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Who's Who of AI
Eiko Fried
Why they matter
Researcher with public evidence across AI research.
- AI signals
- 8 past 30d
- Sources
- 1 distinct domains
- Discussions
- 11 past 30d
- Latest signal
- 1d ago
Professor of Mental Health & Data Science at Leiden University. Studying mental health problems as systems (http://eiko-fried.com). Building an early warning system for depression (http://WARN-D.com).
What they're sharing
Companies Are Using Reddit to Manipulate ChatGPT and Google AI Search 404media.co
How a Starbucks marketing stunt spiralled into mass boycotts in South Korea theguardian.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement - Nature Human Behaviour nature.com
‘Unprecedented’: OpenAI says AI models autonomously hacked another company aljazeera.com
‘Not perfectly aligned’ with human values: Anthropic admits security failures behind AI hacking incidents theguardian.com
Articles & links
Looks like Starbucks used AI to create an incredibly problematic marketing campaign www.theguardian.com/world/2026/j...
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
AI Weekly's analysis
→
- A fine-tuned language model scanned 4,452 items across 459 scales in the International Personality Item Pool and flagged 504 jingle-jangle fallacy candidates.
- Clustering compressed 277 construct labels into 68, cutting fallacies by 58% while requiring 75% fewer unique names.
- The embeddings predicted scale correlations within mean absolute errors of 0.037 to 0.047 and fully recovered 81% of the original scales.
Read full analysis →
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
AI Weekly's analysis
→
- A fine-tuned MPNet model flagged 504 fallacy candidates across 4,452 psychometric items: 340 jingle (same label, different content) and 164 jangle (different labels, same content).
- The authors propose a replacement taxonomy of 68 clusters that reduces fallacy candidates by 58% while using 75% fewer construct labels.
- Embeddings predicted Cronbach's α with mean absolute error of 0.026–0.029 in-sample and 0.065–0.070 out-of-sample across 459 scales covering 277 constructs.
Read full analysis →
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
AI Weekly's analysis
→
- Dirk Wulff and Rui Mata fine-tuned an MPNet embedding model on 200,000 personality item pairs and vectorized 4,452 items across 459 IPIP scales.
- The pipeline flagged 504 'jingle-jangle' fallacies: 340 scales sharing a label but measuring different things, 164 measuring similar things under different labels.
- A reclustering pass cut fallacies by 58% while shrinking unique construct labels by 75%, and reproduced 81% of original scale structures.
Read full analysis →
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
AI Weekly's analysis
→
- A fine-tuned MPNet embedding model flagged 504 fallacy candidates across 459 scales in the International Personality Item Pool, split into 340 jingle and 164 jangle.
- The embeddings predicted internal consistency with Pearson r = 0.75 in-sample and r = 0.61 out-of-sample across 4,452 IPIP items.
- Letting the embeddings regroup constructs cut jingle-jangle fallacies by 58% and compressed 254 unique labels down to 68.
Read full analysis →
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
AI Weekly's analysis
→
- Wulff and Mata ran 4,452 items from the International Personality Item Pool through a fine-tuned language model and flagged 504 taxonomic fallacies: 340 jingle, 164 jangle.
- The method's reorganized mapping produced 58% fewer jingle–jangle fallacies while requiring 75% fewer unique construct labels than the source taxonomy.
- Embeddings predicted empirical internal consistency at r = 0.75 in sample and r = 0.61 out of sample, with 81% of 443 scales recovered in sample.
Read full analysis →
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
AI Weekly's analysis
→
Read full analysis →
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
2025 paper on automated detection of jingle-jangle based on LLM embeddings. Here a proof of principle where they highlight personality scales high in label similarity (similar scale name) but low in item similarity [blue, jingle] and vice versa [beige, jangle]. www.nature.com/…
Right .. www.aljazeera.com/news/2026/7/...
If I hacked 3 companies, was caught, and would argue that “the incident reflected a failure of operational security” on my end, I’d .. still go to jail. Do I understand correctly there won’t by any legal consequences for Anthropic? www.theguardian.com/technology/2...
Their own posts
Recent commentary
Had the privilege to give a #SAA2026 keynote today. Showed early results from WARN-D ML competition, & EMA'd the audience on how different parts of the data—Baseline, EMA, Wearables, & Followup—contributed to predicting depression onset, with live leaderboard & two prizes. Got 220 live responses:
‘OpenAI has disclosed six more examples of “unexpected or concerning” behaviour by its technology, as it warned the pace of development could not continue at “maximum speed for much longer”.’ So like .. stop?
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