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The Sequence Scope: The Challenge of Data-Efficient Machine Learning
This is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Give it a try by subscribing below:
Editorial
Supervised learning is the dominant school in machine learning solutions. The idea of training a model in a labeled dataset in order to master a task seems intuitive. However, in practice, many supervised learning techniques run into the challenges that require large labeled datasets in order to generalize even very simple knowledge.
This challenge is overwhelming for both startups and big companies and is one of the main roadblocks for the mainstream adoption of ML. We all love to hear about breakthroughs like AlphaGo or GPT-3 until you realize the ginormous size of the training datasets used to create those models.
The idea of building machine learning methods that can operate with smaller labeled datasets is an active area of research and there is…
