{"id":2263110,"date":"2026-02-02T16:20:45","date_gmt":"2026-02-02T16:20:45","guid":{"rendered":"https:\/\/celebrity.land\/en\/?p=2263110"},"modified":"2026-02-02T16:20:45","modified_gmt":"2026-02-02T16:20:45","slug":"ux-roundup-ai-judgment-heuristic-evaluation-consistent-brand-assets","status":"publish","type":"post","link":"https:\/\/celebrity.land\/en\/ux-roundup-ai-judgment-heuristic-evaluation-consistent-brand-assets\/","title":{"rendered":"UX Roundup: AI Judgment | Heuristic Evaluation | Consistent Brand Assets"},"content":{"rendered":"

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Summary<\/strong>: AI judgment may follow a scaling law | Scaling AI\u2019s judgment of usability heuristics | Generating consistent visual design assets with AI | New AI music model: Mureka O2<\/span><\/p>\n<\/blockquote>\n

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UX Roundup for February 2, 2026. (Nano Banana Pro)<\/em><\/p>\n

The \u201cbitter lesson\u201d just became <\/span>more bitter<\/em> for those humans who still believe in meatware supremacy and their \u201cunique\u201d ability to have \u201ctaste\u201d or judgment about what\u2019s best among the ceaseless AI creations. AI can also exhibit judgment, and it appears that its judgment gets better with more compute, meaning that it will likely become superior to human judgment in a few years, as AI compute keeps scaling up.<\/span><\/p>\n

(In general, \u201cthe bitter lesson\u201d for AI is that throwing more compute at a problem consistently outperforms approaches built on human knowledge and domain expertise. Time and again across chess, Go, speech recognition, and computer vision, researchers initially made progress by encoding human understanding into systems, but these approaches were ultimately surpassed by simpler methods that scaled with available compute. The \u201cbitter\u201d part is that this lesson is psychologically hard for researchers to accept: we want to believe our insights about the structure of problems matter, but history shows that betting on more compute and letting AI systems learn and work on their own wins in the long run.)<\/p>\n

A <\/span>new research paper<\/a> by Bingyang Ye from Harvard and several colleagues shows that <\/span>AI scales its judgment abilities with more compute<\/strong>. It\u2019s too early to declare this a new AI scaling law because Ye only studied a single domain: AI\u2019s ability to predict which scientific papers would later be seen as the most important.<\/span><\/p>\n

The study cleverly utilized the fact that there is a broadly accepted metric for the importance of academic papers: how many other scientists later cite each paper. In the study, the researchers limited their AI to run offline, using only knowledge as of a given date in the past, meaning the AI could not know how many citations each paper would later receive. (But the researchers knew the citation count, meaning that they could score the AI\u2019s judgments.)<\/p>\n

The interesting point here is not how good AI was at judging which research papers would become the most influential, but rather that this judgment improved with more compute, in two ways:<\/p>\n

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