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.
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".
Three decades ago, the internet was just beginning to revolutionize human communications. Little did the world know how much power would fall into the hands of a few technocratic elites as a result. Autonomous vehicles likewise will transform human transportation in the same way; the skill of helming the wheel will no longer be necessary in about a decade or two, just as the art of writing on paper has all but ceased to exist. Recent news of a so-called Apple Car project has done little to bring positive attention to the possibilities of a self-driving revolution. In poll-after-poll, nearly half of Americans say they would not use an autonomous taxi or ride-sharing service.
Tesla privately admitted to a California regulator that CEO Elon Musk has been exaggerating plans to have fully-autonomous self-driving cars on the road by 2022. The acknowledgment was revealed in a summary of answers to questions put to the company by with the state's Department of Motor Vehicles. They were released by legal transparency group PlainSite, and first reported by The Verge. During an earnings call in January, Musk told investors he was'highly confident the car will be able to drive itself with reliability in excess of human this year,' reported The Verge. That call came five months after Musk told an AI conference in Shanghai that he was'confident' of producing a fully self-driving car by the end of 2020.
In the field of technology, artificial intelligence (AI) and self-driving cars are often discussed together. Though AI is being applied at a breakneck pace in a number of industries, the way it's being used in the automotive industry is currently a contentious subject. Every car maker and its parent company is striving to develop artificial intelligence and self-driving technology, and several tech companies and startups are pursuing the same goal. While many people believe that personal, autonomous vehicles are the way of the future, AI and machine learning are being used in a variety of ways in the design and operation of vehicles. General Motors, one of the largest global automaker, is taking a giant step forward towards automotive design by imagining a future of lighter, more powerful, and customizable vehicles.
Argo AI has made some waves in the autonomous vehicle space over the last few years, with investors Volkswagen and Ford working on cars that use its self-driving tech. The company suggests it's taken a significant step towards bringing autonomous vehicles to roads. Argo created a LiDAR sensor that's said to have a range of 400 meters, which it believes it the longest range of any current LiDAR sensor. The Argo Self-Driving System (SDS) can detect hard-to-see objects with more precision and at a greater distance, the company claims. It can seemingly spot far-away objects that have low reflectivity (less than one percent of light) even at night and scan the environment with photorealistic imaging.
Recently, Elon Musk claimed that Tesla is becoming known more as an Artificial Intelligence and Robotics company. The argument was put forth during Tesla's Q1 2021 Earnings call on April 26th. Tesla's name has been resonating as a leader in the electric vehicle and self-driving cars market. Nonetheless, the company was dragged into many controversies due to the car crashes involving Tesla and other growing complaints about the technical quality issues. The statement from Elon Musk came in the context of Tesla being able to develop a cutting-edge AI team and technology for its self-driving cars.
NI has bought autonomous vehicle simulator monoDrive, expanding the company's footprint into the autonomous driving space as the industry works to expand beyond testing into real-life situations. MonoDrive specializes in creating simulation software for advanced driver-assistance systems and autonomous vehicle development, helping train autonomous systems for their eventual introduction to roads with drivers and pedestrians. NI, formerly known as National Instruments, said the acquisition of monoDrive will help speed up the development, test, and deployment of safer autonomous systems for their transportation customers. According to NI, acquiring monoDrive will allow the company to "streamline the transitions between simulation, lab-based and physical test environments," noting that right now, "disparate tools cause siloed processes, time-to-market delays, and lead to higher costs that reduce the pace of innovation and hinder the quality of advanced technologies." "We welcome the monoDrive employees to NI and look forward to collectively accelerating our growth ambitions for our transportation business," said Chad Chesney, NI vice president and general manager of the transportation business unit.