Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We take the principled view of distributionally robust optimization, which guarantees performance under adversarial input perturbations. By considering a Lagrangian penalty formulation of perturbation of the underlying data distribution in a Wasserstein ball, we provide a training procedure that augments model parameter updates with worst-case perturbations of training data. For smooth losses, our procedure provably achieves moderate levels of robustness with little computational or statistical cost relative to empirical risk minimization. Furthermore, our statistical guarantees allow us to efficiently certify robustness for the population loss. For imperceptible perturbations, our method matches or outperforms heuristic approaches.

Investments in AI for drug discovery are surging. Big Pharmas are throwing big bucks. Sanofi signed a 300 Million dollars deal with the startup Exscientia, and GSK did the same for 42 Million dollars. The Silicon Valley VC firm Andreessen Horowitz launched a new 450 Million dollars bio investment fund, with one focus area in applications of AI to drug discovery. In this craze, lots of pharma/biotech companies and investors wonder whether they should jump on the bandwagon in 2018, or wait and see.

Artificial intelligence will soon change how we conduct our daily lives. Are companies prepared to capture value from the oncoming wave of innovation? Yes, they have a fine MRI machine and powerful software to generate the images. But that's where the machines bog down. The radiologist has to find and read the patient's file, examine the images, and make a determination. What if artificial intelligence (AI) could jump-start that process by enabling real-time and more accurate diagnoses or guidance, beyond what human eyes can see?

I am still working on this, but now many more people are interested. Because the methods we've created on the way to this goal are now permeating the modern world--available to half of humankind, used billions of times per day.

Big data has always been a part of high-performance computing and the science it supports, but new open-source technologies are now being applied to a wider range of scientific and business problems. We've spent time recently testing some of the big data toolkits. Two of the largest drivers of big data applications have been mobile applications and the Internet-of-Things (IoT). Smartphones contain a remarkable array of sensors and are very interesting as mobile devices for sensing and recording a user's environment. The increasing prevalence of low-power sensors monitoring air and water quality, traffic density and so on gives an opportunity to take better socio-economic decisions with better data.

Image recognition technology may be sophisticated, but it is also easily duped. Researchers have fooled algorithms into confusing two skiers for a dog, a baseball for espresso, and a turtle for a rifle. But a new method of deceiving the machines is simple and far-reaching, involving just a humble sticker. Google researchers developed a psychedelic sticker that, when placed in an unrelated image, tricks deep learning systems into classifying the image as a toaster. According to a recently submitted research paper about the attack, this adversarial patch is "scene-independent," meaning someone could deploy it "without prior knowledge of the lighting conditions, camera angle, type of classifier being attacked, or even the other items within the scene."

Investments in AI for drug discovery are surging. Big Pharmas are throwing big bucks. Sanofi signed a 300 Million dollars deal with the startup Exscientia, and GSK did the same for 42 Million dollars. The Silicon Valley VC firm Andreessen Horowitz launched a new 450 Million dollars bio investment fund, with one focus area in applications of AI to drug discovery. In this craze, lots of pharma/biotech companies and investors wonder whether they should jump on the bandwagon in 2018, or wait and see.

Deep neural networks (DNNs) have enabled great progress in a variety of application areas, including image processing, text analysis, and speech recognition. DNNs are also being incorporated as an important component in many cyber-physical systems. For instance, the vision system of a self-driving car can take advantage of DNNs to better recognize pedestrians, vehicles, and road signs. However, recent research has shown that DNNs are vulnerable to adversarial examples: Adding carefully crafted adversarial perturbations to the inputs can mislead the target DNN into mislabeling them during run time. Such adversarial examples raise security and safety concerns when applying DNNs in the real world.

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courvelle has been praised by the great creators of Artificial Intelligence, including Elon Musk, Facebook AI Chief Yann LeCun, and Microsoft CEO Satya Nadella. For more on what we mean by Deep Learning see Salesforce's Richard Socher On Tackling Limits of Deep Learning and AI. This textbook belongs in the library of any creator of tech today and is also a fantastic explainer for the curious power user with a thirst to understand what is really going on here. The book made such a significant impact on Satya Nadella that he hired co-author Yoshua Bengio to help shape the future of Microsoft's Artificial Intelligence approach as a machine learning advisor. Deep learning is also examined in far more detail in the Things Cyber Artificial Intelligence site, where we bring together the context you need to understand to make more sense of how this key topic will impact your life, including your family and business life.

This week's milestone in the history of technology is the patent that launched the ongoing quest to get machines to help us and them know more about our world, from tabulating machines to machine learning to deep learning (or today's "artificial intelligence"). On January 8, 1889, Herman Hollerith was granted a patent titled the "Art of Compiling Statistics." The patent described a punched card tabulating machine which launched a new industry and the fruitful marriage of statistics and computer engineering--called "machine learning" since the late 1950s, and reincarnated today as "deep learning" (also popularly known today as "artificial intelligence"). Commemorating IBM's 100th anniversary in 2011, The Economist wrote: In 1886, Herman Hollerith, a statistician, started a business to rent out the tabulating machines he had originally invented for America's census. Taking a page from train conductors, who then punched holes in tickets to denote passengers' observable traits (e.g., that they were tall, or female) to prevent fraud, he developed a punch card that held a person's data and an electric contraption to read it.