Training Foundation Models is expensive. Techniques like Prompt Engineering address this by freezing the models and providing context in prompts to optimize results at the expense of losing perform...

Foundation Models are the foundation for different AI downstream tasks. To leverage these generic models for specific tasks, prompt engineering is a technique to optimize the results without having...

While there are several playgrounds to try Foundation Models, sometimes I prefer running everything locally during development and for early trial and error experimentations. This post explains how...

Training foundation models and even fine-tuning models for custom domains is expensive and requires lots of resources. To avoid changing the pretrained models, a new more resource-efficient techniq...

Generative foundation models like ChatGPT can handle fluent conversations pretty well. IBM Watson Assistant can be extended with NeuralSeek to search and provide answers from enterprise knowledge b...

Watson Natural Language Processing Library for Embed is a containerized library designed to empower IBM partners with greater flexibility to infuse powerful natural language AI into their solutions...

Watson Natural Language Processing Library for Embed is a containerized library designed to empower IBM partners with greater flexibility to infuse powerful natural language AI into their solutions...

Developing high quality software is not trivial and not cheap. To help developers, there are several attempts to leverage AI and special foundation models optimized for source code. This post gives...

The speed in which AI technologies are progressing is amazing. The post below summarizes how I explain my children how AI has evolved over the last years and why many experts consider the new found...

Sriram Raghavan, Vice President of IBM Research AI, talks in the video below about how IBM uses Foundation Models and shares his thoughts and ideas how foundation models will be operationalized. I...