Alex Ratner (@ajratner, CEO @SnorkelAI) talks about labeling and training of LLMs. We go over Foundational Models and how to take an “off the shelf” model and fine-tune it for private use.

SHOW: 755

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SHOW NOTES:

Topic 1 - Welcome back to the show. We last spoke two years ago. A lot has changed so we thought it would be a great time to talk about updates. For those that aren’t familiar, give everyone a quick background.

Topic 2 - Let’s start with when we last spoke. We talked a lot about data scientists and how data labeling works for training LLM’s. For those that aren’t familiar, can you give everyone a quick intro to data labeling and why it is important for training?

Topic 3 - When we last spoke, LLM weren't as mainstream as they are today. How has this impacted how you think about AI/ML in general? What are the big challenges for LLM today that you see?

Topic 4 - Many organizations are trying “off the shelf” models but this may or may not be a good idea. On one side they don’t have to build a model but on the other they still have to fine tune it to their specific needs and use case. What are your recommendations for organizations to get started and be as effective as possible?

Topic 5 - In addition to organization specific models, are teams building purpose built models for specific functions (i.e. are they running multiple models each with a different task?) How does labeling and training come into play here?

Topic 6 - Another challenge I’m seeing is security and data privacy concerns. What do folks getting started need to be aware of to make sure company data is safe?

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Training and Labeling Foundational AI Models

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