Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911519894142976 |
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| author | Dupoux, Emmanuel LeCun, Yann Malik, Jitendra |
| author_facet | Dupoux, Emmanuel LeCun, Yann Malik, Jitendra |
| contents | We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition. The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while flexibly switching between these learning modes as a function of internally generated meta-control signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt to real-world, dynamic environments across evolutionary and developmental timescales. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_15381 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science Dupoux, Emmanuel LeCun, Yann Malik, Jitendra Artificial Intelligence We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition. The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while flexibly switching between these learning modes as a function of internally generated meta-control signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt to real-world, dynamic environments across evolutionary and developmental timescales. |
| title | Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.15381 |