The Army will begin equipping combat units next year with remote-controlled robotic vehicles designed to carry ammunition, water and other heavy combat necessities for soldiers, if officials at Fort Benning, Georgia, get their way. The Army has been experimenting with the concept of robotic mules for more than a decade. But the performance of four competing prototypes of a Small Multipurpose Equipment Transport (SMET) during a recent operational test demonstration with units from the 10th Mountain and 101st Airborne divisions has made believers out of officials from Benning's Maneuver Capabilities Development and Integration Directorate (MCDID). "The operational test demonstration really showed that the capability is ready," Col. Tom Nelson, director for Robotics Requirements Division at MCDID, told reporters Tuesday. The SMET is capable of hauling 1,000 pounds of soldier gear for 60 miles within 72 hours, and will also generate three kilowatts of power to charge the growing number of tactical electronic devices soldiers carry, according to officials at MCDID, the organization that has the lead for developing and testing robotics and autonomous systems designed for Army brigade combat teams (BCTs).
Through the media, this conversation may appear to sit in a cloud of worry about speculative future-bots that will wipe out humanity. However, real inklings of how we can easily lose mastery over our AI creations are observed in practical problems related to unintended behaviors from poorly designed machine learning systems. Among these potential "AI accidents" is the case of adversarial techniques. This approach takes, for instance, a trained classifier model that performs well with identifying inputs compared to how a person would classify. Then, a new input comes along that includes subtle yet maliciously crafted data that causes the model to behave very poorly.
You can see the AI Soldiers in action at the end of this video. In this video we continue the development of our soldier AI. We'll implement a cover system for our AI Soldiers. After this video, The AI Soldiers will look for the best suitable cover, move towards it and then begin their combat state. The AI is use pathfinding, which we implemented in part 2 of this tutorial series: https://www.youtube.com/watch?v al6fF... Our base AI uses the FSM (Finite-State Machine) Technique implemented in the first video of this tutorial: https://www.youtube.com/watch?v Mbtkp... Soldier Model used in this video: https://assetstore.unity.com/packages... #artificialIntelligence #coversystem #madewithunity
We are surrounded by surveillance cameras that record us at every turn. But for the most part, while those cameras are watching us, no one is watching what those cameras observe or record because no one will pay for the armies of security guards that would be required for such a time-consuming and monotonous task. But imagine that all that video were being watched -- that millions of security guards were monitoring them all 24/7. Imagine this army is made up of guards who don't need to be paid, who never get bored, who never sleep, who never miss a detail, and who have total recall for everything they've seen. Such an army of watchers could scrutinize every person they see for signs of "suspicious" behavior.
The explosion of new data affects all of our domains from healthcare to national security. Across these domains, we're helping our customers adopt artificial intelligence (AI) and machine learning (ML) solutions that turn this data into new opportunities for insight and efficiency. Our deep understanding of our customers, their data, and their missions allows Leidos to deliver AI and ML solutions that provide real value immediately. AI is improving many ways in which our industries operate, but this also presents new concerns about reliability, resilience, and security. AI and ML systems present new attack surfaces, and a broad set of potential vulnerabilities in these systems has already been discovered.
Home » Security Boulevard (Original) » News » Vectra Raises $100M More for Cybersecurity AI Vectra has garnered another $100 million in funding to accelerate development of a threat detection and response system running in the cloud that makes extensive use of artificial intelligence (AI). This latest round of funding brings the total investment in Vectra to $200 million. Company CEO Hitesh Sheth said Vectra's Cognito platform applies machine learning algorithms to network metadata captured across the extended enterprise.
IBM (NYSE: IBM) today announced the first recipients and list of global women leaders and pioneers in AI for business. The list recognizes and celebrates women across a variety of industries and geographies for pioneering the use of AI to advance their companies in areas such as innovation, growth, and transformation. IBM will celebrate the honorees during an inaugural recognition event on June 12, 2019 at the IBM Watson Experience Center in New York, New York where the women will share their experiences leading AI initiatives in their organizations. Students from IBM's P-Tech program will attend to hear from these leaders who have applied AI technology in diverse and meaningful ways to help drive business innovation. "Artificial Intelligence is poised to drive dramatic advances in every industry," said Michelle Peluso, SVP, Digital Sales & CMO, IBM, who also serves as Leader of IBM's Women's Initiative.
Machine Learning, often abbreviated to ML, is a form of learning in which systems use complex computer algorithms to acquire knowledge or skill automatically without being programmed directly. It is considered as a type of AI (Artificial Intelligence) since machines are built with the idea to learn and make decisions from the available data and even improve themselves from experience without requiring human involvement. This is mainly used to maximize the machine's performance. The idea behind ML is based on mathematics, computer science, and statistics. ML comes in three types, Supervised, Reinforcement, and Unsupervised Learning.