Exponential Compute Demand

The demand for compute power is skyrocketing, with expectations of continued exponential growth as models improve. Cohere stands out by training models to operate effectively in secure environments, leveraging internal knowledge bases. A notable application of their RAG approach significantly reduced doctors' prep time from 30 minutes to just five, demonstrating AI's potential to enhance patient care seamlessly within existing workflows.

In this clip
From this podcast
Machine Learning Street Talk (MLST)
Bold AI Predictions From Cohere Co-founder
Related Questions
- Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?
- Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?

Exponential Compute Demand

In this clip

From this podcast

Machine Learning Street Talk (MLST)

Bold AI Predictions From Cohere Co-founder

Related Questions

Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?

Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?