The Alignment Problem from a Deep Learning Perspective
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866909600009158656 |
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| author | Ngo, Richard Chan, Lawrence Mindermann, Sören |
| author_facet | Ngo, Richard Chan, Lawrence Mindermann, Sören |
| contents | In coming years or decades, artificial general intelligence (AGI) may surpass human capabilities across many critical domains. We argue that, without substantial effort to prevent it, AGIs could learn to pursue goals that are in conflict (i.e. misaligned) with human interests. If trained like today's most capable models, AGIs could learn to act deceptively to receive higher reward, learn misaligned internally-represented goals which generalize beyond their fine-tuning distributions, and pursue those goals using power-seeking strategies. We review emerging evidence for these properties. In this revised paper, we include more direct empirical evidence published as of early 2025. AGIs with these properties would be difficult to align and may appear aligned even when they are not. Finally, we briefly outline how the deployment of misaligned AGIs might irreversibly undermine human control over the world, and we review research directions aimed at preventing this outcome. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_00626 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | The Alignment Problem from a Deep Learning Perspective Ngo, Richard Chan, Lawrence Mindermann, Sören Artificial Intelligence Machine Learning In coming years or decades, artificial general intelligence (AGI) may surpass human capabilities across many critical domains. We argue that, without substantial effort to prevent it, AGIs could learn to pursue goals that are in conflict (i.e. misaligned) with human interests. If trained like today's most capable models, AGIs could learn to act deceptively to receive higher reward, learn misaligned internally-represented goals which generalize beyond their fine-tuning distributions, and pursue those goals using power-seeking strategies. We review emerging evidence for these properties. In this revised paper, we include more direct empirical evidence published as of early 2025. AGIs with these properties would be difficult to align and may appear aligned even when they are not. Finally, we briefly outline how the deployment of misaligned AGIs might irreversibly undermine human control over the world, and we review research directions aimed at preventing this outcome. |
| title | The Alignment Problem from a Deep Learning Perspective |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2209.00626 |