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) …
In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. And cloud computing data centers used by AI and machine learning applications worldwide are already devouring more electrical power per year than some small countries. A research team led by the University of Washington has developed new optical computing hardware for AI and machine learning that is faster and much more energy efficient than conventional electronics. Optical computing noise essentially comes from stray light particles, or photons, that originate from the operation of lasers within the device and background thermal radiation. Of course the optical computer didn't have a human hand for writing, so its form of "handwriting" was to generate digital images that had a style similar to the samples it had studied, but were not identical to them.
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A giant 18-wheel transport truck is barreling down a multi-lane Texas highway, and there is no one behind the wheel. The futuristic idea may seem surreal, but it is being tested in this vast southern US state, which has become the epicenter of a rapidly developing self-driving vehicle industry. Before driverless trucks are allowed onto roads and highways, however, multiple tests must still be conducted to ensure they are safe. Self-driving lorries are operated using radars, laser scanners, cameras and GPS antennas that communicate with piloting software. "Each time we drive a mile or a kilometer in real life, we re-simulate a thousand more times on the computer by changing hundreds of parameters," explains Pierre-François Le Faou, trucking partner development manager at Waymo, the self-driving unit at Google's parent company Alphabet.
In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies. In their new book, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, an interdisciplinary team of MIT researchers (leveraging insights gleaned from MIT's multi-year Task Force on the Work of the Future) exam the disconnect between improvements in technology and the benefits derived by workers from those advancements. It's not that America is rife with "low-skill workers" as New York's new mayor seems to believe, but rather that the nation is saturated with low-wage, low-quality positions -- positions which are excluded from the ever-increasing perks and paychecks enjoyed by knowledge workers. The excerpt below examines the impact vehicular automation will have on rank and file employees, rather than the Musks of the world.
Artificial intelligence and machine learning are currently affecting our lives in many small but impactful ways. For example, AI and machine learning applications recommend entertainment we might enjoy through streaming services such as Netflix and Spotify. In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. But the computers used for AI and machine learning demand a lot of energy. Currently, the need for computing power related to these technologies is doubling roughly every three to four months.
The AI software forecast from Gartner is based on use cases, measuring the amount of potential business value, timing of business value and risk to project how use cases will grow. According to the global research firm and consultancy, the top five use case categories for AI software spending in 2022 will be knowledge management; virtual assistants; autonomous vehicles; digital workplace; and crowdsourced data. The AI software market encompasses applications with AI embedded in them, such as computer vision software, as well as software that is used to build AI systems. "The AI software market is picking up speed, but its long-term trajectory will depend on enterprises advancing their AI maturity," said Alys Woodward, senior research director at Gartner. "Successful AI business outcomes will depend on the careful selection of use cases. Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders."
More Chinese automakers collaborating on EVs -- The automotive industry has entered into an intense era of collaboration among carmakers, technology giants, and even software start-ups, among others. This trend comes as countries, including China, accelerate into increased usage of EVs and AVs. Numerous partnerships have sprouted up in the past year, adding density and life to this ecosystem. Among Chinese automakers themselves, a handful of significant partnerships were made to accelerate the developments of EVs and AVs within the country. In fact, China is shaping up to be the first real test of Big Tech's ambitions in the world of car making.
The purpose of Computer Vision (CV) is to allow machines to obtain valuable information from their surroundings, by analyzing visual data that can be provided by different sources such as digital images and videos. The nature of such information depends on the final goal of the machine. Think, for example, of self-driving cars. A CV module that is capable of detecting in real-time objects that appear in front of the car is essential to avoid accidents. On the other hand, a robot that has to give directions to people inside a railway station can change the way of speaking based on whether the listener is a child or an adult.
A new generation of smartphones and other gadgets could be powered by chips designed to act like your brain. BrainChip recently announced its Akida neural networking processor. The processor uses chips inspired by the spiking nature of the human brain. It's part of a growing effort to commercialize chips based on human neural structures. The new generation of chips could mean "more deep neural network processing capability in the future on portable devices, e.g., smartphones, digital companions, smartwatches, health monitoring, autonomous vehicles and drones," Vishal Saxena, a professor of electrical and computer engineering at the University of Delaware told Lifewire in an email interview.
For years, companies and techno-bros have been saying that self-driving cars are ready to roll. Now companies like the ride-hailing service Lyft are actually letting customers take rides in autonomous vehicles. And at CES this year, John Deere unveiled a self-driving tractor that lets farmers put the latest automation tech to work in the fields. But if the time for self-driving vehicles is finally nigh, what does that mean for the workers who make a living behind the wheel? This content can also be viewed on the site it originates from.