When we think of the city of the future, we might think about flying cars and scenes from "Star Trek" or "The Jetsons." But coming new technologies are shaping deeper and more fundamental changes in our cities. These changes are already well underway. CityLab readers already know how ride-hailing companies are transforming the nature of mobility and car ownership. Cities have overtaken suburbs to become a major center for high tech firms and the talent that drives them.
Uber will recruit engineers, AI specialists and computer vision researchers when the center opens this fall. "Initial projects will include: machine learning-based transport demand modeling, high-density low-altitude air traffic management simulations, integration of innovative airspace transport solutions with European aviation regulators such as EASA, and the development of smart grids to support future fleets of electric transport on the ground and in the air," Uber said in a press release. France, under President Emmanuel Macron, is rapidly becoming a hub for machine learning research. Google recently said it that it's building a deep learning team in the nation, and Facebook also plans to double its AI team there. France produces a lot of AI graduates at schools around the country, and plans to create a dedicate artificial intelligence program with the aim of doubling the number of students.
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this article, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today. At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the outcomes they experience such as taking a smaller step if the previous broad step made them fall, machines and software agents use reinforcement learning algorithms to determine the ideal behavior based upon feedback from the environment. Depending on the complexity of the problem, reinforcement learning algorithms can keep adapting to the environment over time if necessary in order to maximize the reward in the long-term.
Millions of tickets arrive at Uber's customer service department every week from its riders, drivers, eaters, etc. It is important for Uber to handle these tickets in a quick and efficient manner to retain its customers and fuel the companies growth. For this purpose, Uber has designed COTA or'Customer Obsession Ticket Assistant'. COTA is a Machine Learning and NLP powered tool that enables quick and efficient issue resolution of more than 90 per cent of Uber's inbound support tickets. For detailed information about different processes in the pipeline, please refer to this article by Uber. Uber is known to organize its processes using Machine Learning to achieve high speed and accuracy.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner In deep learning, the'deep' talks more about the architecture and not about the level of understanding that the algorithms are capable of producing. Take the case of a video game. A deep learning algorithm can be trained to play Mortal Kombat really well and will even be able to defeat humans once the algorithm becomes very proficient. Change the game to Tekken and the neural network will need to be trained all over again. This is because it does not understand the context.