Economists have been studying the relationship between technological change, productivity and employment since the beginning of the discipline with Adam Smith's pin factory. It should therefore not come as a surprise that AI systems able to behave appropriately in a growing number of situations - from driving cars to detecting tumours in medical scans - have caught their attention. In September 2017, a group of distinguished economists gathered in Toronto to set out a research agenda for the Economics of Artificial Intelligence (AI). They covered questions such as what is economically unique about AI, what will be its impacts, and what are the right policies to enhance its benefits. I recently had the privilege of attending the third edition of this conference in Toronto, and to witness first-hand how this agenda has evolved in the last two years.

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.

This headline may seem a bit odd to you. Since data science has a huge impact on today's businesses, the demand for DS experts is growing. At the moment I'm writing this, there are 144,527 data science jobs on LinkedIn alone. But still, it's important to keep your finger on the pulse of the industry to be aware of the fastest and most efficient data science solutions. To help you out, our data-obsessed CV Compiler team analyzed some vacancies and defined the data science employment trends of 2019.

Machine learning and AI may be deployed on such grand tasks as finding exoplanets and creating photorealistic people, but the same techniques also have some surprising applications in academia: DeepMind has created an AI system that helps scholars understand and recreate fragmentary ancient Greek texts on broken stone tablets. These clay, stone or metal tablets, inscribed as much as 2,700 years ago, are invaluable primary sources for history, literature and anthropology. They're covered in letters, naturally, but often the millennia have not been kind and there are not just cracks and chips but entire missing pieces that may comprise many symbols. Such gaps, or lacunae, are sometimes easy to complete: If I wrote "the sp_der caught the fl_," anyone can tell you that it's actually "the spider caught the fly." But what if it were missing many more letters, and in a dead language, to boot?

The first neural network you want to build using squaring of numbers. Every time you want to learn about NNs or data science or AI, you search through google, you go through Reddit, get some GitHub codes. There is MNIST dataset, GANs, convolution layers, everywhere. Everybody is talking about neural networks. You pick up your laptop, run the code, Voila! it works.

Leverage GPUs on Lambda Cloud for Machine Learning and save huge. Our research papers have been accepted into the top machine learning and graphics conferences, including ICCV, SIGGRAPH Asia, NeurIPS, and ACM Transactions on Graphics (TOG).

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.