To predict the future potential of any technology, it used machine learning, patent network analytics, and empirical technology performance data
To help organizations manage research and development (R&D), a team led by an Indian-origin researcher at the Massachusetts Institute of Technology (MIT) has developed a new artificial intelligence (AI) tool that can predict how fast any technology is improving.
Recently published in the journal Research Policy, the study demonstrates the predicted improvement rates for 1,757 technology domains covering 97.2 per cent of the US patent system.
Fastest-improving technologies are predominantly software-related, especially those linked to network management, enterprise security, and media transmission, the researchers noted.
“As technologists, we have to choose between technologies all the time. Many times few technologies end up capturing a lot of attention due to the hype around them”, said study lead author Anuraag Singh from MIT
“However, fast-improving, more promising technologies end up being ignored. We want to bring realism and objectivity to technological decision-making”, Singh said in a statement.
To help organizations anticipate technological advances, breakthroughs, and disruptions, the researchers have also launched a start-up called Technext.
For academic readers to use the system, they also built a free, publicly accessible tool.
The study builds on about two decades of research at MIT, started by Professor Christopher L Magee, one of the co-authors of the study.
To predict the future potential of any technology, it used machine learning, patent network analytics, and empirical technology performance data.
The key technical innovation in the study is the early identification of technological breakthroughs, bottlenecks, and threats, noted the researchers including Giorgio Triulzi from the University of The Andes in Columbia.
This helps organizations avoid being obsolete, improve R&D effectiveness and increase revenue through new product or service development, they said.
The tool can be handy in deploying the USD 2.2 trillion in annual global R&D funding more efficiently by helping organizations avoid low-potential over-hyped technologies and research, the researchers added.
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