"Is it safe to trust self-driving cars? Despite skepticism, 10% of jobs could be transformed in just a few years." wrote Qiu Zhili.
Sebastian Thrun, co-founder of Google X Lab and former vice president of Google, recently emphasized during his speech in China that regardless of how people feel about AI, within one or two years, everyone will have to accept that machines' learning abilities surpass those of humans.
Thrun envisions that by 2050, autonomous driving will surpass human driving skills. He believes that AI technology will play a crucial role in fields like cancer diagnosis and telemedicine. For traditional vehicles, this will inevitably lead to changes in their essence, production methods, and business models.
Whether it's tech startups or traditional automakers, the first step is to establish a data acquisition loop. By using machine learning on these data, machines can be enhanced, thereby improving the autopilot experience. In this cycle, whoever has the largest volume of data, and updates it frequently, is most likely to come out on top.
By 2050, machine-driven vehicles will dominate.
Thrun joined Google in 2007 and co-founded Google X and the driverless car projects. As a professor of computer science and engineering at Stanford University, he has already made significant strides in robotics and automotive technology. Now, at Udacity, he primarily teaches the world about the technical aspects of self-driving vehicles.
Why is AI learning faster than humans?
According to Thrun, AI learns faster due to its ability to continuously refine through vast amounts of data. Using deep learning as an example, AI, machine learning, and human learning operate differently. "People might encounter traffic mishaps occasionally. Even if someone learns from their mistakes, it won't prevent others from making similar ones," Thrun explained.
Google’s AI inputs driving functions into computers via memory, enabling the computer to learn. Through continuous robot learning, everyone else benefits from the knowledge gained through car networks. Thus, AI learns faster than humans.
Thrun strongly believes that by 2050, autonomous driving will outperform human driving. Moreover, with the advent of deep learning, AI technology has become pivotal in sectors such as cancer diagnosis and telemedicine. Deep learning will ensure ongoing advancements. "If autonomous driving becomes a reality, approximately 10% of jobs will transform. For instance, the role of a truck driver might be replaced. Whether young or old, people could enjoy seamless driving experiences," Thrun added.
Thrun mentioned that Google’s self-driving team and Uber have tested EZGO, an autonomous ride-sharing project. Users can summon an autonomous ride-sharing car via an app, opt to drive manually, or let the car drive itself. When the destination is reached, the car can autonomously park itself.
"Do we see cars as servers or private items? Do customers want to own the car or merely benefit from the services it provides? These questions involve business models," Thrun noted.
Counterattacks from Traditional Automakers
For the traditional automotive supply chain, this will undeniably bring changes to the nature of vehicles, production methods, and business models. "Autonomous driving technology will transform cars into smart mobile spaces and fundamentally alter the automotive industry's business model. People won't need to purchase cars; instead, they'll call for autonomous ride-sharing cars," said Xu Heyi, Chairman of Beijing Automotive Group.
Currently, the development of artificial intelligence is considered a vital strategy for enhancing national competitiveness and securing territorial security. The automotive sector has long been closely monitored as a key AI track. The global automotive industry is becoming increasingly complex, with competition among traditional automakers shifting towards the AI domain. Tech companies have consistently entered the market disruptively.
"Since AI defines the future of cars, the auto industry's future competition will hinge on AI talent. Companies like BAIC, which excel in manufacturing, face AI challenges. New car companies such as NIO have better technological roots and Internet company genes, making them more appealing to AI talent," Li Bin, Chairman of NIO, responded when asked which company, BAIC or NIO, was stronger. "It seems NIO cars are more likely to succeed, much like how Amazon overtook Walmart."
Xu Heyi responded: "I agree with Li Bin in principle, but competition isn't just about talent—it involves other resources too."
From a technology implementation standpoint, Audi undoubtedly leads the autonomous driving industry. However, Stefan Greiner, Head of Research and Development for Audi China's Autonomous Driving and Chassis, noted that the hype around autonomous driving has grown over the past few years. Yet, until two years ago, few automakers had drivers outside their companies. Mainstream manufacturers realized that autonomous driving technology was maturing and began exploring how to quickly implement related technologies. In this sense, automakers are becoming more pragmatic.
"For the depot, safety is the primary concern. It doesn’t just refer to infrastructure but also the entire vehicle sensor system. For instance, self-driving cars have various radars, cameras, and laser systems. First, you need to verify the safety of these components in the vehicle," Greiner said.
Thrun believes that main manufacturers play a critical role in the development of self-driving vehicles. After years of building experience, OEMs now face a new challenge: emphasizing the importance of AI software in the manufacturing process to ensure auto parts adapt to autonomous driving technology over the next 20 years.
Still at a Technical Outbreak Stage
China’s autonomous vehicle brands are keeping pace with leading international firms. However, the future development of autonomous driving still requires breakthroughs in laws, regulations, ethics, and infrastructure. Currently, the safety of technology applications remains a major concern for consumers.
According to Chen Juhong, Vice President of Tencent, the industry is now entering the first phase of a technology outbreak. "Over the next four years, 5G communication, newly developed and continuously refined AI algorithms, low-cost laser radars, new client-side OS interfaces, and cloud-based technologies supported by big data training will all explode, stimulating commercialization. Around 2021, we will see the emergence of artificial intelligence-driven unmanned vehicles."
After the technology outbreak, artificial intelligence unmanned vehicles will face challenges such as battery energy density and other energy industry issues, basic transportation facility needs, manual vehicle lifespan, and more. Manual-operated vehicles and commercial artificial intelligence unmanned vehicles will experience a roughly 12-year transitional period before entering the third phase of the intelligent transportation era.
Today’s auto AI evolutionary path still faces challenges across five dimensions: policy, regulations, infrastructure, high-precision maps, technical standards, and acceptance.
Wang Xia, President of the Automotive Industry Branch of the China Council for the Promotion of International Trade and Chairman of the Chamber of Commerce of the China Chamber of International Chamber of Commerce of the Automotive Industry, echoed this sentiment. "The complexity of artificial intelligence means its development can't happen overnight. Many current automotive products are starting to feature voice interactive systems, but they lack self-learning capabilities. The functions of the car's physical conditions, our road conditions, people’s needs, and car networking haven't yet been integrated into a unified platform. Artificial intelligence is still in its infancy."
At the same time, the industry’s mergers and acquisitions and investment booms have made smart cars a focal point. However, many product technologies are similar and lack core innovations. It's important to remember that we're facing a technology-driven transformation. Without core technology, blindly following trends will lead to minimal impact, even if temporarily successful.
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