WASHINGTON – Facial recognition systems can produce wildly inaccurate results, especially for nonwhites, according to a U.S. government study released Thursday that is likely to raise fresh doubts on deployment of the artificial intelligence technology. The study of dozens of facial recognition algorithms showed "false positives" rates for Asians and African-Americans as much as 100 times higher than for whites. The researchers from the National Institute of Standards and Technology (NIST), a government research center, also found two algorithms assigned the wrong gender to black females almost 35 percent of the time. The study comes amid widespread deployment of facial recognition for law enforcement, airports, border security, banking, retailing, schools and for personal technology such as unlocking smartphones. Some activists and researchers have claimed the potential for errors is too great and that mistakes could result in the jailing of innocent people, and that the technology could be used to create databases that may be hacked or inappropriately used.

Smart cars may make our lives easier on the road, but they are also easily hacked by cyber criminals. Scientists have found'holes' in these systems that lets digital deviants access your data or worse, take over the vehicle. The first hole is when users plug their smartphone into their smart car, which is an open door for hackers to breach vehicle systems. However, another vulnerability lets users access features in order to take over the system and could ultimately crash the car. Experts are now calling on carmakers to release constant updates for the software in order to put an end to data breaches and save lives.

Our approach to time series anomaly detection is computationally efficient, automatically learns how to update probabilities and adapt to changes in data. As we describe in the next section, this approach has yielded successful attack detection at high precision. The proposed time series anomaly detection model was deployed and utilized by Microsoft Threat Experts to detect RDP brute force attacks during threat hunting activities. A list that ranks machines across enterprises with the lowest anomaly scores (indicating the likelihood of observing a value at least as large under expected conditions in all signals considered) is updated and reviewed every day. See Table 1 for an example.

The field of law has evolved surprisingly little since the days of Oliver Wendell Holmes, Jr. ... [ ] (1841-1935), considered by many to be the greatest U.S. Supreme Court justice in history. Virtually everything that companies do--sales, purchases, partnerships, mergers, reorganizations--they do via legally enforceable contracts. Innovation would grind to a halt without a well-developed body of intellectual property law. Day to day, whether we recognize it or not, each of us operates against the backdrop of our legal regime and the implicit possibility of litigation. At close to $1T globally, the legal services market is one of the largest in the world.

Sign in to report inappropriate content. Graham MackIntosh is a pioneer in the field of advanced analytics, working as an AI consultant for NASA and the SETI Institute. Hear how NASA is using AI to understand solar events, detect failures and expand our understanding of the universe.

Paige, a NYC-based leader in computational pathology transforming the diagnosis and treatment of cancer, today announced it has closed its Series B funding round of $45 million, bringing the Company's total capital raised to over $70 million. Healthcare Venture Partners brought the largest contribution to the round, with Breyer Capital, Kenan Turnacioglu, and other funds participating. Paige will use this new capital to drive FDA clearance of its products and expand its portfolio, delving deeper into cancer pathology, novel biomarkers, and prognostic capabilities. Additionally, the Company will accelerate commercial efforts in the U.S. and expansion in Europe, Brazil, and Canada. Pathology is the cornerstone of cancer diagnoses.

Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accuracy while little effort has been spent on the important task of understanding the association between the two data modalities. Performance on the time series hence receives little explanation though human-understandable textual information is available. In this work, we address the problem of given a numerical time series, and a general corpus of textual stories collected in the same period of the time series, the task is to timely discover a succinct set of textual stories associated with that time series. Towards this goal, we propose a novel multi-modal neural model called MSIN that jointly learns both numerical time series and categorical text articles in order to unearth the association between them. Through multiple steps of data interrelation between the two data modalities, MSIN learns to focus on a small subset of text articles that best align with the performance in the time series. This succinct set is timely discovered and presented as recommended documents, acting as automated information filtering, for the given time series. We empirically evaluate the performance of our model on discovering relevant news articles for two stock time series from Apple and Google companies, along with the daily news articles collected from the Thomson Reuters over a period of seven consecutive years. The experimental results demonstrate that MSIN achieves up to 84.9% and 87.2% in recalling the ground truth articles respectively to the two examined time series, far more superior to state-of-the-art algorithms that rely on conventional attention mechanism in deep learning.

PARIS – France has officially deployed its first armed drones, three American-built Reapers fitted with laser-guided missiles, in its fight against a jihadi insurrection in Africa's Sahel region, Defense Minister Florence Parly announced Thursday. The drones, which have already since 2014 provided surveillance support to the French anti-jihadi Barkhane mission in Mali, Niger and Burkina Faso, will from now on also be able to strike targets, she said. France joins a small club of countries, including the United States, Britain and Israel, that use armed, distance-piloted aircraft in combat. The Reapers will each carry two 250-kg (550-pound) laser-guided bombs, and are entering service after a series of operational tests carried out from the airbase in the Niger capital Niamey. "Their main missions remain surveillance and intelligence … but these can be extended to strikes," Parly said.

WASHINGTON – The United States said new evidence and analysis of weapons debris recovered from an attack on Saudi oil facilities on Sept. 14 indicates the strike likely came from the north, reinforcing its earlier assessment that Iran was behind the offensive. In an interim report of its investigation -- seen by Reuters ahead of a presentation on Thursday to the United Nations Security Council -- Washington assessed that before hitting its targets, one of the drones traversed a location approximately 200 km (124 miles) to the northwest of the attack site. "This, in combination with the assessed 900 kilometer maximum range of the Unmanned Aerial Vehicle (UAV), indicates with high likelihood that the attack originated north of Abqaiq," the interim report said, referring to the location of one of the Saudi oil facilities that were hit. It added the United States had identified several similarities between the drones used in the raid and an Iranian designed and produced unmanned aircraft known as the IRN-05 UAV. However, the report noted that the analysis of the weapons debris did not definitely reveal the origin of the strike that initially knocked out half of Saudi Arabia's oil production.