Large language models usually give great answers, but because they're limited to the training data used to create the model, over time they can become incomplete--or worse, generate answers that are just plain wrong. One way of improving the LLM results is called "retrieval-augmented generation" or RAG. In this video, IBM Senior Research Scientist Marina Danilevsky explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its info, lending more credibility to what it generates.
Generative AI has stunned the world with its ability to create realistic images, code, and dialogue. Here, IBM expert Kate Soule explains how a popular form of generative AI, large language models, works and what it can do for enterprise.
Both proprietary and open source LLMs share risks, including inaccuracies, bias, and security concerns. In this video, Master Inventor Martin Keen covers the tradeoffs so you can make an informed decision of which option is best for you.
Get a unique perspective on what the difference is between Machine Learning and Deep Learning - explained and illustrated in a delicious analogy of ordering pizza by IBMer and Master Inventor, Martin Keen.
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