Successful companies aren't waiting to embrace and adopt new technologies such as AI and machine learning, IoT, chatbots, and blockchain. According to Gartner, one of the differentiating practices of a high-performing business is focusing investments on moving the technology core forward and testing emerging technologies. Here, find the latest trends around these disruptive new technologies, and learn how your organization can harness their powerful capabilities to grow your business, gain a competitive edge, and achieve real business value. Three Ways to Disrupt IoT for Real Results How can we rethink IoT initiatives so that they actually create value for companies? How Robots and AI Can Improve Shared Services Centers Five strategies can help organizations adopt robotic process automation and artificial intelligence with successful results.
Artificial intelligence is already making significant inroads in taking over mundane, time-consuming tasks many humans would rather not do. The responsibilities and consequences of handing over work to AI vary greatly, though; some autonomous systems recommend music or movies; others recommend sentences in court. Even more advanced AI systems will increasingly control vehicles on crowded city streets, raising questions about safety--and about liability, when the inevitable accidents occur. But philosophical arguments over AI's existential threats to humanity are often far removed from the reality of actually building and using the technology in question. Deep learning, machine vision, natural language processing--despite all that has been written and discussed about these and other aspects of artificial intelligence, AI is still at a relatively early stage in its development.
Alex Bell likes to bike around New York City, but he got fed up with how often bike lanes were blocked by delivery trucks and idling cars. So he decided to do something about it, the New York Times reports. Bell is a computer scientist and he developed a machine learning algorithm that can study traffic camera footage and calculate how often bike and bus lanes are blocked by other vehicles. He trained the algorithm with around 2,000 images of different types of vehicles and for bus lanes, he set the system to be able to tell the difference between buses that are allowed to idle at bus stops and other vehicles that aren't. Then, he applied his algorithm to 10 days of publicly available video from a traffic camera in Harlem.
The era of artificial intelligence (AI) is officially here. The AI market is expected to grow from $21.46 billion in 2018 to $190.61 billion by 2025, at a CAGR of 36.62% between 2018 and 2025, according to a recent report. AI's phenomenal growth across different industries is being fueled by unprecedented computing power, ever-increasing amounts of data--billions of gigabytes every day--and sophisticated deep-learning algorithms. According to the AI Index report, the number of active U.S. startups developing AI systems has increased 14 times whereas the annual VC investment into such startups has increased only 6 times since 2000. Moreover, the share of jobs requiring AI skills in the U.S. has grown 4.5 times since 2013.
Natural human flaws can have severe impacts on business with lasting damage – 82% of operational asset failures are attributed to human performance. Indeed, a recent study by ARC Advisory Group found that the global process industry loses up to $20 billion a year due to unscheduled downtime – or $12,500 hourly, on average. However, machine learning is helping eliminate these costly flaws and is helping transform the manufacturing industry. This technology, along with others like big data analytics, are able to predict if and when something will break – cancelling the possibility of costly downtime. See also: Anticipating downtime will be business' next competitive advantage Seth Page is a cognitive computing veteran and industrial IoT pioneer based in Washington DC, and is CEO and co-founder of DataRPM, a Progress company.
The rapid growth of e-commerce is driving deep changes in logistics, from tightening up trucking capacity to elevating the importance of final-mile delivery processes. To respond, logistics managers now need to think in terms of systems that they can leverage today to make processes more efficient, while also keeping an eye on longer-term developments that will reshape tomorrow's possibilities. Several of thee include solutions currently in use, such as predictive analytics, supply chain control towers, and the continued digitization of freight forwarding; however, many, including blockchain-based traceability, driverless trucks, and even the advent of hyperloops, are all working through development, but promise to present bright new options in the future. The new take, says Joe Vernon, senior manager of North America supply chain analytics for consulting firm Capgemini, is predictive analytics that make use of machine learning and other related technology, including artificial intelligence (AI). "The goal is take all this data and be instructive with it, which is where machine learning comes in.
The Argentinian summer Sun beat down on the Buenos Aires city circuit as the cars approached the penultimate turn. It was February 18, 2017, the Saturday of Formula E's South American weekend, and two cars jostled for first place. The second car, though, was being too aggressive. Nearing the corner's apex, the vehicle misjudged its position and speed. The vehicle slammed into the blue safety walls surrounding the track. As the wreckage crumpled to a stop, a detached wheel rolled freely across the hot asphalt.
Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.
Though its time horizon can't be predicted, artificial intelligence (AI) promises to foundationally influence modern society, for better or worse. A sub-genre of AI -- machine learning -- has garnered particular attention from the pundits for its potential impact on the world's most important industries.