Our second annual list highlights promising, private, U.S.-based companies that are using artificial intelligence in meaningful business-oriented ways.

CV is a nascent market but it contains a plethora of both big technology companies and disruptors. Technology players with large sets of visual data are leading the pack in CV, with Chinese and US tech giants dominating each segment of the value chain. Google has been at the forefront of CV applications since 2012. Over the years the company has hired several ML experts. In 2014 it acquired the deep learning start-up DeepMind. Google's biggest asset is its wealth of customer data provided by their search business and YouTube.

Technology, media and telecoms (TMT) regulators will ponder rather than act on AI. Increased use of AI to generate deepfakes in the US presidential campaign may be the catalyst for substantive regulation. Debate regarding access to, and ownership of, data will continue with little regulatory change. For many industries, the focus will remain on operational efficiency. AI-based virtual assistants will gain significant traction.

Artificial intelligence has long caused fear of job loss across many sectors as companies look for ways to cut costs, support workers and become more profitable. But new research suggests that even in STEM-based sectors like cybersecurity, AI simply can't replace some traits found only in humans, such as creativity, intuition and experience. There's no doubt, AI certainly has its place. And most business leaders agree that AI is important to the future success of their company. A recent survey found CEOs believe the benefits of AI include creating better efficiencies (62 percent), helping businesses remain competitive (62 percent), and allowing organizations to gain a better understanding of their customers, according to Ernst and Young.

Software has never played a more critical role in spaceflight. It has made it safer and more efficient, allowing a spacecraft to automatically adjust to changing conditions. According to Darrel Raines, a NASA engineer leading software development for the Orion deep space capsule, autonomy is particularly key for areas of "critical response time"--like the ascent of a rocket after liftoff, when a problem might require initiating an abort sequence in just a matter of seconds. Or in instances where the crew might be incapacitated for some reason. And increased autonomy is practically essential to making some forms of spaceflight even work.

Eric Hutto is President and Chief Operating Officer at Unisys Corporation. Artificial intelligence (AI) can help humans address many challenges, but it also creates challenges. We know AI has biases. We understand that AI may or may not draw fair and ethical conclusions all the time. Yet it's clear that AI is going to happen anyway.

Secretary of State Mike Pompeo seized on a U.N. report confirming Iranian weapons were used to attack Saudi Arabia in September and were part of an arms shipment seized months ago off Yemen's coast; State Department correspondent Rich Edson reports. A fire and an explosion struck a centrifuge production plant above Iran's underground Natanz nuclear enrichment facility early Thursday, analysts said, one of the most-tightly guarded sites in all of the Islamic Republic after earlier acts of sabotage there. The Atomic Energy Organization of Iran sought to downplay the fire, calling it an "incident" that only affected an under-construction "industrial shed," spokesman Behrouz Kamalvandi said. However, both Kamalvandi and Iranian nuclear chief Ali Akbar Salehi rushed after the fire to Natanz, a facility earlier targeted by the Stuxnet computer virus and built underground to withstand enemy airstrikes. The fire threatened to rekindle wider tensions across the Middle East, similar to the escalation in January after a U.S. drone strike killed a top Iranian general in Baghdad and Tehran launched a retaliatory ballistic missile attack targeting American forces in Iraq. While offering no cause for Thursday's blaze, Iran's state-run IRNA news agency published a commentary addressing the possibility of sabotage by enemy nations such as Israel and the U.S. following other recent explosions in the country.

Companies are making more use of algorithmic hiring tools to screen a flood of job applicants during the coronavirus pandemic amid questions about whether they introduce new forms of bias into the early vetting process. The tools are designed to more efficiently filter out candidates that don't meet certain job-related criteria, like prior work experience, and to recruit potential hires via their online profiles. Businesses like HireVue offer biometric scanning tools that give applicant feedback based on facial expressions, while others like Pymetrics use behavioral tests to home in on ideal candidates. Companies including Colgate-Palmolive Co., McDonald's Corp., Boston Consulting Group Inc., PricewaterhouseCoopers LLP, and Kraft Heinz Co. are using them at a time when 21 million people in the U.S. were without jobs and seeking employment in May, according to the Labor Department. Job candidates might be unable or unwilling to apply and interview in person because of rules limiting social gatherings, said Monica Snyder, a workplace privacy attorney at Fisher Phillips in Boston.

There is a sudden surge to model human behavior due to its vast and diverse applications which includes modeling public policies, economic behavior and consumer behavior. Most of the human behavior itself can be modeled into a choice prediction problem. Prospect theory is a theoretical model that tries to explain the anomalies in choice prediction. These theories perform well in terms of explaining the anomalies but they lack precision. Since the behavior is person dependent, there is a need to build a model that predicts choices on a per-person basis. Looking on at the average persons choice may not necessarily throw light on a particular person's choice. Modeling the gambling problem on a per person basis will help in recommendation systems and related areas. A novel hybrid model namely psychological factorisation machine ( PsychFM ) has been proposed that involves concepts from machine learning as well as psychological theories. It outperforms the popular existing models namely random forest and factorisation machines for the benchmark dataset CPC-18. Finally,the efficacy of the proposed hybrid model has been verified by comparing with the existing models.

As we seek to deploy machine learning models beyond virtual and controlled domains, it is critical to analyze not only the accuracy or the fact that it works most of the time, but if such a model is truly robust and reliable. This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses. We survey the most recent and important results in adversarial example generation, defense mechanisms with adversarial (re)Training as their main defense against perturbations. We also survey mothods that add regularization terms that change the behavior of the gradient, making it harder for attackers to achieve their objective. Alternatively, we've surveyed methods which formally derive certificates of robustness by exactly solving the optimization problem or by approximations using upper or lower bounds. In addition, we discuss the challenges faced by most of the recent algorithms presenting future research perspectives.