Enterprise security has always been a cat and mouse game, with cyber adversaries constantly evolving their attack systems to get past defenses. Can AI based systems help in warding off new age threats and zero day attacks. To get a perspective, we spoke with Vikas Arora, IBM Cloud and Cognitive Software Leader, IBM India/South Asia, who shares his view on how AI can impact enterprise security. What are your views on the cyber security landscape in India? Which sectors do you think are the most vulnerable today?

The Stanford Institute for Human-Centered AI officially launched today. Stanford HAI seeks to become an interdisciplinary global AI hub and to fundamentally change the field of AI by integrating a wide range of disciplines and prioritizing true diversity of thought. Researchers in Korea analyzed literature evaluating 516 AI algorithms for medical image analysis and found that only 6% validated their AI and 0% were ready for clinical use. This lack of appropriate clinical validation is referred to as digital exceptionalism. An analysis of 47 biomedical unicorns found that most of the highest valued startups in healthcare have a limited or non‐existent participation in the publicly available scientific literature.

A unit of the U.S. Department of Commerce has been using photos of immigrants, abused children and dead people to train their facial recognition systems, a worrying new report has detailed. The National Institute of Standards and Technology (NIST) oversees a database, called the Facial Recognition Verification Testing program, that'depends' on these types of controversial images, according to Slate. Scientists from Dartmouth College, the University of Washington and Arizona State University discovered the practice and laid out their findings in new research set to be reviewed for publication later this year. A unit of the U.S. Department of Commerce has been using photos of immigrants, abused children and dead people to train their facial recognition systems, a new report has detailed. The Facial Recognition Verification Testing program was first established in 2017 as a way for companies, academic researchers and designers to evaluate their facial recognition technologies.

The past few years have seen several works establishing PAC frameworks for solving various problems in economic domains; these include optimal auction design, approximate optima of submodular functions, stable partitions and payoff divisions in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling these problems, and establish some useful tools for determining whether a given economic solution concept can be learned from data. Our learning theoretic framework generalizes a notion of function space dimension --- the graph dimension --- adapting it to the solution concept learning domain. We identify sufficient conditions for the PAC learnability of solution concepts, and show that results in existing works can be immediately derived using our general methodology. Finally, we apply our methods in other economic domains, yielding a novel notion of PAC competitive equilibrium and PAC Condorcet winners.

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.

If you thought IBM using "quietly scraped" Flickr images to train facial recognition systems was bad, it gets worse. Our research, which will be reviewed for publication this summer, indicates that the U.S. government, researchers, and corporations have used images of immigrants, abused children, and dead people to test their facial recognition systems, all without consent. The very group the U.S. government has tasked with regulating the facial recognition industry is perhaps the worst offender when it comes to using images sourced without the knowledge of the people in the photographs. The National Institute of Standards and Technology, a part of the U.S. Department of Commerce, maintains the Facial Recognition Verification Testing program, the gold standard test for facial recognition technology. This program helps software companies, researchers, and designers evaluate the accuracy of their facial recognition programs by running their software through a series of challenges against large groups of images (data sets) that contain faces from various angles and in various lighting conditions.

It's not a perfect measure, but unit sales of industrial robots give some idea of a country's industrial might. The names of the top five buyers in 2017 – China, Japan, South Korea, the US and Germany – shouldn't be too surprising. The global average is 74 per 10,000. One factor in this is the small electronics and automotive sectors here, which are two major drivers of industrial robot investment. The high number of SME and micro-businesses in Australian manufacturing is another.

In this paper, we propose a novel approach to tackle the multiple instance regression (MIR) problem. This problem arises when the data is a collection of bags, where each bag is made of multiple instances corresponding to the same unique real-valued label. Our goal is to train a regression model which maps the instances of an unseen bag to its unique label. This MIR setting is common to remote sensing applications where there is high variability in the measurements and low geographical variability in the quantity being estimated. Our approach, in contrast to most competing methods, does not make the assumption that there exists a prime instance responsible for the label in each bag. Instead, we treat each bag as a set (i.e, an unordered sequence) of instances and learn to map each bag to its unique label by using all the instances in each bag. This is done by implementing an order-invariant operation characterized by a particular type of attention mechanism. This method is very flexible as it does not require domain knowledge nor does it make any assumptions about the distribution of the instances within each bag. We test our algorithm on five real world datasets and outperform previous state-of-the-art on three of the datasets. In addition, we augment our feature space by adding the moments of each feature for each bag, as extra features, and show that while the first moments lead to higher accuracy, there is a diminishing return.

Companies in Japan have a little more than six weeks to revamp their computer software to respond to the country's first era change in the digital age when a new Emperor is enthroned on May 1. The Ministry of Economy, Trade and Industry, or METI, is calling on companies to check where they use the Japanese calendar in their computer systems, modify necessary programs and carry out tests to detect potential problems. Eras are how Japan defines its history, so drivers' licenses, newspapers and a host of official documents use it to mark the years, with 2019 currently referred to as the "31st year of Heisei." The government will announce on April 1 the name of the new era, which will begin on May 1 in line with Crown Prince Naruhito's accession to the throne. According to the Information-Technology Promotion Agency, an independent administrative agency, systems using the current Heisei and other era names require program modifications.

Because of the government shut down earlier in the year, there was a delay in with IPOs as the SEC could not evaluate the filings. But now it looks like the market is getting ready for a flood of deals. One of the first will be PagerDuty, which was actually founded during the financial crisis of 2009. The core mission of the company is "to connect teams to real-time opportunity and elevate work to the outcomes that matter." Interestingly enough, PagerDuty refers to itself as the central nervous system of a digital enterprise.