The last 10 years have brought tremendous growth in artificial intelligence. Consumer internet companies have gathered vast amounts of data, which has been used to train powerful machine learning programs. Machine learning algorithms are widely available for many commercial applications, and some are open source.
Now it’s time to focus on the data that fuels these systems, according to AI pioneer Andrew Ng, SM ’98, the founder of the Google Brain research lab, co-founder of Coursera, and former chief scientist at Baidu.
Ng advocates for “data-centric AI,” which he describes as “the discipline of systematically engineering the data needed to build a successful AI system.”
AI systems need both code and data, and “all that progress in algorithms means it's actually time to spend more time on the data,” Ng said at the recent EmTech Digital conference hosted by MIT Technology Review.
Focusing on high-quality data that is consistently labeled would unlock the value of AI for sectors such as health care, government technology, and manufacturing, Ng said.
“If I go see a health care system or manufacturing organization, frankly, I don't see widespread AI adoption anywhere.” This is due in part to the ad hoc way data has been engineered, which often relies on the luck or skills of individual data scientists, said Ng, who is also the founder and CEO of Landing AI.
Data-centric AI is a new idea that is still being discussed, Ng said, including at a data-centric AI workshop he convened last December. But he pointed to some common problems he sees with data:
Continue reading: https://mitsloan.mit.edu/ideas-made-to-matter/why-its-time-data-centric-artificial-intelligence
Now it’s time to focus on the data that fuels these systems, according to AI pioneer Andrew Ng, SM ’98, the founder of the Google Brain research lab, co-founder of Coursera, and former chief scientist at Baidu.
Ng advocates for “data-centric AI,” which he describes as “the discipline of systematically engineering the data needed to build a successful AI system.”
AI systems need both code and data, and “all that progress in algorithms means it's actually time to spend more time on the data,” Ng said at the recent EmTech Digital conference hosted by MIT Technology Review.
Focusing on high-quality data that is consistently labeled would unlock the value of AI for sectors such as health care, government technology, and manufacturing, Ng said.
“If I go see a health care system or manufacturing organization, frankly, I don't see widespread AI adoption anywhere.” This is due in part to the ad hoc way data has been engineered, which often relies on the luck or skills of individual data scientists, said Ng, who is also the founder and CEO of Landing AI.
Data-centric AI is a new idea that is still being discussed, Ng said, including at a data-centric AI workshop he convened last December. But he pointed to some common problems he sees with data:
Continue reading: https://mitsloan.mit.edu/ideas-made-to-matter/why-its-time-data-centric-artificial-intelligence
