Uber opened a research facility around the corner from Carnegie Mellon University’s National Robotics Engineering Center in a move positioned as a partnership between the two organizations.
copyright by venturebeat.com
Uber opened a research facility around the corner from Carnegie Mellon University’s National Robotics Engineering Center in a move positioned as a partnership between the two organizations. Within months, dozens of faculty members had left their positions for full-time roles at Uber, draining the center of much of its talent. Other major tech companies have followed a similar path – in 2018, Facebook launched AI labs in Seattle and Pittsburgh headed by former professors.
These stories provide a window into a tug-of-war that’s been playing out between the tech industry and academia. Keen to build products and services that use AI and machine learning , tech firms and other businesses have been hiring away researchers and professors from universities, creating a shortage of academics who can teach the next generation of data scientists. The proportion of computer science PhDs who stay in academia has reached a “historic low,” the Computing Research Association has said.
In waging this battle, however, the tech industry is endangering its own future as well as progress in AI. Besides nurturing tomorrow’s talent, universities host the kind of blue sky research that corporations are often reluctant to take on because the financial returns are unclear. By moving talented researchers out of academia, businesses are disrupting this important basic research.
At the same time, enterprises face a severe shortage of data science and machine learning experts. In an O’Reilly survey of more than 1,600 developers, data scientists, and data analysts released last year, 74 percent of respondents said they consider ML and AI to be a game changer. Yet the lack of qualified talent remains a major hurdle. Universities are trying to address this by offering more courses in data science and machine learning, but this requires qualified professors to teach them – the same people being lured away by industry.
To tackle this conflict, tech companies should change their relationship with the academic community. Instead of hiring academics full time, more companies should allow researchers and professors to split their time between the two worlds. This could mean remaining in academia and working part time for a business, or vice versa. Either way creates a win for both sides. Universities continue to benefit from the brightest minds in AI and data science, while businesses get their in-house expertise and a pipeline for AI talent through internships.[…]
read more – copyright by venturebeat.com
Uber opened a research facility around the corner from Carnegie Mellon University’s National Robotics Engineering Center in a move positioned as a partnership between the two organizations.
copyright by venturebeat.com
These stories provide a window into a tug-of-war that’s been playing out between the tech industry and academia. Keen to build products and services that use AI and machine learning , tech firms and other businesses have been hiring away researchers and professors from universities, creating a shortage of academics who can teach the next generation of data scientists. The proportion of computer science PhDs who stay in academia has reached a “historic low,” the Computing Research Association has said.
In waging this battle, however, the tech industry is endangering its own future as well as progress in AI. Besides nurturing tomorrow’s talent, universities host the kind of blue sky research that corporations are often reluctant to take on because the financial returns are unclear. By moving talented researchers out of academia, businesses are disrupting this important basic research.
At the same time, enterprises face a severe shortage of data science and machine learning experts. In an O’Reilly survey of more than 1,600 developers, data scientists, and data analysts released last year, 74 percent of respondents said they consider ML and AI to be a game changer. Yet the lack of qualified talent remains a major hurdle. Universities are trying to address this by offering more courses in data science and machine learning, but this requires qualified professors to teach them – the same people being lured away by industry.
To tackle this conflict, tech companies should change their relationship with the academic community. Instead of hiring academics full time, more companies should allow researchers and professors to split their time between the two worlds. This could mean remaining in academia and working part time for a business, or vice versa. Either way creates a win for both sides. Universities continue to benefit from the brightest minds in AI and data science, while businesses get their in-house expertise and a pipeline for AI talent through internships.[…]
read more – copyright by venturebeat.com
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