American car company throws hundreds of millions of dollars to compete for artificial intelligence experts

Competition in the field of driverless cars is becoming more and more cruel.

For example, Tesla, an American electric vehicle manufacturer, is suing its former assistant director of Autopilot, Sterling Anderson.

Tesla said that Anderson had stolen data for its rival, Aurora Innovations, and Aurora Innovations' products have not yet been truly formed. Regarding Tesla’s allegations, Aurora denied it.

In Tesla’s statement of Anderson’s statement, several words point out the nature of competition in the field of driverless cars and the culture nurtured by the industry.

Tesla said: "In order to achieve rapid catch-up, traditional cars have created an environment that can quickly become rich. Some small programmers who only demonstrate samples set a high price of up to one billion US dollars. Cruise Automation is only 40 people. The company, but GM bid nearly $1 billion for its acquisition in July 2016. In August 2016, Uber, the originator of the taxi application, acquired Otto, another small unmanned start-up startup, which only founded nearly seven companies. Month, but worth more than $680 million."

Otto is currently the key to Uber's complaint from Waymo, an unmanned company owned by Alphabet. Waymo said Uber stole its patented technology.

Why are these unnamed micro-entrepreneurs able to sell such high prices? Artificial intelligence professionals say that these acquisitions are not related to the technology of start-ups and that buyers are looking for talent that their company owns.

At present, professionals involved in deep learning are really scarce, and deep learning is the key to high-end driverless technology. This technology is superior to traditional programming, which enables cars to learn safe driving faster.

Yoshua Bengio, a professor of computer science at the University of Montreal, said: "Demand growth is much faster than we are training doctoral or even master's degrees in this field. The industry's interest in this area has exploded. It's like a wild fire spreading on the prairie."

The growth of demand

Machine learning has developed extremely rapidly over the past five years.

In March of last year, Google DeepMind's R & D program defeated the world champion in an extremely complex go game. Before this, Go has always been considered a difficult problem to overcome with artificial intelligence. Because it changes more than cosmic atoms.

This is not the latest achievement of artificial intelligence. In January of this year, the artificial intelligence Libratus developed by Carnegie Mellon University defeated the world's top four Texas Hold'em poker players and won $1.8 million in prize money.

With the rapid development of the field of artificial intelligence, major companies have expanded their artificial intelligence team's scale.

According to Geoff Gordon, an associate researcher in artificial intelligence at Carnegie Mellon University, “As a result of the development of machine learning and artificial intelligence, companies have been recruiting professors in this area.”

However, car companies are particularly willing to invest in artificial intelligence because they start late in this respect compared with traditional technology companies.

Due to the wide range of applications for deep learning, it is not limited to driverless cars. Car manufacturers have to compete with their counterparties, even traditional technology companies.

According to a report issued by KPMG, an accounting firm, in 2016, there were only 28 companies with more than 10 experts in deep learning. Moreover, six technology companies employ 54% of experts in the industry, they are Google (microblogging), Microsoft, NVIDIA, IBM, Intel and Samsung Electronics.

Gary Silberg, head of KPMG's automotive industry, said: “The traditional forces and talent in the automotive industry are the cornerstone of their product development. As a result, car companies will still recruit outstanding mechanical and electronic engineers from the top engineering schools. They are part of product development.”

Hilberg said that there is still a need for a process, it is impossible to immediately recruit artificial intelligence geniuses and computer scientists, and look forward to their immediate integration into car companies.

Bottleneck effect

In March 2015, Uber hollowed out the Artificial Intelligence and Robotics Center at Carnegie Mellon University. Uber hijacked more than 50 experts, including Anthony Stentz, who has been the director of the center for the past four and a half years.

A few months later, Uber announced a strategic partnership with Carnegie Mellon University and created an Uber high-end technology center for driverless cars.

Wu Enda, a well-known deep-learning expert, left Stanford in 2014 to become Baidu's chief scientist. Baidu has been approved to carry out unmanned car testing in California and has been tested in China.

After the brain drain, major universities have worked hard to fill the gap left.

Bengio said that the recruitment of talents for artificial intelligence at such a speed has led to the emergence of bottlenecks, because it is impossible to train talents at this speed. Bengio is considered to be a pioneer in deep learning. In general, the average time spent on an artificial intelligence doctoral degree is about five years.

Since the professors have all left school, to technology companies and car companies to take up the position, this issue has been gradually enlarged.

Professor Bengeo said: "The demand for talent in this area has increased, but the bottleneck is that there are not enough academic laboratories, and there are not enough professors to supervise students who want to work in this field."

Gordon of Carnegie Mellon University stated that he hopes that the number of students studying in deep learning will increase. In the coming academic year, Carnegie Mellon University received 800 applications for a doctoral degree, compared with only 300 applications only two years ago.

Bengio said that 600 people from the University of Montreal apply for machine learning every year, and he can only bring about 20 people.

The upsurge of machine learning is encouraging, but due to the limited number of professors, the scale of its enrollment is also very limited.

Bengio said that there are fewer professionals in the industry and most of them have been dug up by companies. Now, compared to a few years ago, the number of professors is even smaller.

Water goes up

The shortage of artificial intelligence talent means that these professionals are doubling their worth, and start-ups with such talents are often able to meet the needs of large companies for acquisitions.

General Motors spent $581 million in 2016 to acquire start-up company Cruise Automation. Uber spent $680 million in August last year to acquire Otto, a start-up company for driverless trucks.

Recently, Ford invested $1 billion in Argo AI, a start-up in Pittsburgh. This investment will be completed in the next five years.

Ford HR Director Julie Lodge-Jarrett said that the investment in Argo AI shows how the company will be able to recruit and retain talent as the market needs for artificial intelligence experts grows. Change its strategy.

Lodge - Jarrett said that the company's demand for scientific and technological talents is increasing. For example, artificial intelligence and robotics, the company also needs more talent in this area, and sees competition for these special talents will further intensify.

Lodge - Jarrett said that the company is also seeking to acquire a large number of talent opportunities, because it will help future recruitment.

One of the founders of Argo AI was Bryan Salesky, who was the head of hardware for Google’s driverless car division. The company's other founder, Rander, who was previously director of engineering at Uber’s driverless car center.

This relationship makes it easier for Ford to hire such talent later, and they used to work for the tech giant.

Bengio said that the investment in Argo AI shows "the mode of operation of the market over the past few years."

Bengio said: "What's sad is that most of this behavior is purely for hiring. This is a loss to the economy because after these small start-ups are integrated by big companies, their original projects are difficult to continue. In terms of species, it can be said that it is a waste of resources and investment."

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