Foundation Models: Driving Enterprise Value with AI

💡Foundation models are a type of large language model that can be used for a wide range of tasks and applications.

🔑Foundation models have been trained on massive amounts of unstructured data in an unsupervised manner.

💼Foundation models offer advantages in terms of performance and productivity gains compared to traditional AI models.

⚖️Foundation models face challenges related to compute cost and trustworthiness, especially in terms of biased or toxic data.

🌐Foundation models can be applied to various domains beyond language, such as vision, code, chemistry, and climate research.

What are foundation models?

—Foundation models are large language models that have been trained on massive amounts of unstructured data and can be used for various tasks across different domains.

What are the advantages of foundation models?

—Foundation models offer superior performance and productivity gains compared to traditional AI models, thanks to their extensive pre-training on large amounts of data.

What are the challenges associated with foundation models?

—Foundation models can be computationally expensive to train and run. Additionally, the trustworthiness of these models is a concern due to the potential biases and toxic information contained in the training data.

What other domains can foundation models be applied to?

—Foundation models can be applied to domains beyond language, including vision, code, chemistry, climate research, and more.

How is IBM working to improve foundation models?

—IBM is innovating to enhance the efficiency and trustworthiness of foundation models, making them more relevant and reliable in a business setting.

00:00Large language models, known as foundation models, are making a significant impact in various applications.

01:08Foundation models differ from traditional AI models by providing a foundational capability to drive multiple use cases and applications.

01:38Foundation models derive their power from extensive training on terabytes of unstructured data in an unsupervised manner.

04:17Advantages of foundation models include superior performance and productivity gains compared to models trained on limited data.

06:34Compute cost and trustworthiness are notable challenges associated with foundation models.

07:23Foundation models can be applied to various domains beyond language, such as vision, code, chemistry, and climate research.

08:26IBM is actively working on enhancing the efficiency and trustworthiness of foundation models.

08:39To learn more about IBM's efforts in improving foundation models, check out the provided links.