https://wiki.alt-text.eu/index.php?action=history&feed=atom&title=Scaling_lawsScaling laws - Revision history2026-09-15T06:24:06ZRevision history for this page on the wikiMediaWiki 1.45.3https://wiki.alt-text.eu/index.php?title=Scaling_laws&diff=70&oldid=previmported>ALT-TEXT: Import: AI terminology and people glossary2026-09-07T10:36:21Z<p>Import: AI terminology and people glossary</p>
<p><b>New page</b></p><div>== Scaling laws ==<br />
Observed, fairly predictable relationships between an AI model's size (its [[Parameters (AI model)|parameters]]), the amount of [[Training data|training data]] and compute used, and its resulting performance. Scaling laws have driven much of the AI industry's strategy over the past decade, on the assumption that bigger models trained on more data will keep improving, though there is active debate about where this trend slows down. (See also: [[Compute (AI)|Compute]], [[Emergent capabilities]])<br />
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[[Category:Glossary]]<br />
[[Category:Artificial Intelligence]]<br />
[[Category:Machine Learning]]<br />
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