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[00:06:47] Building GPU data centers in 2017
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Classroom Contents
How DeepL Built a Translation Powerhouse with AI
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- 1 [00:00:00] Introducing Jarek and DeepL’s mission
- 2 [00:01:46] Competing with Google Translate & LLMs
- 3 [00:04:14] Pretraining vs. proprietary model strategy
- 4 [00:06:47] Building GPU data centers in 2017
- 5 [00:08:09] The value of curated bilingual and monolingual data
- 6 [00:09:30] How DeepL measures translation quality
- 7 [00:12:27] Personalization and enterprise-specific tuning
- 8 [00:14:04] Why translation demand is growing
- 9 [00:16:16] ROI of incremental quality gains
- 10 [00:18:20] The role of human translators in the future
- 11 [00:22:48] Hallucinations in translation models
- 12 [00:24:05] DeepL’s work on speech translation
- 13 [00:28:22] The broader impact of global communication
- 14 [00:30:32] Handling smaller languages and language pairs
- 15 [00:32:25] Multi-language model consolidation
- 16 [00:35:28] Engineering infrastructure for large-scale inference
- 17 [00:39:23] Adapting to evolving LLM landscape & enterprise needs
