Anton and Ben from NLP Logix discuss how to turn large language models into business value. They debunk misconceptions, highlight the role of data curation and balance, and stress the importance of human oversight for accuracy in generative AI.
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Quick takeaways
Large language models struggle with handling numbers, traditional AI performs better in tasks like weather prediction.
Efficient document processing is crucial to prevent data inaccuracies in generative AI models; leaders should ensure clean data sources for relevance.
Deep dives
Dispelling Myths About Generative AI
Anton and Ben emphasize that generative AI, particularly large language models (LLMs), are not adept at handling numbers and traditional AI performs better in tasks like weather prediction. They highlight that LLMs do not replace existing AI solutions, and organizations must be diligent in processing documents to avoid unclean data that can lead to inaccuracies in AI models.
Balancing Data Concerns and Data Scrubbing
Efficient document processing into AI systems is crucial to prevent data inaccuracies that could lead to misinterpretations within generative AI models. Leaders must also ensure that data sources are clean to maintain relevance in searches and customer inquiries.
Importance of Layering Models and Expert Feedback
Anton suggests using LLMs as foundational models and layering specific models on top to address distinct business needs. Ben advocates for simplicity in AI model development and stresses the significance of having experts involved in the decision-making process to maintain human oversight in final judgments rather than relying solely on AI.
Today’s guests are Anton Kornienko and Ben Webster of NLP Logix, where Anton serves as Data Science Platform Architect and Ben serves as Modeling and Analytics Team Lead. NLP Logix is a fast-growing AI services firm based in Florida that serves both the public and private sectors. Ben and Anton join us on today’s program to offer Emerj’s executive podcast audience advice on the essentials of turning large language models into business value. Throughout the episode, the pair underscore what they see as the biggest misconceptions about LLMs in the media hype cycle and how leaders can find a balance between foundational and bespoke models for different workflows. This episode is sponsored by NLP Logix. Learn how brands work with Emerj and other Emerj Media options at emerj.com/ad1.
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