{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Instrumental Convergence — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Instrumental Convergence</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">The observation, formalized by Nick Bostrom, that a wide range of AI systems with very different ultimate goals would likely converge on certain intermediate goals, such as self-preservation, resource acquisition, and resistance to goal modification, because these are useful for achieving almost any objective. Instrumental convergence is a key concept in AI safety: it suggests that even an AI with seemingly benign goals could develop concerning behaviors as a side effect of pursuing them effectively.  See also: control problem, corrigibility, AI alignment.</p><a href=\"https://gaks.ai/glossary/instrumental-convergence\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/instrumental-convergence →</a></div>"}