[Netease Smart News October 28 news] Artificial Intelligence has become the latest darling of Silicon Valley. Google, Facebook, Amazon, and Apple have applied artificial intelligence to their image recognition algorithms, voice virtual assistants, and help you organize news and social media content. This is also a hot area for small startups. In the five months since the beginning of the year, the risk investment for artificial intelligence has increased from US$3.2 billion in 2014 to US$9.5 billion. This is the largest investment area in the year and the largest recruitment area.
Although research in this area dates back to the 1950s, until recently, artificial intelligence developed into a mainstream computer science topic. Under such circumstances, the number of experts in this field is limited (and relatively small), and the number of experts who continue to stay in academia is decreasing due to the huge profits provided by technology companies.
In a report in the New York Times this week, Cade Metz described in detail how large technology companies attract AI experts, who can provide up to 500,000 US dollars in annual salary. "Only 10,000 people worldwide have the skills needed to address advanced artificial intelligence research," writes Metz. At Stanford University, four famous researchers have left or are preparing to leave in the past few years. At Washington University, another artificial intelligence center, 6 of the 20 professors in the school work in related industries during holidays. If professors and researchers leave academia, who will teach the next generation of artificial intelligence experts?
This is not the first time it has happened. In 2015, Uber poached the core of the robotics laboratory at Carnegie Mellon University, responsible for the company's driverless car project. After initially attracting several developers from the National Robot Engineering Center, Uber eventually hired 40 former CMU employees (about one-third of NREC's total employees). This includes many senior staff, as well as some department heads. "They took all the research team members away," a person familiar with the matter told the Verge.
According to a report from the Pittsburgh Post, as of March 2016, Uber’s position remained vacant (although NREC told the Post-Gazette reporter at the time that they planned to hire $11 million in new research funding. 15 To 20 new employees). But Andrew Moore, Dean of the School of Computer Science at Carnegie Mellon University, dismisses it. Moore told the Pittsburgh Post: "This kind of thing happens several times a year. We should focus on 'What should I do next?"
According to Moore, this kind of thing happens periodically. Researchers may start with academia, then spend several years doing business research, and then return to academia with new ideas and ideas. In an interview with the TechCrunch website in 2016, Moore said that 5 to 15 employees leave each year and work in related industries. The maximum vacation time is up to 4 years. Some people, but not everyone, have finally returned.
This is what we want to see now. Technology companies (other than Uber) do not rely solely on researchers digging from academic institutions, but instead take things in their own hands in a more constructive way.
Companies such as Google and Facebook now offer training courses to help employees speed up artificial intelligence research. Since few experts can succeed in this field, projects such as Facebook's AI Academy are designed to make engineers proficient in deep learning. Once trained, they can apply their new knowledge to the company's other engineering teams. Strategic acquisitions can also help solve the labor shortage problem of artificial intelligence, such as Google's acquisition of DeepMind in 2014, but the premise is that there are artificial intelligence startups can start from this field.
Even so, for companies that require doctoral-level professionals, demand far exceeds supply. If tech companies want to ensure that the number of AI experts in the future is sufficient, they will need to find a balance between hiring experts to meet their needs and allowing them to continue teaching the next generation. (From: future tense compilation: NetEase See Compilation Robot Review: Rain Eggs)
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