Intelligent to a Fault: When AI Screws Up, You Might Still Be to Blame


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.

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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.

NVIDIA And Artificial Intelligence: How NVDA Is Leading The Way


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.

Evolution of E-commerce: The possibilities of tomorrow


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.

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning Artificial Intelligence

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.

Scaling the AI Ladder - THINK Blog


The first automobile was driven down the streets of Detroit in 1890. It would take another 30 years before Henry Ford streamlined production and made cars available to the mass market. The obvious lesson: sometimes technology has a long gestation period, before we can scale it for everyday use.

5 Innovative Uses for Machine Learning


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.

What Is The Difference Between Deep Learning, Machine Learning and AI?


Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there.

The Amazing Ways Tesla Is Using Artificial Intelligence And Big Data


Tesla has become a household name as a leader and pioneer in the electric vehicle market, but it also manufactures and sells advanced battery and solar panel technology.

Technology Go – How Artificial Intelligence Influences Automotive Logistics


Two weeks ago, the e-commerce retailer Amazon opened its first offline convenience store, Amazon Go – without a cashier. On January 22, the first visitors of the Seattle store were tracked in the shop using image recognition and machine learning algorithms. The technology finds out what the visitors have bought and charges it automatically to their account. After the customer has scanned their smartphone to enter the store, cameras throughout the store track them as a 3D object without facial recognition. The biggest challenge for image recognition is to differentiate between similar-looking products and customers hands, which often cover the products and their labels. For the retail industry, this is a revolution and is likely to be a major step in linking the online and offline worlds.