The cybersecurity skills shortage is well documented, but the gap seems to be widening. The 2019 Cybersecurity Workforce study produced by nonprofit (ISC)² looked at the cybersecurity workforce in 11 markets. The report found that while 2.8 million people currently work in cybersecurity roles, an additional 4 million were needed -- a third more than the previous year -- due to a "global surge in hiring demand." As companies battle a growing array of external and internal threats, artificial intelligence (AI), machine learning (ML), and automation are playing increasingly large roles in plugging that workforce gap. But to what degree can machines support and enhance cybersecurity teams, and do they -- or will they -- negate the need for human personnel? These questions permeate most industries, but the cost of cybercrime to companies, governments, and individuals is rising precipitously. Studies indicate that the impact of cyberattacks could hit a heady $6 trillion by 2021. And the costs are not only financial. As companies harness and harvest data from billions of individuals, countless high-profile data breaches have made privacy a top concern. Reputations -- and in some cases people's lives -- are on the line. Against that backdrop, the market for software to protect against cyberattacks is also growing.

President TrumpDonald John TrumpTrump travels to Dover to receive remains of service members killed in Afghanistan Nadler demands answers from Barr on'new channel' for receiving Ukraine info from Giuliani Trump tweets scene from'Curb Your Enthusiasm' featuring'Make America Great Again' hat MORE's budget proposal unveiled Monday includes significant funding increases for research and development of artificial intelligence (AI) and quantum computing. If approved, the fiscal 2021 budget would double nondefense AI spending from roughly $973 million to almost $2 billion. It would also significantly boost quantum information science research by 50 percent compared with last year, with plans to double investment by 2022. "Last year, the President committed to investing in the Industries of the Future," Michael Kratsios, the White House's chief technology officer, said in a statement. "Now, he's delivering on this promise with a historic doubling of AI and quantum R&D investments that will fuel American leadership in these critical technologies for years to come. In the face of intense global competition, the FY 21 Budget affirms the importance of technology leadership to America's economic strength and national security," Kratsios added.

Lawmaker Yasuhiko Funago has a neurological disease that means he cannot speak and communicates by blinking to his carer or operating a computer system with his mouth. But he is demanding to be heard as he fights to improve the lives of people with disabilities in Japan, where many in the community complain of feeling "invisible." "I was a corporate soldier before I had amyotrophic lateral sclerosis and had hardly any opportunities to have contact with people with disabilities," Funago told a committee in November. "I had no idea how people with disabilities or illness were living," he said in the remarks read by his parliamentary aide. Such "ignorance" leads to "prejudice and discrimination," the 62-year-old warned.

MBDA's plan to acquire a stake in Numalis, a French company will help the former build Artificial Intelligence-based solutions for use in missile systems to detect, track, intercept and destroy aerial targets. "Numalis will give us access to essential building blocks of the future validation – and ultimately certification – processes for the AI-based applications used in our missile systems," said Éric Béranger, CEO of MBDA. The company announced its plan to buy a stake in the French firm on Monday. "Whilst AI holds a lot of promise, it remains a highly complex concept to validate and integrate into systems where error is not acceptable. Because it develops sovereign mission-critical products, MBDA has decided to invest in Numalis in order to benefit from its expertise and solutions," MBDA said in a statement.

A group of experts wants to study the brain waves and eye movements of people playing a video game in order to build an advanced AI that could coordinate the actions of military robots. The US Defense Advanced Research Projects Agency, known as DARPA, awarded a team from the University of Buffalo's Artificial Intelligence Institute a $316,000 grant for the study. Although swarm robotics is inspired by many things, including ant colonies, researchers believe that humans have a lot of potential to improve AI learning systems. The study of 25 video game players will include real-time strategy games such as StarCraft, Stellaris and Company of Heroes. "The idea is to eventually scale up to 250 aerial and ground robots, working in highly complex situations. For example, there may be a sudden loss of visibility due to smoke during an emergency. The robots need to be able to effectively communicate and adapt to challenges like that," the grant's principal investigator, Souma Chowdhury, assistant professor of mechanical and aerospace engineering, School of Engineering and Applied Sciences, told UBNow.

President Trump Monday proposed cutting federal research spending--except in key areas including artificial intelligence and quantum technologies. Trump's budget for the fiscal year beginning October 1 proposes spending $142.2 billion in research and development, 9 percent less than in the current year. The White House says its proposal is 6 percent more than it requested last year. The budget request is something of a gambler's approach to funding American innovation, betting big in select areas. "I find it disappointing and concerning that funding for basic research is down," says Martijn Rasser, a senior fellow at the Center for a New American Security, a policy think tank in Washington, DC. "We just don't know where the next breakthroughs will come from."

The future of artificial intelligence (AI) is here: self-driving cars, grocery-delivering drones and voice assistants like Alexa that control more and more of our lives, from the locks on our front doors to the temperatures of our homes. For example, should an autonomous vehicle swerve into a pedestrian or stay its course when facing a collision? These questions plague technology companies as they develop AI at a clip outpacing government regulation, and have led Seattle University to develop a new ethics course for the public. Launched last week, the free, online course for businesses is the first step in a Microsoft-funded initiative to merge ethics and technology education at the Jesuit university. Seattle U senior business-school instructor Nathan Colaner hopes the new course will become a well-known resource for businesses "as they realize that [AI] is changing things," he said.

Every week, Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Adversarial attacks are a common mechanism to evaluate the robustness of neural networks. Conceptually, adversarial attacks use neural networks to create data samples that disrupt the learning process of other neural networks.

Fears surrounding AI and cybersecurity reflect very real risks. AI-powered malware isn't a threat we need to worry about right now, but attackers have become adept at manipulating AI systems to their own advantage, essentially turning them against users. Widespread manipulation of the algorithms used on social media is already causing problems in many parts of the world. And as sophisticated AI tools become freely available, it would be naive not to expect adversaries to take advantage of the technology. But for now, we suspect that threat actors are using AI in rather indirect ways, such as for data analysis or by using tools to produce fake content.

Effective decision making requires understanding the uncertainty inherent in a prediction. To estimate uncertainty in regression, one could modify a deep neural network to predict coverage intervals, such as by predicting the mean and standard deviation. Unfortunately, in our empirical evaluations the predicted coverage from existing approaches is either overconfident or lacks sharpness (gives imprecise intervals). To address this challenge, we propose a novel method to estimate uncertainty based on two distinct neural networks with two distinct loss functions in a similar vein to Generative Adversarial Networks. Specifically, one network tries to learn the cumulative distribution function, and the second network tries to learn its inverse. Theoretical analysis demonstrates that the idealized solution is a fixed point and that under certain conditions the approach is asymptotically consistent to ground truth. We benchmark the approach on one synthetic and five real-world datasets, including forecasting A1c values in diabetic patients from electronic health records, where uncertainty is critical. In synthetic data, the proposed approach essentially matches the theoretically optimal solution in all aspects. In the real datasets, the proposed approach is empirically more faithful in its coverage estimates and typically gives sharper intervals than competing methods.