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Using deep learning to improve traffic signal performance Penn State University

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Traffic signals serve to regulate the worst bottlenecks in highly populated areas but are not always very effective. Researchers at Penn State are hoping to use deep reinforcement learning to improve traffic signal efficiency in urban areas, thanks to a one-year, $22,443 Penn State Institute for CyberScience Seed Grant. Urban traffic congestion currently costs the U.S. economy $160 billion in lost productivity and causes 3.1 billion gallons of wasted fuel and 56 billion pounds of harmful CO2 emissions, according to the 2015 Urban Mobility Scorecard. Vikash Gayah, associate professor of civil engineering, and Zhenhui "Jessie" Li, associate professor of information sciences and technology, aim to tackle this issue by first identifying machine learning algorithms that will provide results consistent with traditional (theoretical) solutions for simple scenerios, and then building upon those algorithms by introducing complexities that cannot be readily addressed through traditional means. "Typically, we would go out and do traffic counts for an hour at certain peak times of day and that would determine signal timings for the next year, but not every day looks like that hour, and so we get inefficiency," Gayah said.


AI fails to recognize these nature images 98% of the time

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You can't fool all the people all the time, but a new dataset of untouched nature photos seems to confuse state-of-the-art computer vision models all but two-percent of the time. AI just isn't very good at understanding what it sees, unlike humans who can use contextual clues. The new dataset is a small subset of ImageNet, an industry-standard database containing more than 14 million hand-labeled images in over 20,000 categories. The purpose of ImageNet is to teach AI what an object is. If you want to train a model to understand cats, for example, you'd feed it hundreds or thousands of images from the "cats" category.


The Impact Of Artificial Intelligence In Web Design

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When Alan Turing invented the first intelligent machine, few could have predicted that the advanced technology would become as widespread and ubiquitous as it is today. Since then, companies have adopted AI for pretty much everything, from self-driving cars to medical technology to banking. We live in the age of big data, an age in which we use machines to collect and analyze massive amounts of data in a way that humans couldn't do on their own. In many respects, the cognition of machines is already surpassing that of humans. With the explosion of the internet, AI has also become a critical element of web design.


Is Artificial Intelligence the Avengers of the Web Analytics Universe

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When we talk about Artificial intelligence, we immediately recollect the science fiction blockbuster movies of the '80s and '90s like the Terminator, the Fifth Element and AI. These movies set a pathway for future scientists who have then researched the possibilities of simulating human behavior in robots. But back then, there was no concept of Big data processing which is vital for machine learning since there has to be huge processing of data as responses to different human emotions. When the technology matured with respect to Big data processing, it opened up infinite possibilities of applications of Artificial intelligence. Autonomous cars, AI chatbots, and AI enabled security checks, are some of the recent advents in the field of AI and machine learning.


Japan 'underdeveloped' in use of AI technology, says SoftBank's Masayoshi Son

The Japan Times

SoftBank Group Corp. Chairman and CEO Masayoshi Son said Thursday that Japan has become an "underdeveloped" country in the use of artificial intelligence in businesses, lagging behind China, India and Southeast Asian countries that have fast-growing technology companies. "Japan once was a leader in technology but has become an underdeveloped country in AI. It is in a pretty bad situation so Japan needs to awaken," Son told an audience at a company event in Tokyo. Among the audience were Ritesh Agarwal, CEO of Indian hotel operator Oyo Hotels & Homes, and Anthony Tan, CEO of ride-hailing company Grab Holdings Inc. of Singapore. Oyo and Grab are among over 80 AI startups in which SoftBank Group's $100 billion Vision Fund has invested.


The autonomous delivery bot that's designed to be 'nimble and fast enough' to ride in the bike lane

Daily Mail - Science & tech

Autonomous robots could soon be ferrying deliveries alongside human messengers in your city's bike lane. Refraction AI has unveiled a 5-foot-tall delivery robot dubbed REV-1 that can zip around at speeds of up to 15 miles per hour on its three wheels. It can carry the equivalent of about four or five grocery bags in its cabin, according to the firm. The company says its lightweight, nimble design will allow it to operate in both the bike lane and the roadway, making for more efficient last-mile delivery options. 'We have created the Goldilocks of autonomous vehicles in terms of size and shape,' Matthew Johnson-Roberson, cofounder and CEO at Refraction, said in a statement when the bot launched this month at TechCrunch Mobility.


Tesla is cutting the price of its top-selling Model 3

USATODAY - Tech Top Stories

Tesla Powerwalls and Solar Roof, two of Elon Musk's innovative strategies to get consumers onto the solar grid, require waits of six months or longer. The company says customers are hungry, but it doesn't have the product yet. Tesla is cutting the price of the Model 3, as it aims to make its best-selling product more affordable, and is discontinuing versions of other vehicles. Tesla said on Monday that it's reducing the price of the Model 3 by $1,000 to $38,990. The company will no longer sell the standard range versions of the Model S and Model X, raising the minimum costs consumers will have to pay for those cars.


Uber drivers and other gig workers in California could get better pay under proposed law

USATODAY - Tech Top Stories

Some Uber drivers in New York City want to see a decrease in the commission taken by the company. SAN FRANCISCO -- Gig economy workers are increasingly ubiquitous, shuttling us to appointments and delivering our food while working for Uber, Lyft, DoorDash and others. Thanks in large part to the app-based tech boom emanating from this city, 36% of U.S. workers participate in the gig economy, according to Gallup. But not all gigs are created equal, Gallup adds, noting that so-called "contingent gig workers" experience their workplace "like regular employees do, just without the benefits of a traditional job -- benefits, pay and security." California lawmakers are weighing what is considered a pro-worker bill that, if passed into law, would set a national precedent that fundamentally redefines the relationship between worker and boss by forcing corporations to pay up.


How can attackers abuse artificial intelligence? - Help Net Security

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Artificial intelligence (AI) is rapidly finding applications in nearly every walk of life. Self-driving cars, social media networks, cybersecurity companies, and everything in between uses it. But a new report published by the SHERPA consortium – an EU project studying the impact of AI on ethics and human rights – finds that while human attackers have access to machine learning techniques, they currently focus most of their efforts on manipulating existing AI systems for malicious purposes instead of creating new attacks that would use machine learning. The study's primary focus is on how malicious actors can abuse AI, machine learning, and smart information systems. The researchers identify a variety of potentially malicious uses for AI that are well within reach of today's attackers, including the creation of sophisticated disinformation and social engineering campaigns.


Machine Learning in Java - Programmer Books

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As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data.