Tootfinder

OpenAI raised concerns about anti-competitive conduct by "entrenched companies" in a September EU meeting; source: OpenAI targeted Google, Microsoft, and Apple (Samuel Stolton/Bloomberg)
https://www.bloomberg.com/news/articles/20

OpenAI Warns EU Antitrust Watchdogs of Big Tech’s Data Dominance
OpenAI has raised concerns with European Union antitrust enforcers over potentially harmful conduct by the likes of Alphabet Inc.’s Google, Microsoft Corp. and Apple Inc.

Poet and writer Daniela Naomi Molnar on art as an antidote to loneliness
https://thecreativeindependent.com/people/poet-and-…

Poet and writer Daniela Naomi Molnar on art as an antidote to loneliness – The Creative Independent
Poet and visual artist Daniela Naomi Molnar discusses writing through the difficult things in life, the transformation from saying yes to saying no, and how delegating leaves more room for art

Almost 10 years ago, I wrote an article about consolidation in movies, and the vertical integration perils of movie studios owning the movie distribution channels.
4 years ago, I republished it, because the trend continued.
Now, the trend keeps going. Not great for the industry or the audience.
https://

What’s Old Is New Again: Antitrust
will consolidation change TV and film streaming

Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments
Amir Homayounirad, Enrico Liscio, Tong Wang, Catholijn M. Jonker, Luciano C. Siebert
https://arxiv.org/abs/2509.06704

Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments
Aggregating multiple annotations into a single ground truth label may hide valuable insights into annotator disagreement, particularly in tasks where subjectivity plays a crucial role. In this work, we explore methods for identifying subjectivity in recognizing the human values that motivate arguments. We evaluate two main approaches: inferring subjectivity through value prediction vs. directly identifying subjectivity. Our experiments show that direct subjectivity identification significantly …

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Jiaman He, Zikang Leng, Dana McKay, Damiano Spina, Johanne R. Trippas
https://arxiv.org/abs/2510.06658

Can We Hide Machines in the Crowd? Quantifying Equivalence in LLM-in-the-loop Annotation Tasks
Many evaluations of large language models (LLMs) in text annotation focus primarily on the correctness of the output, typically comparing model-generated labels to human-annotated ``ground truth'' using standard performance metrics. In contrast, our study moves beyond effectiveness alone. We aim to explore how labeling decisions -- by both humans and LLMs -- can be statistically evaluated across individuals. Rather than treating LLMs purely as annotation systems, we approach LLMs as an alternat…

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets
Pedro Ramoneda, Pablo Alonso-Jimenez, Sergio Oramas, Xavier Serra, Dmitry Bogdanov
https://arxiv.org/abs/2509.06936

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets
Music autotagging aims to automatically assign descriptive tags, such as genre, mood, or instrumentation, to audio recordings. Due to its challenges, diversity of semantic descriptions, and practical value in various applications, it has become a common downstream task for evaluating the performance of general-purpose music representations learned from audio data. We introduce a new benchmarking dataset based on the recently published MGPHot dataset, which includes expert musicological annotati…

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
Boyuan Long, Yueqi Wang, Hiloni Mehta, Mick Zomnir, Omkar Pathak, Changping Meng, Ruolin Jia, Yajun Peng, Dapeng Hong, Xia Wu, Mingyan Gao, Onkar Dalal, Ningren Han
https://arxiv.org/abs/2510.06657

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content "vibe") at scale within a large-scale industrial short-form video recommendation system. Traditional machine learning classifiers for content understanding face protracted development cycles and a lack of deep, nuanced comprehension. The "LLM-as-annotators" approach addresses these by significantly shortening developm…

The US v. Google ruling signals a cautious antitrust approach by courts and a reluctance to intervene too deeply, ahead of other Big Tech antitrust rulings (Steve Lohr/New York Times)
https://www.nytimes.com/2025/09/03/technology/google-ruling-antitrust.html…

Google Search Monopoly Ruling Sends Signal for Big-Tech Antitrust Cases
A federal judge ordered steps in the search monopoly case that will restrain Google but not break it up, signaling a cautious antitrust approach by courts.

Sources: Meta and Apple are close to settling two EU antitrust cases and agreeing a deal to change their business practices after a total €700M in DMA fines (Barbara Moens/Financial Times)
https://www.ft.com/content/74a31a0f-d4d2-4681-9ce8-3a1b5a7632dc

Memo: Apple's March update to its AI training guidelines for data annotators marked DEI as a "controversial" topic and removed intolerance as "harmful" behavior (Océane Herrero/Politico)
https://www.politico.eu/article/apple-teac

Apple is teaching its artificial intelligence to adapt to the Trump era
Apple policy documents enacted by a subcontractor show how the company shifted its approach to fine-tuning its AI in March, two months after the U.S. president was inaugurated.