Chinese startup Mobike has announced that it lost more than 200,000 bikes in 2019. From a report: The company said in a blog that 205,600 “dockless” bikes were lost to theft and vandalism. In 2018, it pulled out of Manchester after a series of incidents. Shared dockless bikes, which are hired via an app, have become commonplace in cities worldwide over the last few years. Companies like Uber, Lime and Ofo have all put shared bikes on city streets, as have some local councils. In China, thousands of shared bikes have ended up in huge scrapheaps, leading to questions about whether there is demand for them.

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Source: Slashdot – Chinese Startup Mobike Lost More Than 200,000 Bikes in 2019

Positive affectivity, or the characteristic that describes how people experience affects (e.g., sensations, emotions, and sentiments) and interact with others as a result, has been linked to increased interest and curiosity as well as satisfaction in learning. Inspired by this, a team of Microsoft researchers propose imbuing reinforcement learning, an AI training technique that employs rewards to spur systems toward goals, with positive affect, which they assert might drive exploration useful in gathering experiences critical to learning. From a report: As the researchers explain, reinforcement learning is commonly implemented via policy-specific rewards designed for a predefined goal. Problematically, these extrinsic rewards are narrow in scope and can be difficult to define, as opposed to intrinsic rewards that are task-independent and quickly indicate success or failure. In pursuit of an intrinsic policy, the researchers developed a framework comprising mechanisms motivated by human affect — one that motivates agents by drives like delight. Using a computer vision system that models the reward and another system that uses data to solve multiple tasks, it measures human smiles as positive affect. The framework encourages agents to explore virtual or real-world environments without getting into perilous situations, and it has the advantage of being agnostic to any specific machine intelligence application. A positive intrinsic reward mechanism predicts human smile responses as the exploration evolves, while a sequential decision-making framework learns a generalizable policy. As for the positive intrinsic affect model, it changes the action selection such that it biases actions providing better intrinsic rewards, and a final component uses data collected during the agent’s exploration to build representations for visual recognition and understanding tasks.

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Source: Slashdot – Microsoft Proposes AI That Improves When You Smile

An anonymous reader shares a report: Reinforcement learning, an AI training technique that employs rewards to drive software policies toward goals, has been applied successfully to domains from industrial robotics to drug discovery. But while firms including OpenAI and Alphabet’s DeepMind have investigated its efficacy in video games like Dota 2, Quake III Arena, and StarCraft 2, few to date have studied its use under constraints like those encountered in the game industry. That’s presumably why Ubisoft La Forge, game developer Ubisoft’s eponymous prototyping space, proposed in a recent paper an algorithm that’s able to handle discrete, continuous video game actions in a “principled” and predictable way. They set it loose on a “commercial game” (likely The Crew or The Crew 2, though neither is explicitly mentioned) and report that it’s competitive with state-of-the-art benchmark tasks.

“Reinforcement Learning applications in video games have recently seen massive advances coming from the research community, with agents trained to play Atari games from pixels or to be competitive with the best players in the world in complicated imperfect information games,” wrote the coauthors of a paper describing the work. “These systems have comparatively seen little use within the video game industry, and we believe lack of accessibility to be a major reason behind this. Indeed, really impressive results … are produced by large research groups with computational resources well beyond what is typically available within video game studios.” The Ubisoft team, then, sought to devise a reinforcement learning approach that’d address common challenges in video game development. They note that data sample collection tends to be a lot slower generally, and that there exist time budget constraints over the runtime performance of agents.

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Source: Slashdot – Ubisoft Uses AI To Teach a Car To Drive Itself in a Racing Game

Since 2010, a slew of on-demand companies and technologies have managed to use consumer data to transform the commercial significance of urban living. From a report: Historically, one of the great economic benefits of urban life is having access to jobs, schooling, goods, and services without needing to travel very far. But digital platforms that aggregate consumer demand are making physical density less important. Uber and Airbnb, the killer apps of the 2010s, exemplify this change. Once upon a time, visitors needed to flock to quarters where a city’s supply of hotel accommodations and other tourist amenities were physically consolidated, usually downtown. If you needed a ride, you used to call the taxi company directly, or flag down one of the cabs that served that area.

Now we transmit our demands for trips and beds as data from wherever we are, rather than direct interactions that depend on physical nearness. Uber and Airbnb consolidate our requests with those of a sea of other users, set prices, offer us suppliers, and dispatch them to us. The apps are creating their own agglomerations of demand, networks that are held together via digital ligaments instead of actual proximity. Kevin Webb, a transportation data expert, points out that Amazon works the same way, building off the big-box store model that came before it: Instead of physically traveling to an area where you can buy tennis balls, shampoo, and a can of tomato paste at three different but close-together shops, its shopping algorithms mean that it can stash those items on a single warehouse shelf thousands of miles away.

What does this shift mean? On-demand platforms have made certain kinds of goods and services more convenient, affordable, and accessible for customers across the income, age, and race spectrums. New places and things opened up for new markets. But the less-desirable consequences of replacing physical marketplaces with digital bundles of demand have been major. As ride-hailing emerged, the taxi industry in most cities has been gutted; in many others, traffic congestion has spiked and transit ridership has declined. Thanks to online short-term rentals, traditional hotels have seen a declining share of travelers opting for their wares and neighborhood housing shortages have been exacerbated by hosts who rent to Airbnb guests rather than full-time tenants. In some cases, once-residential neighborhoods have been emptied of locals and turned into streets of rentable ghost hotels.

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Source: Slashdot – How the On-Demand Economy Reshaped Cities