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Andrew Ng Launches Data-Centric AI Competition
Andrew Ng Launches Data-Centric AI Competition

Andrew Ng Launches Data-Centric AI Competition

DeepLearning.AI and Landing.AI collaborated to announce the Data-Centric AI Competition

Published Updated

Key takeaways

  • DeepLearning.AI and Landing.AI have launched a unique Data-Centric AI Competition that focuses on improving datasets rather than model performance.
  • Participants can submit their modified datasets from June 17 until September 4, 2021, coinciding with John McCarthy's birthday.
  • Winners in two categories will have the opportunity to engage with Andrew Ng and will be featured in various DeepLearning.AI and Landing.AI platforms.
  • This competition highlights the importance of dataset engineering, which has not kept pace with advancements in model architectures.
5 min

In a new format, DeepLearning.AI and Landing.AI collaborated to announce -- the Data-Centric AI Competition whereby participants are asked to improve a dataset given a fixed model. Andrew Ng invited participants to be a part of this unique competition. 

Starting from June 17, contestants can submit their altered dataset for evaluation latest by September 4 2021 - the birth date of John McCarthy, who coined the term artificial intelligence - by 6 PM PT (06:30 AM IST). 

The top three winners from each of the two categories (Best Performance Overall and Most Innovative) will be invited to a private event with Andrew Ng to share ideas about how to grow the data-centric movement. They will be highlighted in The Batch and other DeepLearning.AI and Landing AI channels.

Image Credits: Twitter

Traditionally, in most machine learning competitions, a fixed dataset is given and asked to develop a high-performing model. On the other hand, machine learning has progressed to the point where high-performance model architectures are widely available, whereas dataset engineering approaches have lagged.

In the Data-Centric AI Competition, one needs to improve a dataset given a fixed model. To be precise, a dataset will be provided to improve by applying data-centric techniques such as fixing incorrect labels, adding examples representing edge cases, applying data augmentation, etc. The once in a while opportunity is here that needs to be grabbed.

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