Seyfarth Synopsis: As companies face increasing competition for the best talent within the marketplace, a growing number of businesses are turning to artificial intelligence and data driven strategies to more effectively identify and evaluate potential employees. The first installment of our artificial intelligence series will focus on some of the ways that employers are using these technologies in the area of talent acquisition. Business has always been in a search for "the next big thing." Something to give them an edge over competitors or allow them to anticipate shifts in the marketplace before they happen. Companies who moved from hand production to large-scale manufacturing were able to dominate nascent markets around the turn of the 20th Century.

Here is our annual list of technological advances that we believe will make a real difference in solving important problems. We avoid the one-off tricks, the overhyped new gadgets. Instead we look for those breakthroughs that will truly change how we live and work. We're excited to announce that with this year's list we're also launching our very first editorial podcast, Deep Tech, which will explore the the people, places, and ideas featured in our most ambitious journalism. Later this year, Dutch researchers will complete a quantum internet between Delft and the Hague. An internet based on quantum physics will soon enable inherently secure communication. A team led by Stephanie Wehner, at Delft University of Technology, is building a network connecting four cities in the Netherlands entirely by means of quantum technology.

The U.S. Department of Defense officially adopted a series of ethical principles for the use of Artificial Intelligence today following recommendations provided to Secretary of Defense Dr. Mark T. Esper by the Defense Innovation Board last October. The recommendations came after 15 months of consultation with leading AI experts in commercial industry, government, academia and the American public that resulted in a rigorous process of feedback and analysis among the nation's leading AI experts with multiple venues for public input and comment. "The United States, together with our allies and partners, must accelerate the adoption of AI and lead in its national security applications to maintain our strategic position, prevail on future battlefields, and safeguard the rules-based international order," said Secretary Esper. "AI technology will change much about the battlefield of the future, but nothing will change America's steadfast commitment to responsible and lawful behavior. The adoption of AI ethical principles will enhance the department's commitment to upholding the highest ethical standards as outlined in the DOD AI Strategy, while embracing the U.S. military's strong history of applying rigorous testing and fielding standards for technology innovations."

Sci-fi movies have created an impact on our minds that using robots in our life is a very bad idea. From The Terminator to The Matrix, almost every Hollywood movie shows that robots took control over humanity. Even RUR, the 1920s Karel Capek play introduced the term "robot,". Despite the cinematic warnings robots have moved from fiction stories to an important piece of modern world arsenal. Now the developed world is also debating on the point to use develop killer robots and machine to save human life. In 1960, a company started building something that meets the guidelines of making a robot, that's when SRI International in Silicon Valley developed first truly perceptive and mobile robot known as SHAKY.

Cybersecurity is a bit like a game of chess. The winner will be the player who can best discern the opponent's intentions and anticipate their moves. So how has the advent of artificial intelligence changed the rules of the game? Alarmists might disagree, but artificial intelligence (AI), like any other technology, is neither good nor bad -- it all depends how people use it. And the role of AI in cybersecurity is a good example: hackers use it to do harm, cybersecurity experts use it to help thwart their attacks.

The National Transportation Safety Board held a hearing on Tuesday regarding a deadly 2018 crash in which a Tesla Model X slammed into a Mountain View highway divider at 70mph, was subsequently struck by two other vehicles and then exploded. During that announcement, the safety board revealed that the driver, Apple developer Walter Huang, was playing a mobile game on his phone at the time of the accident, while the vehicle's Autopilot feature was engaged. "Government regulators have provided scant oversight" over the semi-autonomous driving systems that are quickly becoming standard features on modern automobiles, NTSB chair Robert Sumwalt declared. While the NTSB does not have the authority to enforce safety measures, the National Highway Traffic Safety Administration can issue recalls for unsafe vehicle tech. The NTSB also determined via cellphone records and device data that Huang's phone was running a mobile game at the time of the crash.

The following is a summary of my predictions of the ICT trends for 2020. They have been selected because of their impact on the networking industry, and they forecast what is expected to happen or start happening, within the next 12 months. This information incorporates input and insights from several sources, available in a supporting document. "A machine with basic reading capabilities will be able to read everything the human race has ever written by lunchtime, and then it will be looking around for something else to do." – Stuart Russell, Human Compatible Welcome to the cognitive era. Compute costs will continue to head towards zero.

Computers using artificial intelligence are discovering medicines, designing better golf clubs and creating video games. Patent offices around the world are grappling with the question of who -- if anyone -- owns innovations developed using AI. The answer may upend what's eligible for protection and who profits as AI transforms entire industries. "There are machines right now that are doing far more on their own than to help an engineer or a scientist or an inventor do their jobs," said Andrei Iancu, director of the U.S. Patent and Trademark Office. "We will get to a point where a court or legislature will say the human being is so disengaged, so many levels removed, that the actual human did not contribute to the inventive concept."

Is there a classifier that ensures optimal robustness against all adversarial attacks? This paper answers this question by adopting a game-theoretic point of view. We show that adversarial attacks and defenses form an infinite zero-sum game where classical results (e.g. Sion theorem) do not apply. We demonstrate the non-existence of a Nash equilibrium in our game when the classifier and the Adversary are both deterministic, hence giving a negative answer to the above question in the deterministic regime. Nonetheless, the question remains open in the randomized regime. We tackle this problem by showing that, undermild conditions on the dataset distribution, any deterministic classifier can be outperformed by a randomized one. This gives arguments for using randomization, and leads us to a new algorithm for building randomized classifiers that are robust to strong adversarial attacks. Empirical results validate our theoretical analysis, and show that our defense method considerably outperforms Adversarial Training against state-of-the-art attacks.

Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learning---a key tool for performing meta-learning---learns a data representation that can transfer knowledge across multiple tasks, which is essential in regimes where data is scarce. Despite a recent surge of interest in the practice of meta-learning, the theoretical underpinnings of meta-learning algorithms are lacking, especially in the context of learning transferable representations. In this paper, we focus on the problem of multi-task linear regression---in which multiple linear regression models share a common, low-dimensional linear representation. Here, we provide provably fast, sample-efficient algorithms to address the dual challenges of (1) learning a common set of features from multiple, related tasks, and (2) transferring this knowledge to new, unseen tasks. Both are central to the general problem of meta-learning. Finally, we complement these results by providing information-theoretic lower bounds on the sample complexity of learning these linear features, showing that our algorithms are optimal up to logarithmic factors.