through online courses hosted on cloud based LMS platforms. Students can access these courses from any place in the world at any time, by simply logging into their account using their login credentials. Although e-learning is a complete and self-sustainable medium for imparting knowledge, it also works well in conjunction with traditional classroom training.
Tank warfare isn't traditionally easy to predict. In July 1943, for instance, German military planners believed that their advance on the Russian city of Kursk would be over in ten days. In fact, that attempt lasted nearly two months and ultimately failed. Even the 2003 Battle of Baghdad, in which U.S. forces had air superiority, took a week. The U.S. Army has launched a new effort, dubbed Project Quarterback, to accelerate tank warfare by synchronizing battlefield data with the aid of artificial Intelligence.
Breast cancer is the global leading cause of cancer-related deaths in women, and the most commonly diagnosed cancer among women across the world (1). From our perspective, improved treatment options and earlier detection could have a positive impact on decreasing mortality, as this could offer more options for successful intervention and therapies when the disease is still in its early stages. Our team of IBM researchers published research in Radiology around a new AI model that can predict the development of malignant breast cancer in patients within the year, at rates comparable to human radiologists. As the first algorithm of its kind to learn and make decisions from both imaging data and a comprehensive patient's health history, our model was able to correctly predict the development of breast cancer in 87 percent of the cases it analyzed, and was also able to correctly interpret 77 percent of non-cancerous cases. Our model could one day help radiologists to confirm or deny positive breast cancer cases.
Accenture's research predicts that AI use could double annual economic growth rates in more than a dozen developed economies by 2035. But as AI adoption grows, it will change the way businesses operate, forging a new relationship between humans and machines that's expected to increase labor productivity by up to 40 percent, Accenture says. Changing business dynamics through AI will depend largely upon the use of deep neural networks, an outgrowth of artificial neural networks. Harvard Business Review has estimated that 40 percent of the potential value created by analytics today comes from deep learning underpinned by DNNs. Artificial neural networks (ANNs) have existed in computational neurobiology since the late 1950s, when psychologist Frank Rosenblatt created what's known as perceptrons.
Deep learning also uses deduction, but in a linear, basic, and one-dimensional way. Training the artificial neural networks to classify lions as dangerous might make them sensitive only to lions. A bear can't get classified as dangerous automatically. Training them to identify a cat will only make them recognize a cat, but not deduce that a leopard belongs to the cat family. Similarly, through facial recognition, deep learning can tag faces on photos but might stumble when there are faces of siamese twins.
The University of Toronto and the affiliated Vector Institute for Artificial Intelligence have announced the recruitment of two rising stars in machine learning research as part of a continued drive to assemble the best AI talent in the world. Chris Maddison and Jakob Foerster will both come to U of T having completed their doctoral research at the University of Oxford. He earned his undergraduate and master's degrees in computer science at U of T – the latter under the supervision of University Professor Emeritus Geoffrey Hinton. A senior research scientist at Google-owned AI firm DeepMind, Maddison will join U of T's departments of computer science and statistical sciences in the Faculty of Arts & Science as an assistant professor next summer. Foerster, a research scientist at Facebook AI Research, will start as an assistant professor in the department of computer and mathematical sciences at U of T Scarborough in fall of 2020.
Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.