Many enterprises are using artificial intelligence (AI) technologies as part of their overall security strategy, but results are mixed on the post-deployment usefulness of AI in cybersecurity settings. This trend is supported by a new white paper from Osterman Research titled "The State of AI in Cybersecurity: The Benefits, Limitations and Evolving Questions." According to the study, which included responses from 400 organizations with more than 1,000 employees, 73 percent of organizations have implemented security products that incorporate at least some level of AI. However, 46 percent agree that rules creation and implementation are burdensome, and 25 percent said they do not plan to implement additional AI-enabled security solutions in the future. These findings may indicate that AI is still in the early stages of practical use and its true potential is still to come. "Any ITDM should approach AI for security very cautiously," said Steve Tcherchian, chief information security officer (CISO) and director of product at XYPRO Technology.

I welcome you all to the Cyber Society of Today--a wondrous place where'what' is a possibility, 'how' is full of options, and'when' is a mystery. Despite what you may think, this is a real place. It is here, it is now, and most certainly you are in it. So, buckle up, be open-minded, and enjoy the ride--the doors are locked, and there is no place to hide. In this podcast, Sean and I are following up on an exciting story that we started during one of the panels we hosted at the RSA Conference in San Francisco a few weeks ago.

LONDON, UNITED KINGDOM - 2019/12/10: A London ambulance parked outside the East London hospital. In London, the National Health Service's 111 helpline receives up to 41,000 urgent calls every week. With this number constantly growing, health and clinical advisors must provide advice to patients with potentially serious medical conditions accurately and quickly. However, NHS 111 London has previously struggled to deliver a consistent patient experience and meet healthcare outcomes because it lacked a way to track and prioritize patients with developing or known medical conditions. Martin Taylor, deputy CEO and cofounder of cloud contact center platform Content Guru, says: "Patients were routed to any available call handler, and this varied each time they called NHS 111 (regardless of the time between calls), leading to patients having to repeat their details, symptoms, and developments. "Further to this, the NHS were unable to track repeat callers and therefore could not monitor changes in symptoms effectively.

Deep Tech is a new subscriber-only podcast that brings alive the people and ideas in our print magazine. Episodes are released every two weeks. We're making the first four installments, built around our 10 Breakthrough Technologies issue, available for free. Every two weeks, give or take, SpaceX puts another 60 Starlink communications satellites into low Earth orbit. Its initial goal is to launch 12,000 of these small mass-produced satellites--six times the number of operating satellites currently in orbit--with another 42,000 possibly to follow. Other companies such as Amazon, Telesat, and Planet are planning their own satellite "mega-constellations." The result could be a welter of new space-based services, from Internet connectivity to continuous mapping. But there's also growing attention to the potential downsides, including an increased risk of collisions that could end up littering low Earth orbit with dangerous debris and rendering it unusable. In this episode of Deep Tech, we hear from OneWeb founder Greg Wyler and science writer and former astrophysicist Ramin Skibba about efforts to mitigate the hazards.

In a major about-face in public health policy, the Centers for Disease Control (CDC), U.S. Surgeon General Dr. Jerome Adams, and state and local health officials around the country recently began urging people to wear homemade face masks when they're out in public. The directive is not meant to replace social distancing, but to reduce the spread of infection and ensure the most effective personal protective equipment goes to health care workers on the front line. But it could also throw a wrench in a number of facial recognition applications, including those used to unlock smartphones. Less than a year old, Google's facial recognition system on Pixel 4 smartphones is built to recognize a person even if they've shaved their beard or are wearing sunglasses, but Face Unlock for Pixel 4 is rendered virtually useless by homemade face masks. A Google spokesperson told VentureBeat that Face Unlock isn't made to recognize people wearing face masks and declined to say whether the company is working to add that capability to its system.

Tomorrow's technologies are in use by today's federal civilian and defense agencies. Artificial intelligence--once the stuff of science fictionˆhas become a staple of the Trump administration's technology policy, and its use is increasing among agencies looking to improve efficiencies and decision making. The White House and presidential advisers are grappling with how to promote AI and other emerging technologies, like quantum computing, and how they may shape the industries of the future. At the same time, federal agencies are mapping out their plans for the technology. The Labor Department plans to explore how automation can transform federal buying.

The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We propose an improved dissection procedure and a mechanical fixture that increases the rate of mosquito dissection and helps to deskill this stage of the production process. We further demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task.

We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids within a tender and use summary statistics like the mean, median, maximum, and minimum of each screen as predictors in the machine learning algorithm. This approach tackles the issue that competitive bids in incomplete cartels distort the statistical signals produced by bid rigging. We demonstrate that our algorithm outperforms previously suggested methods in applications to incomplete cartels based on empirical data from Switzerland.

The grant will reshape current forecasting methods and will allow the team to develop an open-source, modular, and flexible tool that uses epidemic case incidence data to inform short-term case and epidemic risk projections. It builds on the "Mapping the Risk of International Infectious Disease Spread (MRIIDS)" prototype that the team developed with funding from the U.S. Agency for International Development. Project partners will apply natural language processing and machine learning algorithms to automate the extraction of epidemic case data and will further refine and improve forecasting algorithms through use of artificial intelligence. Integration of other innovative data streams will strengthen the accuracy and validity of these predictive models for impending outbreaks.

Cyberwarfare is rapidly heading into meatspace: People are designing T-shirts and dresses that purposefully mess with AI image recognition algorithms. But what happens when someone works out how to mess with the algorithms used for autonomous cars?