Rafie is back! In this episode, we delve into Multilingual AI and AI-enabled translation services, machine learning translation techniques, and we discuss and compare translation tools such as Google AutoML, GPT-4, Marian NMT, DeepL, Welocalize, Llama 2, Mistral and BLOOM
Furthermore, we discuss the potential and impact of multilingual AI for global startups (the andreesen horowitz view is that the company of the future is 'default global').

06:00 - Machine Translation vs LLMs
16:10 - Where GPT-4 and LLMs perform badly
29:10 - Mistral Model Weights Leak

We discuss: Multilingual AI, Machine Translation, Parallel Data, Marian NMT, DeepL, Welocalize, WMT, GPT-4, Translating Prompts, Multilingual Utterances, Localization, Prompt Pipelines, GPT-5, Bloom, Parameters, Mistral, LLMs, MT Engines, Token use

Links:
- https://marian-nmt.github.io/
- https://www.deepl.com/en/translator
- https://www.welocalize.com/do-llms-or-mt-engines-perform-translation-better/
- https://machinetranslate.org/wmt
- https://machinetranslate.org/wmt23#czech--ukrainian
- https://mistral.ai/news/mixtral-of-experts/
- https://cloud.google.com/translate
- https://www.reddit.com/r/MachineLearning/comments/1452ziq/d_llms_in_languages_other_than_english/
- https://instruct-multilingual-frontend-dtjnk4f6ra-ue.a.run.app/
- https://heidloff.net/article/llm-languages-german/
- https://www.theverge.com/2022/11/2/23434360/google-1000-languages-initiative-ai-llm-research-project
- https://www.theverge.com/2022/7/6/23194241/meta-facebook-ai-universal-translation-project-no-language-left-behind-open-source-model

We mentioned as a possible example the idea of translating vs locally-generating a French employment contract in France. This was in relation to our AI legaltech startup https://www.genieai.co

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Thanks,

Alex, Alex and Rafie

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