Scaling Foundation Models: Paving the Future Path for Enterprise AI

Abstract

Modern AI models are adept at learning from vast amounts of data, offering innovative solutions to intricate problems. Nevertheless, constructing these systems typically demands a substantial investment of time and a copious volume of data. The forthcoming evolution of AI introduces a paradigm shift, replacing task-specific models with versatile foundation models, which are trained on extensive unlabeled datasets and require minimal fine-tuning for various applications. These foundation models serve as the underpinning for many AI use cases. These models can effectively apply their generalized knowledge to specific tasks by employing self-supervised learning and fine-tuning techniques. Foundation models are revolutionizing the adoption of AI within the business sector. Drastically reducing the labor-intensive tasks of data labeling and model programming will make it significantly more accessible for businesses to integrate AI into a wide array of mission-critical scenarios. This presentation will shed light on our strategy for extending the reach of foundation models to the enterprise, all within a seamlessly integrated hybrid-cloud environment. It will also showcase IBM Research's approach and vision to developing software, middleware, and hardware that facilitates frictionless, cloud-native development while harnessing the potential of foundation models for enterprise AI, focusing on practical industry use cases. The talk will also highlight upcoming research trends and offer insights into the future directions of foundation models.