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Contenido proporcionado por Machine Learning Street Talk (MLST). Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Machine Learning Street Talk (MLST) o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
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Cohere co-founder Nick Frosst on building LLM apps for business

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Manage episode 423903540 series 2803422
Contenido proporcionado por Machine Learning Street Talk (MLST). Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Machine Learning Street Talk (MLST) o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Nick Frosst, co-founder of Cohere, on the future of LLMs, and AGI. Learn how Cohere is solving real problems for business with their new AI models.

This is the first podcast from our new Cohere partnership!

Nick talks about his journey at Google Brain, working with AI legends like Geoff Hinton, and the amazing things his company, Cohere, is doing. From creating the must useful language models for businesses to making tools for developers, Nick shares a lot of interesting insights. He even talks about his band, Good Kid! Nick said that RAG is one of the best features of Cohere's new Command R* models. We are about to release a deep-dive on RAG with Patrick Lewis from Cohere, keep an eye out for that - he explains why their models are specifically optimised for RAG use cases.

Learn more about Cohere Command R* models here:

https://cohere.com/commandhttps://github.com/cohere-ai/cohere-toolkit

Nick's band Good Kid:

https://goodkidofficial.com/

Nick on Twitter:

https://x.com/nickfrosst

Disclaimer: We are in a partnership with Cohere to release content for them. We were not told what to say in the interview, and didn't edit anything out from the interview. We are currently planning to release 2 shows per month under the partnership about their AI platform, research and strategy.

  continue reading

191 episodios

Artwork
iconCompartir
 
Manage episode 423903540 series 2803422
Contenido proporcionado por Machine Learning Street Talk (MLST). Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Machine Learning Street Talk (MLST) o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Nick Frosst, co-founder of Cohere, on the future of LLMs, and AGI. Learn how Cohere is solving real problems for business with their new AI models.

This is the first podcast from our new Cohere partnership!

Nick talks about his journey at Google Brain, working with AI legends like Geoff Hinton, and the amazing things his company, Cohere, is doing. From creating the must useful language models for businesses to making tools for developers, Nick shares a lot of interesting insights. He even talks about his band, Good Kid! Nick said that RAG is one of the best features of Cohere's new Command R* models. We are about to release a deep-dive on RAG with Patrick Lewis from Cohere, keep an eye out for that - he explains why their models are specifically optimised for RAG use cases.

Learn more about Cohere Command R* models here:

https://cohere.com/commandhttps://github.com/cohere-ai/cohere-toolkit

Nick's band Good Kid:

https://goodkidofficial.com/

Nick on Twitter:

https://x.com/nickfrosst

Disclaimer: We are in a partnership with Cohere to release content for them. We were not told what to say in the interview, and didn't edit anything out from the interview. We are currently planning to release 2 shows per month under the partnership about their AI platform, research and strategy.

  continue reading

191 episodios

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