Integrating State Space Models (SSM) into AI foundation models such as GPT-4 offers a unique opportunity to foster synergies between control theory and artificial intelligence. This paper reviews past developments and recent applications, identifying successful integrations with deep learning to advance the learning of sequences and representations in models. Special focus is placed on examining model efficiency and comparing traditional Transformer architectures.

Key Highlights:

• Application of SSM in foundation models for sequence learning.
• Comparative analysis across SSM and Transformer models.
• Potential for new developments in the integration of control theory and AI.
• Reflection on how SSM can enhance model performance and design.

SSMs bring a valuable control theoretic perspective to the field of artificial intelligence, promising enhanced innovation and efficiency in machine learning models.