The case for ๐ฝ๐ฟ๐ผ๐ฝ๐ฟ๐ถ๐ฒ๐๐ฎ๐ฟ๐ ๐๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ in #localization? It's all about owning the conditions.
Owning the inputs brings predictability.
Owning the system brings control.
Owning the pipeline brings transparency.
None of it retrofits onto a foundation that
For those who are passionate about language technology. Like us. Translated.
- ๐ช๐ฒ๐ฎ๐๐ต๐ฒ๐ฟ ๐ถ๐ ๐๐ต๐ฒ ๐ผ๐น๐ฑ๐ฒ๐๐ ๐น๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ต๐๐บ๐ฎ๐ป๐ถ๐๐ ๐ต๐ฎ๐ ๐ฒ๐๐ฒ๐ฟ ๐๐ฟ๐ถ๐ฒ๐ฑ ๐๐ผ ๐ฑ๐ฒ๐ฐ๐ถ๐ฝ๐ต๐ฒ๐ฟ. ๐ฅ๐ฒ๐ฎ๐ฑ๐ถ๐ป๐ด ๐ถ๐ ๐ต๐ฎ๐ ๐ฎ๐น๐๐ฎ๐๐ ๐ฏ๐ฒ๐ฒ๐ป ๐ฎ ๐บ๐ฎ๐๐๐ฒ๐ฟ ๐ผ๐ณ ๐๐๐ฟ๐๐ถ๐๐ฎ๐น. But what happens when the grammar of that language changes? Weather
- ๐ค Every breakthrough in hashtag#AI begins when the way it learns changes. We have moved from rules to data to prediction. What comes next? Join us today to explore what ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ณ๐ฟ๐ผ๐บ ๐ฒ๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ means for #translation AI and introduce the next chapter of
- AI can beat any human at chess, but it still can't fix a leaking pipe. For Alexander Waibel, professor at @CarnegieMellon and the Karlsruhe Institute of Technology (KIT), that gap reveals more about intelligence than any benchmark ever could. For Waibel, intelligence goes far
- ๐ย ๐ข๐ฝ๐ฎ๐ฐ๐ถ๐๐ ๐๐ฎ๐ (๐ฏ๐ฐ๐ถ๐ฏ) The hidden cost of building an AI workflow from multiple models, providers, and integrations that you cannot fully see or control. Symptoms include: - Unexpected model changes. - Inconsistent quality. - Unclear accountability. - More time

