Data Skeptic
I LLM and You Can Too
Dec 23, 2023
The podcast explores the utilization of large language models in daily life and work processes. It discusses the challenges and risks of using them as a service, the concept of retrieval augmented generation, and the use of embeddings and LLMs in text analysis and product development. The podcast also delves into the applications of text embeddings in similarity, search, and classification tasks, while addressing their limitations and potential risks.
23:52
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Quick takeaways
- Cloud platforms like Google Cloud Platform, Azure, or Amazon Web Services provide easy access to large language models via APIs, enabling integration into projects.
- Large language models can enhance traditional search methods through techniques like similarity analysis and classification, using embeddings to map text data in a semantic space.
Deep dives
Using Large Language Models in Everyday Life
Large language models have become an integral part of our daily lives. People can easily access systems like GPT or BARD to ask for advice. While basic usage involves asking simple questions, there are endless possibilities for leveraging large language models. One way is by using them as a data scientist, MLOps engineer, or general engineer to incorporate these technologies into their work. However, training your own large language model can be costly and inaccessible for the average person. Instead, developers can rely on cloud platforms like Google Cloud Platform, Azure, or Amazon Web Services to access large language models via APIs, enabling easy integration into projects.
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