Bike sharing systems (BSS) have been a popular traveling service for years and are used worldwide. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and greenhouse gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. Some studies propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Other studies propose to extend the approach by including weather data. This study aims to extend upon these proposals and opportunities to explore how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm. K-means managed to identify three clusters that correspond to bike usage depending on weather conditions. The results show that the weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables.

For 23 years, Larry Collins worked in a booth on the Carquinez Bridge in the San Francisco Bay Area, collecting tolls. The fare changed over time, from a few bucks to $6, but the basics of the job stayed the same: Collins would make change, answer questions, give directions and greet commuters. "Sometimes, you're the first person that people see in the morning," says Collins, "and that human interaction can spark a lot of conversation." But one day in mid-March, as confirmed cases of the coronavirus were skyrocketing, Collins' supervisor called and told him not to come into work the next day. The tollbooths were closing to protect the health of drivers and of toll collectors. Going forward, drivers would pay bridge tolls automatically via FasTrak tags mounted on their windshields or would receive bills sent to the address linked to their license plate. Collins' job was disappearing, as were the jobs of around 185 other toll collectors at bridges in Northern California, all to be replaced by technology.

"How do you do that without creating unintended consequences?" said Casey Ellis, founder of San Francisco-based cybersecurity startup Bugcrowd Inc. "That's a hard problem to solve, and I think she's been pretty thoughtful about it." A native of the San Francisco Bay Area, Ms. Harris served as California's attorney general as Silicon Valley companies such as Facebook Inc. and Alphabet Inc. grew into global titans and critics began questioning their influence. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. While vice presidents have limited power to shape or enforce policy from the White House, advocates say she could help prioritize the tech industry's policy wishlist, which includes issues such as immigration reform. The Trump administration has curtailed visas for highly skilled workers, drawing rebukes from Silicon Valley in recent months.

Gong.io, the US-Israeli company that uses AI to analyze customer data for sales teams, has announced it has raised its third round in 18 months, securing $200 million at a whopping $2.2 billion valuation in a Series D funding round. The funding round was led by Coatue Management, Index Ventures and Salesforce Ventures and joined by earlier investors like Sequoia Capital and Battery Ventures. Gong said it will use the new funding to "fulfill strong market demand for its Revenue Intelligence Platform, reinforce its market leadership, and invest in its product, engineering and go-to-market teams." The company said it has raised a total of $334 million to date. This includes a $65 million Series C funding round in December 2019 and a $40 million Series B investment in February 2019.

This post is a practical example of Neural Style Transfer based on the paper A Neural Algorithm of Artistic Style (Gatys et al.). For this example we will use the pretained Arbitrary Image Stylization module which is available in TensorFlow Hub. We will work with Python and tensorflow 2.x. Neural style transfer is an optimization technique used to take two images--a content image and a style reference image (such as an artwork by a famous painter)--and blend them together so the output image looks like the content image, but "painted" in the style of the style reference image. This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image.

AWS announced the general availability of AWS Wavelength on Verizon's 5G Network. Applications demanding ultra-low latency in single-digit milliseconds can leverage AWS compute and storage at the edge with Verizon's 5G Network. The service is now available with Boston and the San Francisco Bay, starting with the San Jose area. AWS has embedded AWS compute and storage services at the edge of 5G with Verizon's 5G Network. Developers can now leverage machine learning, Internet of Things (IoT), and video/game streaming requiring low latency edge processing with AWS Wavelength, notes the announcement.

If you ask pet owners to pick the most difficult part of their workday routine, many would say "mornings." They don't mean the common struggles with getting out of bed or commuting, but rather the emotions involved in leaving their beloved pets behind. The sad look in the pets' eyes suggests this is not an easy time for them either. Surveys conducted by ZenCrate and Zulily show that more than 32 million dogs in the US suffer anxiety when left alone at home, while 84 percent of pet parents frequently worry about their housebound fur babies. There are a number of solutions available, such as pet sitters or daycare facilities, but these do not allow people and pets to actually stay connected. An increasingly popular tech-based approach that enables distanced interactions is smart pet monitoring cameras.

Amazon Web Services brought its compute and storage capabilities to the mobile edge this week for developers in Boston and the San Francisco Bay area with its new AWS Wavelength on Verizon's 5G network. AWS' first Wavelength Zones use Verizon's 5G Edge mobile compute platform to allow developers and enterprises to deploy ultra-low latency-dependent applications to mobile and connected wireless devices at the edge of Verizon's 5G Ultra Wideband network. Use cases for 5G mobile edge compute using AWS Wavelength include machine learning (ML) inference at the edge, autonomous industrial equipment, connected cars, smart cities and factories, the Internet of Things, live and interactive video and game streaming, and virtual reality. Developers can build applications that provide near-real-time analytics for instant decision-making and automated robotic systems for manufacturing facilities. AWS Wavelength's debut comes eight months after AWS CEO Andy Jassy and Verizon CEO Hans Vestberg (pictured) unveiled the new service at the AWS re:Invent conference in Las Vegas in December.

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Aerospike is the global leader in next-generation, real-time NoSQL data solutions for any scale. Aerospike's patented Hybrid Memory Architecture delivers an unbreakable competitive advantage by unlocking the full potential of modern hardware, delivering previously unimaginable value from vast amounts of data at the edge, to the core and in the cloud. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important?

Artificial Intelligence (AI) is not the one that is borne by the overwhelming science fiction vision. In the near future, we will see almost every area of life in order to make our activities more effective and interactive. According to China's search engine, Baidu's top researcher, "Reliability of speech technology approaches the point we will only use and do not even think about." Andrew Ng says the best technology is often invisible, and speech recognition will disappear in the background as well. Baidu is currently working on more accurate speech recognition and more efficient sentence analysis, which expects sound technologies to be able to interact with multiple devices such as household appliances.