If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
His Excellency Mattar Mohammed Al Tayer, Director-General, Chairman of the Board of Executive Directors of Roads and Transport Authority (RTA), revealed that RTA's precautionary measures and initiatives applied to the scheduling and the operation of public buses, marine transit means and taxis had accelerated the recovery from the Covid-19 pandemic. He stated that such measures contributed to restoring the growth of public transport ridership to 70% of the pre-Covid-19 levels. They also contributed to reducing the number of kilometres travelled by 18%, improving bus on-time arrival by 6%, and cutting carbon emissions by 34 metric tons. "In cooperation with Alibaba Cloud, RTA has recently started trialling the'City Brain' system to manage traffic in urban areas using artificial intelligence and advanced algorithms. The system analysis a massive number of big data received from nol cards, operating buses and taxis as well as the Enterprise Command and Control Centre. Then it converts the data into useful information that could be used in sending instant notifications and improving bus schedules and routes. The system is expected to improve the bus ridership by 17%, average waiting time by 10%, and the journey time and the average bus usage by 5%," stated Al Tayer.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Yellowstone National Park announced Wednesday that it is testing its first autonomous electric shuttle. T.E.D.D.Y., or The Electric Driverless Demonstration in Yellowstone, is a small vehicle with a big job. Annual visitation to the park has increased by almost 40% since 2008 – by 1 million people in the last decade, causing issues like parking lot overflow, traffic jams, unsanitary conditions, roadside soil erosion and vegetation trampling.
Public transport forms the backbone of any urban mobility system, enabling cities to be more dynamic and competitive while creating more jobs. However, most cities' public bus systems are hemorrhaging riders in recent years due to slow speeds compared to options such as ridesharing services. Not to mention the continuously rising threats of transit agency budget cuts, traffic congestion and public safety. Hayden AI – a company creating smart city solutions purposely built for modern traffic conditions and increased urbanization – asking the question, What can we do to reverse this? The answer isn't bulking up on traffic enforcement, but rather, to enable smarter, more scalable enforcement.
The battery-powered sedan broadcasts a request to merge into the next lane, and other nearby vehicles automatically adjust as it glides over and exits the highway. Inside, the passenger finishes a quick email check, then clicks on a monitor to catch up with the day's news. For fans of automated vehicles (AVs), it's the holy grail of transportation: Cars would pilot themselves in an orderly fashion, making driving safer, cheaper, and faster. Easy transportation would be available to everyone without the environmental impacts or traffic congestion seen today. But those benefits could be offset or canceled out altogether, skeptics say, if the technology inadvertently encourages more driving among people who can't currently drive, and among commuters who might opt to travel all the way into cities rather than "park and ride."
As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional explanations - how groups of similar instances behave in a complex model, and the related issue of visualizing statistical feature interactions. The lack of utilities available for these analytical needs hinders the development of models that are mission-critical, transparent, and align with social goals. We present VINE (Visual INteraction Effects), a novel algorithm to extract and visualize statistical interaction effects in black box models. We also present a novel evaluation metric for visualizations in the interpretable ML space.
In Ann Arbor, Michigan, last week, 125 mostly white, mostly male, business-card-bearing attendees crowded into a brightly lit ballroom to consider "mobility." That's the buzzword for a hazy vision of how tech in all forms--including smartphones, credit cards, and autonomous vehicles-- will combine with the remains of traditional public transit to get urbanites where they need to go. There was a fizz in the air at the Meeting of the Minds session, advertised as a summit to prepare cities for the "autonomous revolution." In the US, most automotive research happens within an hour of that ballroom, and attendees knew that development of "level 4" autonomous vehicles--designed to operate in limited locations, but without a human driver intervening--is accelerating. Susan Crawford (@scrawford) is an Ideas contributor for WIRED, a professor at Harvard Law School, and the author of Captive Audience: The Telecom Industry and Monopoly Power in the New Gilded Age.
Today, we are launching support for Random Cut Forest (RCF) as the latest built-in algorithm for Amazon SageMaker. RCF is an unsupervised learning algorithm for detecting anomalous data points or outliers within a dataset. This blog post introduces the anomaly detection problem, describes the Amazon SageMaker RCF algorithm, and demonstrates the use of the Amazon SageMaker RCF on an example real-world dataset. Suppose you have collected data on traffic volume over a period of time across multiple city blocks. Can you predict if a spike in traffic volume represents a collision or just the usual rush hour?
Numerous urban planners advocate for differentiated transit pricing to improve both ridership and service equity. Several metropolitan cities are considering switching to a more "fair fare system," where passengers pay according to the distance travelled, rather than a flat fare or zone boundary scheme that discriminates against various marginalized groups. In this paper, we present a two-part optimal pricing formula for switching to distance-based transit fares: the first formula maximizes forecasted revenue given a target ridership, and the second formula maximizes forecasted ridership given a target revenue. Both formulas hold for all price elasticities. Our theory has been successfully tested on the SkyTrain mass transit network in Metro Vancouver, British Columbia, with over 400,000 daily passengers. This research has served Metro Vancouver's transportation authority as they consider changing their fare structure for the first time in over 30 years.
Uber's discrimination investigation recommends dozens of reforms within their company walls. A sign marks a pick-up point for the Uber car service at LaGuardia Airport in New York on March 15, 2017. Travis Kalanick, the combative and embattled CEO of ride-hailing giant Uber, resigned June 20, 2017 under pressure from investors at a pivotal time for the company. SAN FRANCISCO -- In the tumultuous months leading up to Uber CEO and co-founder Travis Kalanick's resignation, the ride-hailing company lost U.S. market share and saw its brand image tarnished, most notably by a former engineer's blog post blasting the ride-hailing company for its sexist work environment. Among several surveys tracking the company's decline: one based on credit card spending, which found over the past two years, Uber's share of rides has dropped to 75% from 90%, according to TXN Solutions.
A*STAR researchers have created a program that predicts public transport usage based on land-use and the location of amenities, an essential capability for smart city planning. From schools and shops to hospitals and hotels, a modern city is made of many different parts. Urban planners must take account of where these services are located when designing efficient transit networks. A*STAR researchers have developed a machine-learning program to accurately recreate and predict public transport use, or'ridership', based on the distribution of land-use and amenities in Singapore1. Traditional cities comprise an inner central business district (CBD), where most people work, surrounded by outer residential and industrial zones.