Chatbots Data Discovery Generative AI 
IBM Journey to AI blog
AUGUST 23, 2023
Risks of training LLM models on sensitive data Large language models can be trained on proprietary data to fulfill specific enterprise use cases. For example, a company could take ChatGPT and create a private model that is trained on the company’s CRM sales data. Can you prove if the model is somehow copying your work?
IBM Journey to AI blog
JULY 3, 2024
By 2026, over 80% of enterprises will deploy AI APIs or generative AI applications. AI models and the data on which they’re trained and fine-tuned can elevate applications from generic to impactful, offering tangible value to customers and businesses.
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ML @ CMU
MARCH 24, 2023
Figure 1: Behavior-driven AI development centers model iteration on evaluating and improving specific real-world use cases. It has never been easier to prototype AI-driven systems. While chatbot A might sound more human-like, a practitioner will deploy chatbot B if it produces concise and accurate answers that customers prefer.
The MLOps Blog
JUNE 5, 2023
The topic of this conversation, obviously, is to dive a little bit into GPT-3 and language models; there’s all this hype now about Generative AI. Speaking of the Generative AI space, the core focus of this episode would be the GPT-3, but could you share a bit more about what GPT-3 means and just give a background there?
NOVEMBER 27, 2024
A Streamlit application is hosted in Amazon Elastic Container Service (Amazon ECS) as a task, which provides a chatbot UI for users to submit queries against the knowledge base in Amazon Bedrock. The table only exists in the Data Catalog. This powerful solution opens up exciting possibilities for enterprise data discovery and insights.
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