Agilent Technologies Inc. announced it has acquired advanced artificial intelligence (AI) technology developed by Virtual Control, an AI and machine learning software developer that creates innovative analysis solutions in lab testing. Agilent will integrate the software, known as ACIES, into its industry-leading gas chromatography and mass spectrometry (GS/MS) platforms to improve the productivity, efficiency and accuracy of high-throughput labs the company serves around the world. "We're extremely pleased to be adding these additional capabilities to our product lineup." With the acquisition, Agilent obtained the software and other assets associated with ACIES. As part of the transaction, core members of the ACIES team also became Agilent employees.

In 98 percent of large building projects, logistical challenges result in cost overruns of more than 30 percent. Moreover, 77 percent of new construction projects are completed at least 40 percent late. Construction errors and risk mitigation costs contribute 10 percent to 30 percent to this figure. What's an earnest contractor to do? Tel Aviv-based startup SiteAware, which just raised a $15 million Series B financing round, offers an AI-powered Digital Construction Verification (DCV) platform that creates a "digital twin" of a building under construction. Every stage of new construction -- core, shell or interior -- is documented using drones, on-site cameras or people on the ground.

When the United States' Department of Homeland Security announced in early February it was training quadruped "robot dogs" to help secure the US-Mexico border, the department's spokesperson described the nearly 2,000-mile region as "an inhospitable place for man and beast, and that is exactly why a machine may excel there". But, of course, people do live, work, and try to eke out a living in this "inhospitable" desert space – leaving one to question what, exactly, the robot dog is meant to excel at? The border has long been a testing ground for a range of emerging surveillance and policing technologies, which activists have argued make the space even more dangerous to migrants, all in the name of protection, law, and order. Nicknamed the "smart wall", the tools used at the border include semi-autonomous surveillance drones and surveillance towers equipped with cameras and night vision, and radar. Former US congressman William Hurd, who represented the only Republican-held congressional district on the US-Mexico border for six years, endorsed a plan to bury fiber optic sensors capable of detecting underground movement.

Self-driving vehicles, small robots on production lines, drones flying rescue missions, even robots that keep older people company: these are some of the proposed, not-so futuristic, ways robots will aid us. But all those scenarios won't be possible without trust. Humans need to feel secure enough around robots and robotic systems to rely on them. "Building human-robot trust into autonomous robotic systems like self-driving vehicles is key to the systems' success," said Ryan Williams, a Virginia Tech assistant professor of electrical and computer engineering. "As we readily observe in human teams, collaboration without trust is often ineffective or even counterproductive," he said.

Autonomous modeling of artificial swarms is necessary because manual creation is a time intensive and complicated procedure which makes it impractical. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstacles and resistive forces (like linear drag, angular drag, and gravity) are modeled to track multiple dynamic targets. Moreover, reward functions for robust swarm formation and target tracking are devised for learning complex swarm behaviors. Since the number of agents is not fixed and has only the partial observance of the environment, swarm formation and navigation become challenging. In this regard, the proposed strategy consists of three main phases to tackle the aforementioned challenges: 1) A methodology for dynamic swarm management, 2) Avoiding obstacles, Finding the shortest path towards the targets, 3) Tracking the targets and Island modeling. The dynamic swarm management phase translates basic sensory input to high level commands to enhance swarm navigation and decentralized setup while maintaining the swarms size fluctuations. While, in the island modeling, the swarm can split into individual subswarms according to the number of targets, conversely, these subswarms may join to form a single huge swarm, giving the swarm ability to track multiple targets. Customized state of the art policy based deep reinforcement learning algorithms are employed to achieve significant results. The promising results show that our proposed strategy enhances swarm navigation and can track multiple static and dynamic targets in complex dynamic environments.

KYIV, Ukraine--Last October, as artillery shells fired by Moscow-backed separatists pounded a Ukrainian mechanized brigade, Kyiv responded with a powerful new weapon. A Turkish-made drone launched a missile and knocked out a Russian-supplied howitzer. This demonstration of force--the first drone strike in combat by Ukraine's military--unnerved Russia and fueled complaints from Moscow that North Atlantic Treaty Organization countries including Turkey, the U.S. and Britain were threatening Russia's security by supplying sophisticated new weapons to Kyiv.

Students, academics and electronics hobbyists are using homemade drones and motion-sensing cameras to patrol the Ukrainian border for signs of Russian military build-up and aggression. They say they have also struck at Russian targets with adapted Soviet missiles. The intelligence this group gathers is fed into a custom software package that it helped develop for the country's military. Aerorozvidka is an non-governmental organisation staffed by dozens of software and hardware engineers.

The United States and the European Union already restrict their exports of defense-related electronics to Russia, and have toughened those rules in recent years. Yet Russian networks have found ways around those obstacles. In 2015, several Russian agents were convicted of, or pleaded guilty to, federal charges of using a Texas-based company they set up to illegally export high-tech chips to Russian military and intelligence agencies.

Smart farming using AI and IoT is no longer a distant dream, smart farms are here to stay thanks to amazing advancements in AI and IoT devices. Over the past decades, the agriculture sector has undergone significant changes. Today, it's possible to grow plants even in the most hostile climatic regions. Crops are more resistant to insects, weeds, and climate change than ever before. Lastly, it's possible to breed high-yielding farm animals. But despite all these advancements, a large population of the world is still undernourished.

SwissInspect, a start-up from Swiss technology institute École Polytechnique Fédérale (EPFL) in Lausanne, Switzerland has developed a novel bridge-inspection system that combines structural engineering with drone technology, artificial intelligence and computer vision. SwissInspect is the result of research at Earthquake Engineering and Structural Dynamics Laboratory (EESD) in collaboration with Swiss Data Science Center (SDSC) on the image-based inspection and monitoring of structural elements. The company plans to test its system on around 50 bridges in Switzerland, according to a press release. Switzerland's bridges are currently inspected every two to five years using conventional visual inspection. But SwissInspect hopes to change all that with its new technology, which provides more objective evaluations and could be applied to other types of structures like tunnels, dams and buildings.