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The AI startup culture in China has expanded quickly in recent years due to vast amounts of data and an abundance of people with technical skills. A wide spectrum of technology, including computer vision, natural language processing, and self-driving automobiles, has been developed by Chinese firms in the AI domain. Many of these firms have also successfully obtained sizable funding from investors, domestically and abroad. Let's check out a few of the most cutting-edge AI startups based in China. Horizon Robotics is focused on developing energy-efficient solutions for the Internet of Things (IoT) and smart vehicles.
The Justice Department has been urged by representatives of a U.S. national-security panel to consider economic-espionage charges against leaders of TuSimple Holdings Inc., an American self-driving-truck company with ties to China, according to people familiar with the matter. The recommendation for criminal charges, made late last year, stemmed from concerns that two founders and the current chief executive of the San Diego-based company were improperly transferring technology to a Chinese startup, the people said. The concerns were based on material gathered as part of a national-security review of TuSimple launched earlier last year.
Officials in San Francisco have asked for a halt to the expansion of driverless car tests across the city after a series of incidents that have hampered the work of emergency services. San Francisco's position at the heart of Silicon Valley and its wealth of technology talent has made it a hotbed for the driverless car industry. Both Waymo, owned by Google's parent company Alphabet, and Cruise, owned by General Motors, operate experimental robotic taxi services in the city. But they haven't been without problems. New Scientist has previously reported how autonomous vehicles (AV) from Cruise, for example, have randomly stopped and blocked traffic and had a run-in with police.
As your self-driving car navigates this exhilarating course, decelerating for twists and turns, the stakes are high. That's because on this test track, no variable in any given driving scenario is left unturned, and every variable is repeatably and reliably measured. The mission is to train your car's autonomous driving algorithm so that it makes the optimal decision every time, with no catastrophic errors in the process. This sounds like an impossible feat in the real world, where virtually every variable surrounding a self-driving car is unpredictable. Moreover, accidents during test runs are bound to happen.
Machine learning (ML) is ubiquitous, contributing to society-facing applications that each day impact how we work, play, communicate, live, and solve problems. In 2015, after Marc Andreesen proclaimed "software is eating the world," others countered "machine learning is eating software." While there have been exciting ACM A.M. Turing-award-winning worthy advances in the science of ML, it is important to remember ML is not just an academic subject: it is a technology used to build software that is consequential in the real world. Failures of ML systems are commonplace in the news: IBM's Oncology Expert Advisor project is canceled for poor results after a $60 million investment; the chatbot Tay learns abusive language; an Uber self-driving car runs a red light; a Knightscope security robot knocks over a toddler;8 facial recognition systems unfairly make three times more errors for non-white non-males.2
A 2016 video that Tesla used to promote its self-driving technology was staged to show capabilities like stopping at a red light and accelerating at a green light that the system did not have, according to testimony by a senior engineer. The video, which remains archived on Tesla's website, was released in October 2016 and promoted on Twitter by Elon Musk as evidence that "Tesla drives itself". But the Model X was not driving itself with technology Tesla had deployed, Ashok Elluswamy, director of Autopilot software at Tesla, said in the transcript of a July deposition taken as evidence in a lawsuit against Tesla for a 2018 fatal crash involving a former Apple engineer. The previously unreported testimony by Elluswamy represents the first time a Tesla employee has confirmed and detailed how the video was produced. The video carries a tagline saying: "The person in the driver's seat is only there for legal reasons. He is not doing anything. The car is driving itself."
Researchers also found that to keep computer-generated emissions from spiraling out of control in the coming decades, each autonomous vehicle would need to consume less than 1.2 kilowatts of energy for computing, which would require hardware to double in efficiency roughly every 1.1 years, a "significantly faster pace" than what's being done currently.
As we all move farther into our digitally altered world, artificial intelligence (AI) continues to be a potent transformation catalyst for international sectors and enterprises. In 2023, it is anticipated that governments and corporations will spend more than $500 billion on AI globally. In many areas of our society and daily life, artificial intelligence (AI) has now become integrated. It's hard to dispute its effect on everything from chatbots and virtual helpers like Siri and Alexa to automated industrial equipment and self-driving cars. The technology most often used to achieve AI today is machine learning, which consists of sophisticated software algorithms designed to perform a single specific task, such as answering questions, translating languages, or navigating a journey, and getting better at it as they are exposed to more and more data.
AI chipmaker Nvidia has announced a partnership with Taiwanese electronics manufacturer Foxconn to build autonomous vehicle platforms at the Consumer Electronic Show (CES) 2023. Foxconn will manufacture electronic control units (ECUs) for cars based on Nvidia's DRIVE Orin chip and DRIVE Hyperion sensors for self-driving vehicles. The chip is made specifically for computing in connected autonomous vehicles. Nvidia estimates a $300 billion market opportunity in the automobile industry, where it generated $251 million in revenue in the third quarter. Nvidia strives to use its technology to aid Foxconn in overcoming shortcomings like upgrading production and higher costs.
Getting a jump on the parade of announcements to come at this week's CES tech trade show in Las Vegas, the graphics and AI chip company Nvidia gave an unofficial virtual keynote presentation Tuesday morning, unveiling updates to its gaming, robotics, and auto businesses. Nvidia (ticker: NVDA) had a rough 2022, with its stock losing about half of its value. The company was hurt by a combination of a slowing PC gaming market, a sharp plunge in purchases of graphics chips by cryptocurrency miners, and softening sales of chips for data centers, in particular in China, where tougher export restrictions hurt the business. But the company remains a Wall Street favorite, viewed as a leader in the market for chips used in both graphics and AI applications. Nvidia focused in particular on the use of artificial intelligence software across its product lines.