Advanced cybersecurity capabilities are essential to safeguard software, systems, and data in a new era of cloud, the internet of things, and other smart technologies. In the real estate industry, for example, companies are concerned about the potential for hijacked elevators, as well as compromised building management and heating and cooling systems. According to Greg Belanger, vice president of security technologies at CBRE, the world's largest commercial real estate services and investment company, securing the enterprise has grown more complex--security teams must be familiar with controls and hardware on new devices, as well as what version of firmware is installed and what vulnerabilities are present. For example, if a heating, ventilation, and air-conditioning (HVAC) system is connected to the internet, he questions, "Is the firmware that's running the HVAC system vulnerable to attack? Could you find a way to traverse that network and come in and attack employees of that company?" Understanding enterprise vulnerabilities are crucial to safeguard physical assets but investing in the right tools can also be a challenge, says Belanger. "Artificial intelligence and machine learning need large sets of data to be effective in delivering the insights," he explains. In the era of cloud-first and industrial internet of things, the perimeter is becoming far more fluid. By applying AI and machine learning to data sets, he says, "You start to see patterns of risk and risky behavior start to emerge." Another priority when securing physical assets is to translate insights into metrics that C-suite leaders can understand, to help boost decision-making. CEOs and members of boards of directors, who are becoming more security savvy, can benefit from aggregated scores for attack surface management. "Everybody wants to know, especially after an attack like Colonial Pipeline, could that happen to us? How secure are we?" says Belanger.

WASHINGTON: How big a difference does it make when you reinforce foot troops with drones and ground robots? You get about a 10–fold increase in combat power, according to a recent Army wargame. "Their capabilities were awesome," said Army Capt. Philip Belanger, a Ranger Regiment and Stryker Brigade veteran who commanded a robot-reinforced platoon in nearly a dozen computer-simulated battles at the Fort Benning's Maneuver Battle Lab. "We reduced the risk to US forces to zero, basically, and still were able to accomplish the mission."

WASHINGTON: How big a difference does it make when you reinforce foot troops with drones and ground robots? You get about a 10–fold increase in combat power, according to a recent Army wargame. "Their capabilities were awesome," said Army Capt. Philip Belanger, a Ranger Regiment and Stryker Brigade veteran who commanded a robot-reinforced platoon in nearly a dozen computer-simulated battles at the Fort Benning's Maneuver Battle Lab. "We reduced the risk to US forces to zero, basically, and still were able to accomplish the mission."

Customer experience platform provider, Cerebri AI, recently announced the launch of its new second-generation solution, Cerebri Values. The Cerebri Values CX platform is now in full production and available to customers everywhere. The CV/CX version 2 offering measures customer engagement and uses the metrics around CX to drive financial results for businesses. Cerebri helps customers to manage cross-selling, up-selling and churn solutions. Designed as a new way for companies to apply AI to draw insights from modern customer journeys, CV/CX v2 is a leap forward in business intelligence and analytics.

One of the modern computer's first killer apps was predicting the weather. John von Neumann, who built the initial ENIAC computer, became fascinated with predicting weather in the 1930s. He called it "the most complex, interactive, and highly nonlinear problem that had ever been conceived of." In 1948, he assembled a team of meteorologists to create a mathematical model that would describe what weather would occur based on conditions in the atmosphere. These first weather predictions took more than 24 hours to compute, but proved the idea was possible--and that we needed faster computers.