The Alignment Problem from a Deep Learning Perspective

In coming years or decades, artificial general intelligence (AGI) may surpass human capabilities at many critical tasks. 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 that generalize beyond their fine-tuning distributions, and pursue those goals using power-seeking strategies. AGIs with these properties would be difficult to align and may strategically appear aligned even when they are not. In this revised paper, we expand our review of emerging evidence for these properties to include more direct empirical observations published as of early 2025. 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.