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Professors Build AI to Help Autonomous Vehicles Locate Themselves On Maps

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Yi Fang, a research assistant professor in the Department of Electrical and Computer Engineering and a faculty member at NYU Abu Dhabi, and Edward K. Wong, an associate professor in the NYU Tandon Department of Computer Science and Engineering, are developing a deep learning system that will allow self-driving cars to navigate, maneuver, and respond to changing road conditions by mating data from onboard sensors to information on HERE HD Live Map, a cloud-based service for automated driving. NYU Tandon is one of HERE's first university research and development partners in HERE HD Live Map. The researchers added that this precision is also important because automobiles connected to HERE's HD Live Map service will deliver data to the cloud on road conditions, traffic, weather, obstacles, speed limits, and other variables, allowing the service to upgrade nearly in real-time to reflect changing conditions. Another venture - headed by a cybersecurity research team in the Department of Computer Science and Engineering - is developing the first free, open-source method for automakers to secure software updates.


Exploring the Artificially Intelligent Future of Finance

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Exploring the Artificially Intelligent Future of Finance With technological enhancements increasing computing power and decreasing its cost, easing access to big data and innovating algorithms, there has been a huge surge in interest of artificial intelligence, machine learning and its subset, deep learning, in recent years. What have been the leading factors enabling recent advancements and uptake of deep learning? Yuanyuan: Customer experience could be significantly improved using AI by analyzing individual level attributes to make traditional service much more tailor-made. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it.


Applying IoT and Swarm Algorithms to Reduce Traffic Congestion

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For example, existing technologies can detect smartphone Bluetooth radios (for short range) and WiFi radios (for longer ranges) from vehicles as they pass through points where sensor detectors record the cars' presence. Having much greater transparency into traffic flow and congestion points could help city planners identify opportunities to smooth traffic patterns and more accurately plan infrastructure to support their cities' growing needs. Using Swarm Intelligence (SI) algorithms, such as Particle Swarm Optimization (PSO), city planners can also create simulations to understand potential congestion challenges based on how vehicles and pedestrians navigate public spaces. Simulations using real data collected through this mechanism can help city planners determine potential traffic challenges at a highly-granular level--by street, intersection, freeway ramp, school area, etc.--to significantly reduce error rates in planning and address current congestion problems much more quickly.


Exploring the Artificially Intelligent Future of Finance

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In the long run AI, will completely change our investment industry, but (certainly on the institutional investment side) we are only at the beginning of a long and slow transition of 50 years. Financial advisory is another under developing area, where in future, individuals could expect a machine to suggest best investment portfolios based on their own family balance and consumption behaviors. Alesis: One of the main challenges for start-ups when applying Machine Learning specifically to financial services is educating the customers on the importance of data and access to it. To continue on the above examples, advances in NLP assure that bots will be able to handle simple tasks in customer service and AI systems will on the other hand provide automated information from news, press releases and other textual documents to prices.