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The future of deep learning (according to its pioneers)
Current deep learning systems are still limited in the scope of problems they can solve.
They perform well on specialized tasks but “are often brittle outside of the narrow domain they have been trained on.” Often, slight changes such as a few modified pixels in an image or a very slight alteration of rules in the environment can cause deep learning systems to go astray.
In this article, review recent advances in deep learning that have helped make progress in some of the fields where deep learning struggles, including Transformers and Contrastive Learning.
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