A Tesla engineer has informed California regulators that the electric vehicle company might not have a fully self-driving vehicle ready for this year. The information comes from documents dated May 6 exchanged between the California Department of Motor Vehicles and several Tesla employees, including CJ Moore, the company's autopilot engineer. The documents were released by the legal transparency group PlainSite, which got them under the Freedom of Information Act (FOIA). In January, Tesla chief Elon Musk said he was "highly confident the car will be able to drive itself with reliability in excess of human this year." "Tesla is at Level 2 currently. The ratio of driver interaction would need to be in the magnitude of 1 or 2 million miles per driver interaction to move into higher levels of automation," California DMV noted in the memo.
Federal investigators said Monday they were able to glean some insights into what might have happened after a fire erupted from a Tesla crash that killed two people in the Houston area in April and destroyed the vehicle's data recorder. . The National Transportation Safety Board released preliminary findings from its probe into the crash, which raised speculation about whether the vehicle's partially self-driving system, Autopilot, was to blame. The speculation stemmed from local authorities saying they were nearly positive that no one was behind the wheel when the vehicle crashed. The NTSB, in its preliminary report, said video footage from the vehicle owner's home security system showed him getting behind the wheel of the Tesla Model S and then slowly exiting the driveway. The vehicle traveled about 550 feet "before departing the road on a curve, driving over the curb, and hitting a drainage culvert, a raised manhole and a tree," according to the NTSB.
Artificial intelligence is already impacting virtually every industry and every human being. This incredible technology has brought many good and questionable things into our lives, and it will create an even bigger impact in the next two decades. According to Ray Kurzweil, one of the most-known futurists, computers will have the same level of intelligence as humans by 2029. Kurzweil stated to Futurism, "2029 is the consistent date I have predicted for when an AI will pass a valid Turing test and therefore achieve human levels of intelligence. I have set the date 2045 for the'Singularity' which is when we will multiply our effective intelligence a billion fold by merging with the intelligence we have created."
It's a tall order, but one that Zapf says artificial intelligence (AI) technology can support by capturing the right data and guiding engineers through product design and development. No wonder a November 2020 McKinsey survey reveals that more than half of organizations have adopted AI in at least one function, and 22% of respondents report at least 5% of their companywide earnings are attributable to AI. And in manufacturing, 71% of respondents have seen a 5% or more increase in revenue with AI adoption. Once "rarely used in product development," AI has experienced an evolution over the past few years, Zapf says. Today, tech giants known for their innovations in AI, such as Google, IBM, and Amazon, "have set new standards for the use of AI in other processes," such as engineering.
When a self-driving car passes by, you tend to notice. The towering sensors whirling around on the top of the car more than stand out. But Chinese autonomous vehicle company Pony.ai is reimagining the roofline for its next generation of autonomous taxicabs. As part of a partnership with autonomous vehicle sensor maker Luminar announced Monday, the Pony.ai Typical LiDAR sensors like those from Velodyne, Intel's Mobileye, and Waymo's own Laser Bear Honeycomb are mostly cone-shaped to help pull in a full 360-degree view from the top and around the car.
Plus plans to merge with Hennessy Capital Investment Corp. V in a transaction that would bring the company, which is based in California and China, about $500 million in gross proceeds and a market capitalization of roughly $3.3 billion. The agreement is expected to close in the third quarter, the companies said Monday. The deal would provide "a significant cash infusion for us to expand our commercialization efforts," Plus Chief Executive and co-founder David Liu said, as the company steps up production and aims to fill thousands of contracted orders and vehicle reservations from Chinese and U.S. fleets. The transaction would include a $150 million private placement of shares with BlackRock Inc., D.E. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology.
We already know we can teach machines to see. Sensors enable autonomous cars to take in visual information and make decisions about what to do next when they're on the road. But did you know machines can smell, too? Artificial Intelligence Is Developing A Sense Of Smell: What Could A Digital Nose Mean In Practice? Aryballe, a startup that uses artificial intelligence and digital olfaction technology to mimic the human sense of smell, helps their business customers turn odor data into actionable information.
China is shaping up to be the first real test of Big Tech's ambitions in the world of carmaking, with giants from Huawei Technologies Co. to Baidu Inc. plowing almost $19 billion into electric and self-driving vehicle ventures widely seen as the future of transport. While Apple Inc. has long had plans for its own car and Alphabet Inc. has Waymo, its autonomous driving unit, the size -- and speed -- of the move by China's tech titans puts them at the vanguard of that broader push. The lure is an industry that's becoming increasingly high tech as it pivots away from the combustion engine, with sensors and operating systems making cars more like computers, and the prospect of autonomy re-envisioning how people use will them. As the world's biggest market for new-energy cars, China is a key battlefield. Established automakers like Volkswagen AG and General Motors Co. are already slogging it out with local upstarts such as market darling Nio Inc. and Xpeng Inc.
The history of artificial intelligence has been marked by repeated cycles of extreme optimism and promise followed by disillusionment and disappointment. Today's AI systems can perform complicated tasks in a wide range of areas, such as mathematics, games, and photorealistic image generation. But some of the early goals of AI like housekeeper robots and self-driving cars continue to recede as we approach them. Part of the continued cycle of missing these goals is due to incorrect assumptions about AI and natural intelligence, according to Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute and author of Artificial Intelligence: A Guide For Thinking Humans. In a new paper titled "Why AI is Harder Than We Think," Mitchell lays out four common fallacies about AI that cause misunderstandings not only among the public and the media, but also among experts.
If we are not actively engaged in industries related to technology, we may fail to fully appreciate how we might already be influenced by artificial intelligence in our day-to-day world. Everyone is talking about self-driving cars, seemingly inanimate objects conversing with you about your personal preferences, someone somewhere already seems to recommend your shopping list armed with the knowledge of what you like or dislike. From the viewpoint of the business world, all companies today are looking to adopt AI in some form or the other to improve business processes, achieve efficiency, so on and so forth. I recently read an article about Softbank's Masayoshi Son and his vision "for an AI-powered utopia where machines control how we live". While this may sound like an unreal possibility, one could relate to this thought better if one were to ponder over David Fano's (Chief Growth Officer, WeWork) words, "Basically, every object will have the potential to be a computer".