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Deep Learning and Artificial General Intelligence: Still a Long Way to Go

arXiv 2203.14963 ↗First posted 25 Mar 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

In recent years, deep learning using neural network architecture, i.e. deep neural networks, has been on the frontier of computer science research. It has even lead to superhuman performance in some problems, e.g., in computer vision, games and biology, and as a result the term deep learning revolution was coined. The undisputed success and rapid growth of deep learning suggests that, in future, it might become an enabler for Artificial General Intelligence (AGI). In this article, we approach this statement critically showing five major reasons of why deep neural networks, as of the current state, are not ready to be the technique of choice for reaching AGI.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 25 Mar 2022.

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References · 8

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  3. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  4. 2017Adversarial Patchcited by 517
  5. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 460
  6. 2018A Survey on Deep Transfer Learningcited by 419
  7. 2021Monte Carlo Tree Search: A Review of Recent Modifications and Applicationscited by 161
  8. 2016The AGI Containment Problemcited by 36

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