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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) …
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Aka: Why this burning money pit has failed to produce meaningful results for decades. The future is here, and it looks nothing like we expected. As we approach the 10-year anniversary of Alexnet, we have to critically examine the successes and failures of machine learning. We are looking out from a higher plateau.
Artificial intelligence (AI) trials have shown that lineside vegetation may be monitored securely, inexpensively, rapidly, and at scale by identifying species of trees and other plants from images obtained by on-train cameras. Due to safety considerations, the size of Britain's 20,000-mile rail network, and the number of specialist surveyors required, monitoring flora and fauna on the side of a railway track to promote improved management of lineside ecosystems is exceedingly challenging. However, Network Rail has been collaborating with the UK Centre for Ecology and Hydrology (UKCEH) and technology firm Keen AI to create creative ways to remotely monitor biodiversity. Researchers have shown that AI can recognize invading species by their tracks, as well as native trees that may be threatened by diseases like ash dieback. As part of Network Rail's aim to achieve biodiversity net gain on its property by 2035, this information would enable railway staff to take necessary action to better manage lineside vegetation.
On an early April morning, around 4 am, a San Francisco Fire Department truck responding to a fire tried to pass a doubled-parked garbage truck by using the opposing lane. But a traveling autonomous vehicle, operated by the General Motors subsidiary Cruise without anyone inside, was blocking its path. While a human might have reversed to clear the lane, the Cruise car stayed put. The fire truck only passed the blockage when the garbage truck driver ran from their work to move their vehicle. "This incident slowed SFFD response to a fire that resulted in property damage and personal injuries," city officials wrote in a filing submitted to the California Public Utilities Commission.
On a beautiful day in May 2015, I drove the 13 hours from my home in Portland, Oregon, to Harris Ranch, California, halfway between San Francisco and Los Angeles. At the time, Tesla was touting a battery-swap station that could send Tesla drivers on their way in a fully powered vehicle in less than the time it takes to fill up a car with gas. Overtaken by curiosity, I had decided to spend a long Memorial Day weekend in California's Central Valley to see if Elon Musk's latest bit of dream weaving could stand up to reality. There, amid the pervasive stench of cow droppings from a nearby feedlot, I discovered that Tesla's battery swap station was not in fact being made available to owners who regularly drove between California's two largest cities. Instead, the company was running diesel generators to power additional Superchargers (the kind that take 30 to 60 minutes to recharge a battery) to handle the holiday rush, their exhaust mingling with the unmistakable smell of bullshit.
Artificial intelligence (AI) is transforming every walk of life. Ever wondered about artificial intelligence examples that the common man is enjoying? Artificial Intelligence is a technology that has evolved so much in itself in the past few years. We can say that it has'truly' become intelligent. Artificial intelligence is a technology that makes a device smart and allows it to perform actions that simulate human beings.
Of the many ways artificial intelligence and machine learning are poised to improve modern life, the promise of impacting mass transit is significant. The world is much different compared with the early days of the pandemic, and people around the world are again leveraging mobility and transit systems for work, leisure and more. Across the U.S., traditional mass transit systems including buses, subways and personal vehicles have returned to struggling through gridlock, rider levels and congestion. However, advanced AI and machine learning solutions built on cloud-based platforms are being deployed to reduce these frustrations. Transportation is one of the most important areas in which modern AI provides a significant advantage over conventional algorithms used in traditional transit system technology.
"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.
Amazon is reportedly installing AI-powered cameras in delivery vans to keep tabs on its drivers in the UK. The technology was first deployed, with numerous errors that reportedly denied drivers' bonuses after malfunctions, in the US. Last year, the internet giant produced a corporate video detailing how the cameras monitor drivers' driving behavior for safety reasons. The same system is now apparently being rolled out to vehicles in the UK. Multiple camera lenses are placed under the front mirror.
The massive, beautiful tree canopies in the Western U.S., which may grow perilously close to power lines, can quickly spark destructive wildfires. In fact, 70% of electrical outages are caused by vegetation, and this number has increased by 19% year over year from 2009-2020. The second-largest wildfire in California's history, The Dixie Fire, sparked when power lines came into contact with a fir tree. Could AI-driven solutions help prevent wildfires before they start by analyzing the tree growth that can spark them? Hitachi Energy, the Zurich, Switzerland-based global technology company, says yes. Hitachi Energy, formerly known as Hitachi ABB Power Grids (the name was changed last October) is currently focused on "powering good for a sustainable energy future."