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Landing AI hires vision expert Dechow to correct the Big Data fallacy

#artificialintelligence

The field of deep learning has been suffering from what you might call a Big Data fallacy, the belief that more and more data is always a good thing. It may be time to focus on quality rather than just quantity. "There's a very fundamental problem that a lot of AI faces," said Andrew Ng, founder and CEO of Landing AI, a startup working to perfect the technology for industrial uses, in an interview with ZDNet this week. "A lot of AI is focused on maximizing the number of calories, which works up to a certain point," he said. "And sometimes you do have a lot of data, but when you have a small data set, it's more the quality of the data rather than the sheer volume."


Landing.AI hires vision expert Dechow to correct the Big Data fallacy

ZDNet

The field of deep learning has been suffering from what you might call a Big Data fallacy, the belief that more and more data is always a good thing. It may be time to focus on quality rather than just quantity. "There's a very fundamental problem that a lot of AI faces," said Andrew Ng, founder and CEO of Landing.AI, a startup working to perfect the technology for industrial uses, in an interview with ZDNet this week. "A lot of AI is focused on maximizing the number of calories, which works up to a certain point," he said. "And sometimes you do have a lot of data, but when you have a small data set, it's more the quality of the data rather than the sheer volume."


Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team

#artificialintelligence

KDnuggets recently ran an Automated Data Science and Machine Learning blog contest, which garnered numerous entries and lots of appreciation for the winning posts and a pair of honorable mentions. The winning post, titled Contest Winner: Winning the AutoML Challenge with Auto-sklearn, written by Matthias Feurer, Aaron Klein, and Frank Hutten, all of the University of Freiburg, provides an overview of Auto-sklearn, an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. The project is built around the successful scikit-learn library and won the recent AutoML challenge. Given the popularity of the post, we asked the authors if they would be interested in answering a few followup questions on themselves, their project, and automated data science in general. What follows is the result of this conversation.