How IoT sensors and machine learning can make e-scooters safer

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Electric scooters are invading America, with scooters from popular brands including Lime, Bird, Spin, Lyft, and Uber becoming commonplace in major city centers. These dockless scooters, which are controlled by mobile app, accounted for 45.8% of all micromobility trips in 2018; this number is significant, considering 84 million micromobility trips were taken in 2018 alone, the National Association of City Transportation Officials reported. The success of scooters are built on the promises of reducing traffic congestion, providing a more efficient and cost-effective means of travel, and decreasing harmful emissions, according to the INRIX's Micromobility Potential in the US, UK and Germany report. However, scooters are also responsible for at least 1,500 injuries and eight fatalities of US riders since late 2017. The injuries and deaths have caused many US cities to take pause, with Atlanta recently banning scooter usage during nighttime hours, and San Diego considering a temporary ban.


Building machine learning products: a problem well-defined is a problem half-solved.

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Previously, I wrote about organizing machine learning projects where I presented my framework for building and deploying models. However, that framework operates on the implicit assumption that you already know generally what your model should do. In this post, we'll dig deeper into how to develop the requirements for a machine learning project when you're given a vague problem to solve. Some questions that we'll address include: Note: Sometimes machine learning projects can be very straightforward; your stakeholders define an API specification stating the inputs to the system and the desired outputs and you agree that the task seems feasible. These projects typically support existing products with existing "intelligence" solutions - your task is to simply encapsulate the "intelligence" task using machine learning in lieu of the existing solution (ie.


AI Reading List

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Near- and long-term strategic challenges and opportunities presented by AI. An introduction to the AI governance problem: the problem of devising global norms, policies, and institutions to best ensure the beneficial development and use of advanced AI. You can also keep up to date with the latest developments in the AI space by signing up for Import AI by Jack Clark and the Alignment Newsletter by Rohin Shah, and reading them closely every week. If you enjoyed these resources and are interested in working on the challenges and opportunities presented by artificial intelligence research, check out the 80,000 Hours job board to see who's hiring. If you have questions or feedback, feel free to get in touch.


Flu's spread is unpredictable. Can AI yield more reliable forecasts? - STAT

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Flu season reliably arrives around this time every year -- but where the virus heads and how it will spread can seem wildly unpredictable. Now, artificial intelligence is playing a bigger role in trying to change that. One startup is using data collected from thermometers to develop algorithms to derive insights about flu activity that it could sell to retailers, consumer-goods makers, and perhaps even health systems. Academic researchers are refining sophisticated AI models, including using machine learning and statistical methods to recognize patterns and map out future trajectories. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free!



AI and ML for Music Streaming

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The track experiences precisely the same type of neural network that assesses pictures to analyze the raw audio, called Convolutional Neural Networks. It means the sound and also produces characteristics like time signature, key, mode, pace, and loudness. After being processed with CNN, it provides metrics that make songs fall under the same category. This understanding lets the music to be compared by Spotify dependent on those critical metrics. For example, someone who likes heavy metal and rock may like songs that tend to be far more"loud" By combining these three models, Spotify assesses the similarity of distinct songs and artists and urges fresh songs to users' playlists.



University of Artificial Intelligence launched in Abu Dhabi

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Abu Dhabi: Taking another step in the world of artificial intelligence (AI), Abu Dhabi on Wednesday announced the opening of the world's first …


How AI-enabled marketing can lower website bounce rates and improve accessibility

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Marketers always strive hard to boost website traffic. A significant metric which quantifies the performance of websites is the bounce rate, indicating the percentage of users who leave without delving any further than the initial page. In this context, it is important to understand web accessibility. The Americans with Disabilities Act (ADA) became law in 1990. It prohibits discrimination against people with disabilities and provides equal opportunities for accessing the web to all users.


Why Most Companies Are Failing at Artificial Intelligence: Eye on A.I. – Fortune

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Most companies that say they're using artificial intelligence have yet to gain any value from their A.I. investments. A survey from MIT Sloan Management Review and Boston Consulting Group released Tuesday found that companies that view A.I. as merely a "technology thing," akin to a product rather than a business overhaul, fail to gain financial results. The survey's authors defined the "value" of an A.I. project as lifting sales, reducing costs, or creating a new product. The survey, based on responses from nearly 2,500 executives, found that seven out of ten companies report little to no impact from their A.I. projects so far. Overall, 40% of the surveyed companies that have made "significant investments" in A.I. have yet to report any business gains.