The demand for artificial intelligence has grown significantly in the last few years due to the advantages it provides. Rising use of big data, growing demand for greater transparency and visibility into supply chain data and processes, and increasing adoption of AI for improving consumer services and satisfaction are some of the other factors driving the demand in this market. Moreover, growing applicability of AI in various industries has further augmented the demand in this market. The global artificial intelligence in supply chain market could be classified on basis of technology, application, end-user industry, and offerings. The end-user industry category can further be segmented into manufacturing, aerospace, automotive, retail, consumer packaged goods, healthcare, food and beverages, and others.

This article is the result of working in Omdena's AI challenge to estimate crops yield with the UN World Food Program in Nepal. The problem was tough, challenges were huge, and resources scarce. Still, a community of 36 collaborators managed to build a solution with 89% accuracy. This article details the problems we had to solve and lessons learned in creating an appropriate dataset. It's estimated, that every day 821 million people -- one in nine -- still go to bed on an empty stomach each night.

Western democracies are confronted with collapsing trust in their governments and surging nationalist movements. These phenomena have a particularly cruel timing, occurring contemporaneously with the rise of complex, global problems that urgently require coordinated, collective action. As the world becomes increasingly complicated and interconnected, the decisions of governments, corporations, and large institutions have become less accessible to the average citizen. Whether in relation to data privacy, climate change, or automation and the Fourth Industrial Revolution, citizens are being asked to accept decisions made by experts whose knowledge they can't match or evaluate. The average citizen, whose voice and consent was once proclaimed as essential to the functioning of democracy and legitimized political power, is now being asked to yield more authority to experts.

This challenge of making quantum-enhanced AI (QAI) applications accessible for a wide variety of users lies at the heart of the PlanQK project. The project aims to develop an open platform and ecosystem for QAI to connect experts and developers as well as users, customers, service providers and consultants. The PlanQK platform will comprise QAI algorithms, applications and data pools. It will be the technical basis for the creation of a QAI community and facilitate the exchange of QAI knowledge and technology. In this way, the PlanQK platform will make quantum-enhanced AI available to industry and, in particular, SMEs and enable them to make use of these key technologies. The PlanQK project is one of 15 projects selected from more than 130 proposals initially submitted to the innovation contest "Artificial intelligence as a driver for economically relevant ecosystems" by the Federal Ministry of Economics and Energy (BMWi).

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In May of last year, after a 13-month slumber, the ground beneath Washington's Puget Sound rumbled to life. The quake began more than 20 miles below the Olympic mountains and, over the course of a few weeks, drifted northwest, reaching Canada's Vancouver Island. It then briefly reversed course, migrating back across the US border before going silent again. All told, the monthlong earthquake likely released enough energy to register as a magnitude 6. By the time it was done, the southern tip of Vancouver Island had been thrust a centimeter or so closer to the Pacific Ocean.

The Trinity team are not proposing anything quite so drastic. They are inviting computer savvy individuals -- whether lovelorn or not -- to consider the future of meaningful relationships and how technology will change them via an interactive conversation that will begin with the question: Would you swap your partner for a robot designed to be "the one"?

Doing business in Sudan isn't easy, but back in 2011, it should have been possible. "We were working with major telecoms company Zain based out of southern Sudan at the time – before the country split in two," explains Charlie Tryon, chief executive of Maris, an investment holding company that operates across east and southern Africa. "Sudan was on the US's Office of Foreign Assets Control (OFAC) sanctions list, but the south was exempt, so it should have been OK for us to work here. We took out all the precautions we needed – did all the necessary paperwork, spoke to all the right people, told the banks, made sure we had permission from OFAC and notified all other regulatory bodies about our cross-border transaction – everything. "But the money we transferred from our business bank account to Zain was still stopped," he says. Tryon's transaction failed to pass compliance tests. In a bind, he was forced to physically move thousands of dollars from a bank account in Uganda to Sudan to pay his suppliers – at great personal risk. "We had suitcases full of cash that we took via plane and car into Sudan," says Tryon. "This isn't the ideal way to run a business, but at the time we had very little choice." A lot has changed since 2011. Know-your-customer and anti-money laundering (AML) screening is increasingly automated, helping to remove some of the delays caused by strict compliance measures. On top of this, banks, retailers, payment service providers (PSPs) and other businesses involved in the money transfer process are using artificial intelligence (AI) to make much more accurate decisions about payments. "At the time, we spent a huge amount of money and time trying to set this right," says Tryon. "It included international travel, lobbying, meetings with banks and regulators.

State Farm is almost the opposite of a tech startup. State Farm is #36 on the Fortune 500 and has been the #1 insurer in the United States for the last 70 years. But three years ago, State Farm executives knew they needed a digital transformation of basically Biblical proportions to continue to be relevant in the digital age. They had "dabbled" in online but failed, by their own admission, to put all the innovations together successfully. They had hired a consultant, automated existing business processes, but had not moved the needle on customer experience, revenue, or profits. As a result, the brand was getting less relevant in an app and digital-centric era, and market share started to suffer.

Session 3A: Deep Learning and Adversarial ML - 02 VulDeePecker: A Deep Learning-Based System for Vulnerability Detection SUMMARY The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning.