TLDR: He posted an image of a real Monet, told everyone it was AI, and asked them to explain in detail how they could tell. People flooded replies explaining the technical elements that prove it's AI (the brushstrokes, colour, depth are off, etc). Then he broke the news that it was real.
Indian factory workers are training the next generation of robotic AI in a way most executives are missing.
Workers in this factory are not just producing garments, they are generating one of the most valuable assets in AI: real-world training data.
By wearing head cameras, they capture tasks from a first-person view, showing how hands move, adjust, and assemble in live production conditions. This gives AI systems exposure to context, repetition, and the messy reality of how work actually gets done.
For leaders, this is the bigger signal: competitive advantage in AI will increasingly come from proprietary operational data, not just bigger models.
The companies that win may not be the ones with the flashiest demos. They may be the ones quietly building data pipelines from real workflows, real environments, and real human expertise.
This also raises bigger questions for the enterprise. Who owns that data? How is worker consent handled? How do organizations balance automation, productivity, and trust?
We are entering a phase where the factory floor, warehouse, hospital, and field site become training grounds for the next generation of intelligent machines.
That shift is worth paying attention to.
What are your thoughts?
#ArtificialIntelligence #AI #Robotics #Robot #Automation #DataScience #Technology | 41 comments on LinkedIn
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Seit 2017 erprobt die VolkswagenStiftung ein neues Auswahlverfahren für Projektanträge: In ihrer Förderlinie "Experiment!" werden nicht nur Projekte von einer unabhängigen Jury ausgewählt, sondern zusätzlich weitere Vorhaben aus den zum Programmziel passenden und qualitativ uneingeschränkt förderbaren Anträgen ausgelost. Hintergründe, Reaktionen und Begleitforschung zu einem ungewöhnlichen Verfahren.
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Ubitec begleitet Sie mit KI-gestützten Chat- und Voicebots in die digitale Zukunft. Sicher, effizient und individuell – mit messbarem Erfolg. Jetzt informieren!
KI Lokal: Use-Cases für den digital souveränen KI-EinsatzKI #tuaustria #tugraz
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Mehr als 80 Prozent der digitalen Technologien und Infrastrukturen in Europa stammen aus dem Ausland. 70 Prozent der weltweit eingesetzten Grundmodelle für Künstliche Intelligenz (KI) kommen aus den USA. Lediglich 7 Prozent der globalen Forschungsausgaben im Bereich Software und Internet werden von europäischen Unternehmen getragen. Diese drei Zahlen verdeutlichen eindrucksvoll, wie stark Europa im Bereich der Digitalisierung von anderen Wirtschaftsmächten abhängig ist. Der „EuroStack“ soll der Schlüssel zur digitalen Souveränität und Selbstbestimmung der Europäischen Union werden.
aTrain – Kostenloser Download und Installation unter Windows | Microsoft Store
aTrain is a tool for automatically transcribing speech recordings utilizing state-of-the-art machine learning models without uploading any data to the internet. It was developed by researchers at the Business Analytics and Data Science-Center at the University of Graz and tested by researchers from the Know-Center Graz.
aTrain offers the following benefits:
- Fast and accurate
aTrain provides a user friendly access to the faster-whisper implementation of OpenAI’s Whisper model, ensuring best in class transcription quality paired with higher speeds on your local computer. Transcription when selecting the highest-quality model takes only around three times the audio length on current mobile CPUs typically found in middle-class business notebooks (e.g., Core i5 12th Gen, Ryzen Series 6000).
- Speaker detection
aTrain has a speaker detection mode and can analyze each text segment to determine which speaker it belongs to.
- Privacy Preservation and GDPR compliance
aTrain processes the provided speech recordings completely offline on your own device and does not send recordings or transcriptions to the internet. This helps researchers to maintain data privacy requirements arising from ethical guidelines or to comply with legal requirements such as the GDRP.
- Multi-language support
aTrain can process speech recordings in any of the following 57 languages: Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh.
- MAXQDA and ATLAS.ti compatible output
aTrain provides transcription files that are seamlessly importable into the most popular tools for qualitative analysis, ATLAS.ti and MAXQDA. This allows you to directly play audio for the corresponding text segment by clicking on its timestamp.
- NVIDIA GPU support
aTrain can either run on the CPU or an NVIDIA GPU (CUDA toolkit installation required). A CUDA-enabled NVIDIA GPU significantly improves the speed of transcriptions and speaker detection, reducing transcription time to 20% of audio length on current entry-level gaming notebooks.
LLM-assisted proposal writing in competitive R&D funding: Evidence from Horizon Europe
Large language models (LLMs) can lower the cost of producing complex text, potentially reshaping competition for research and development (R&D) funding to private firms. We provide the first evidence on this issue using data covering the universe of firm applications to a major competitive R\&D funding program: Horizon Europe. We find that LLM-assisted writing rises sharply following the public release of ChatGPT in late 2022, with around 40% of proposal abstracts exhibiting LLM-modified content by the end of 2024. Adoption is heterogeneous across applicants and is more common among younger, and less innovative firms, as well as among firms located in countries with lower levels of English proficiency, economic development and R&D intensity. In cross-sectional analyses, proposals that rely extensively on LLM-generated text are associated with lower evaluation scores and funding probabilities, whereas partial LLM assistance is only weakly related to such outcomes. However, analyses exploiting repeated submissions of the same proposals do not indicate that adopting LLM-assisted writing causally worsens evaluation results. Overall, the findings suggest that generative AI may reduce barriers to participation in competitive funding without clear evidence that LLM-assisted writing itself alters evaluation decisions.
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