We propose regularization strategies for learning discriminative models that are robust to in-class variations of the input data. We use the Wasserstein-2 geometry to capture semantically meaningful neighborhoods in the space of images, and define a corresponding input-dependent additive noise data augmentation model. Expanding and integrating the augmented loss yields an effective Tikhonov-type Wasserstein diffusion smoothness regularizer. This approach allows us to apply high levels of regularization and train functions that have low variability within classes but remain flexible across classes. We provide efficient methods for computing the regularizer at a negligible cost in comparison to training with adversarial data augmentation. Initial experiments demonstrate improvements in generalization performance under adversarial perturbations and also large in-class variations of the input data.

GE Healthcare announced the Food and Drug Administration's 510(k) clearance of Critical Care Suite, a collection of artificial intelligence (AI) algorithms embedded on a mobile X-ray device. Built-in collaboration with UC San Francisco (UCSF), using GE Healthcare's Edison platform, the AI algorithms help to reduce the turn-around time it can take for radiologists to review a suspected pneumothorax, a type of collapsed lung. Additional partners in the development of Critical Care Suite include St. Luke's University Health Network, Humber River Hospital, and CARING – Mahajan Imaging – India. A prioritized "STAT" X-ray can sit waiting for up to eight hours for a radiologist's review1. However, when a patient is scanned on a device with Critical Care Suite, the system automatically analyzes the images by simultaneously searching for a pneumothorax.

Artificial intelligence and machine learning may be ideal for picking up the day-to-day tasks of running enterprises, but still fall flat when it comes to innovation or reacting to unforeseen or one-off events. While enterprise-grade AI is still a ways off, it's incumbent on business and IT leaders to start piloting and exploring the advantages AI potentially offers. That's the word coming out of a recent report from the MIT Task Force on the Work of the Future, which looked at AI as part of a broad range of changes sweeping the employment scene and workplace. "We are a long way from AI systems that can read the news, re-plan supply chains in response to anticipated events like Brexit or trade disputes, and adapt production tasks to new sources of parts and materials," state the report's authors, David Autor of the National Bureau of Economic Research, along with David Mindell and Elisabeth Reynolds, both with MIT. For starters, data – the fuel that propels AI decision-making – is not ready for the leap.

Artificial intelligence and machine learning may be ideal for picking up the day-to-day tasks of running enterprises, but still fall flat when it comes to innovation or reacting to unforeseen or one-off events. While enterprise-grade AI is still a ways off, it's incumbent on business and IT leaders to start piloting and exploring the advantages AI potentially offers. That's the word coming out of a recent report from the MIT Task Force on the Work of the Future, which looked at AI as part of a broad range of changes sweeping the employment scene and workplace. "We are a long way from AI systems that can read the news, re-plan supply chains in response to anticipated events like Brexit or trade disputes, and adapt production tasks to new sources of parts and materials," state the report's authors, David Autor of the National Bureau of Economic Research, along with David Mindell and Elisabeth Reynolds, both with MIT. For starters, data – the fuel that propels AI decision-making – is not ready for the leap.

The attack comes after Iran exceeded their enriched uranium stockpile limit in the nuclear deal. Secretary of State Mike Pompeo called on the international community to join him Saturday in condemning Iran for drone attacks on two Saudi oil facilities, which he described as "an unprecedented attack on the world's energy supply." "Tehran is behind nearly 100 attacks on Saudi Arabia while [President Hassan] Rouhani and [Foreign Minister Mohammad] Zarif pretend to engage in diplomacy," Pompeo tweeted, referring to the nation's president and foreign affairs minister. There is no evidence the attacks came from Yemen." Iran-backed Houthi rebels in Yemen claimed responsibility for the attack hours before Pompeo's tweet. The world's largest oil processing facility in Saudi Arabia and a major oil field were impacted, sparking huge fires at a vulnerable chokepoint for global energy supplies. "The United States will work with our partners and allies to ensure that energy markets remain well supplied and Iran is held accountable for its aggression," Pompeo concluded. According to multiple news reports that cited unidentified sources, the drone attacks affected up to half of the supplies from the world's largest exporter of oil, though the output should be restored within days. It remained unclear if anyone was injured at the Abqaiq oil processing facility and the Khurais oil field. Sen. Chris Murphy, D-Conn., who sits on the Senate Foreign Relations Committee, denounced Pompeo's description of the attack, calling it an "irresponsible simplification." "The Saudis and Houthis are at war.

Is Artificial Intelligence & Machine Learning A Bane To Cybersecurity? … if and when they learn to use AI and machine learning to do the dirty work.

These startups are applying AI to discover new drugs, remotely monitor patients, securely transfer patient data, and more. Healthcare has become a crucial area for artificial intelligence research and applications. Startups in the space are leveraging AI technology to help individual consumers, clinicians, and hospital systems improve everything from fitness to clinical trials to diagnostics. For example, consumers are adopting virtual assistants to inquire about symptoms and using applications to track fitness metrics. Meanwhile, radiologists are using computer vision to discern between malignant and benign cells, while hospital systems are deploying AI-driven software to analyze the financial risk of individual patients on behalf of insurers.

One morning a few weeks ago Stephen Foot, a warehouseman from Enfield, woke up in a London hospital to discover the unlikely harbinger of a coming medical revolution. This Ghost of Healthcare to Come took the form of a nephrologist at the end of his bed. "That was the last thing I was expecting," he tells me. "Somebody from the renal department to come and say, 'Oh, by the way, there's something going on that has sparked an alert on your kidney.'" Foot had entered hospital because of his foot.

On a recent weeknight, Dahlia and Adam Brown came home to their spacious Colonial on a quiet cul-de-sac in Marietta, Ga. The Browns both work demanding jobs and have two young sons. They bought the house in June using Knock, a company that's trying to revolutionize the real-estate industry with a "home trade-in platform" making it easier to buy and sell at once. That solution was ideal for the Browns, who are just as busy as most couples but more introverted, making the idea of prospective buyers tramping through their private space seem excruciating. Across town, Martha Seay was overseeing movers in a rambling brown ranch-style house nestled among tall hickory trees. The day before, she had closed on the sale of the house, where she and her husband had raised their family, to the real-estate company Zillow.

Utilizing ALCF supercomputing resources, Argonne researchers are developing the deep learning framework MaLTESE with autonomous -- or self-driving -- and cloud-connected vehicles in mind. This work could help meet demand to deliver better engine performance, fuel economy and reduced emissions. Researchers used nearly the full capacity of the ALCF's Theta system to simulate a typical 25-minute drive cycle of 250,000 vehicles. Researchers at Argonne are developing the deep learning framework MaLTESE (Machine Learning Tool for Engine Simulations and Experiments) to meet ever-increasing demands to deliver better engine performance, fuel economy and reduced emissions. Automotive manufacturers are facing an ever-increasing demand to deliver better engine performance, fuel economy and reduced emissions.