Executive Briefing: How Microsoft, Bayer, and Discovery Use AI on the Data You Can't Upload

Large companies are investing in fine-tuning AI models on their proprietary data to handle sensitive information that cannot be shared with public model providers. Bayer and Discovery Bank have successfully implemented this by training specialized models on their specific terminology, rules, and examples, leading to significant improvements in response times and accuracy. Tools like LM Studio also enable individuals to process sensitive documents offline on their own devices, offering a localized solution for secure AI-driven analysis.

• Companies like Bayer and Discovery Bank are fine-tuning AI models on proprietary data to avoid sending sensitive files to external providers.
• Bayer uses a fine-tuned Microsoft Phi model for crop-protection queries, reducing resolution time from days/weeks to seconds.
• Discovery Bank fine-tuned Azure OpenAI models for tasks like understanding financial language and generating SQL, decreasing response times.
• Fine-tuned models remain exclusive to the customer and are not used to improve general foundation models without permission.
• LM Studio allows users to run AI models locally on their laptops, disconnecting from the internet to process sensitive documents securely.
• Local AI testing helps determine which jobs fit on a laptop and when an enterprise system is required.
• The article will cover offline setup, a model leak incident, the limits of laptop AI, and Microsoft’s approach to model dependency.
• The goal is to enable secure AI processing of sensitive files, starting with individual documents and scaling to enterprise solutions.
  Continue reading https://natesnewsletter.substack.com/p/run-ai-offline-private-files