If we can protect videos, audio and photos with digital watermarking, why not AI models? This is the question my colleagues and I asked ourselves as we looked to develop a technique to assure developers that their hard work in building AI, such as deep learning models, can be protected. You may be thinking, "Protected from what?" Well, for example, what if your AI model is stolen or misused for nefarious purposes, such as offering a plagiarized service built on stolen model? This is an concern, particularly for AI leaders such as IBM. Earlier this month we presented our research at the AsiaCCS '18 conference in Incheon, Republic of Korea, and we are proud to say that our comprehensive evaluation technique to address this challenge was demonstrated to be highly effective and robust.

While overall adoption of artificial intelligence remains low among businesses (about 20% upon our last study), senior executives know that AI isn't just hype. Organizations across sectors are looking closely at the technology to see what it can do for their business. As they should--we estimate that 40% of all the potential value that can created by analytics today comes from the AI techniques that fall under the umbrella "deep learning," (which utilize multiple layers of artificial neural networks, so-called because their structure and function are loosely inspired by that of the human brain). In total, we estimate deep learning could account for between $3.5 trillion and $5.8 trillion in annual value. However, many business leaders are still not exactly sure where they should apply AI to reap the biggest rewards.

Artificial Intelligence (AI) is evolving at light-speed. Artificial systems are capable of outperforming human experts on many levels: crunching data, analysing legal documents, solving Rubix cubes, and winning games both ancient and modern. They can produce writing indistinguishable from their human counterparts, conduct research, pen pop songs, translate between multiple languages and even create and critique art. And AI-driven tasks like object detection, speech recognition and machine translation are becoming more sophisticated every day. These advances can be credited to many developments, from improved statistical approaches to increased computer processing powers.

As part of our effort to speed the deployment of GPU-accelerated high-performance computing and AI, we've more than tripled the number of containers available from our NVIDIA GPU Cloud (NGC) since launch last year. Users can now take advantage of 35 deep learning, high-performance computing, and visualization containers from NGC, a story we'll be telling in depth at this week's International Supercomputing Conference in Frankfurt. Over the past three years, containers have become a crucial tool in deploying applications on a shared cluster and speeding the work, especially for researchers and data scientists running AI workloads. These containers make deploying deep learning frameworks -- building blocks for designing, training and validating deep neural networks -- faster and easier. Installing frameworks is complicated and time consuming.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

For each hotel we receive dozens of images and face the challenge of choosing the most "attractive" image for each offer on our offer comparison pages, as photos can be just as important for bookings as reviews. Given that we have millions of hotel offers, we end up with more than 100 million images for which we need an "attractiveness" assessment. We addressed the need to automatically assess image quality by implementing an aesthetic and technical image quality classifier based on Google's research paper "NIMA: Neural Image Assessment". NIMA consists of two Convolutional Neural Networks (CNN) that aim to predict the aesthetic and technical quality of images, respectively. The models are trained via transfer learning, where ImageNet pre-trained CNNs are fine-tuned for each quality classification tasks.

New York (GenomeWeb) – Researchers from Princeton University and the Flatiron Institute's Center for Computational Biology have developed a deep learning approach that they say can predict the effects of genetic variants in noncoding regions on gene expression in specific tissues as well as on disease risk.

Finally, they tested the systems. In some cases, they used test problems with the same abstract factors as the training set -- like both training and testing the AI on problems that required it to consider the number of shapes in each image. In other cases, they used test problems incorporating different abstract factors than those in the training set. For example, they might train the AI on problems that required it to consider the number of shapes in each image, but then test it on ones that required it to consider the shapes' positions to figure out the right answer.

Over 160 companies working in artificial intelligence have signed a pledge not to develop lethal autonomous weapons. The pledge, which was signed by 2,400 individuals including representatives from Google DeepMind, the European Association for AI and University College London, says that signatories will "neither participate in nor support the development, manufacture, trade, or use of lethal autonomous weapons." The pledge was announced by Max Tegmark, president of the Future of Life Institute, which organized the effort. "I'm excited to see AI leaders shifting from talk to action, implementing a policy that politicians have thus far failed to put into effect. AI has huge potential to help the world -- if we stigmatize and prevent its abuse," he said in a statement.

I love design (UI, graphic, product, fashion, interior). I love great visuals, storytelling and conveying (complex) knowledge in a simple and visually striking manner. The intersection of (deep) tech and creativity is fascinating me. This resulted in a new "visual whitepaper" I prepared in the last months: "The Simple Guide to Deep Learning -- or What All These F*cking Buzzwords Actually Mean." The whitepaper IS for you if you notice all the hype about Deep Learning around you, are interested in the topic and want to learn the basics.