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MIT Spinoff Liquid AI Secures $37.5M for Liquid Neural Network Advancements

  • MIT spinoff Liquid AI emerges from stealth with $37.5 million seed funding for liquid neural network advancements.
  • Liquid AI focuses on efficient and adaptable liquid neural networks, challenging existing foundation models like GPTs.
  • The funding will support Liquid AI's goal of developing accountable, safe, and efficient liquid foundation models for diverse AI applications.

Liquid AI, an MIT spinoff co-founded by robotics expert Daniela Rus, comes out of stealth with $37.5 million in seed funding. The startup focuses on advancing liquid neural networks, a novel AI model architecture known for its efficiency and adaptability. 

With investors including OSS Capital, Automattic, and Samsung Next, Liquid AI aims to commercialize its technology, competing with prominent foundation model companies and providing AI infrastructure for various applications.

Liquid Neural Networks: Efficient, adaptable, and general-purpose AI

Liquid neural networks, a brainchild of MIT researchers, stand out for their smaller size, requiring less computing power and inherent adaptability. With applications in autonomous navigation, drone search and rescue, and data analysis, liquid neural networks offer interpretability and ease of deployment.

Liquid AI's technology is set to challenge existing foundation models like GPTs, providing accountable, safe, and efficient machine learning models for both domain-specific and generative AI applications.

Funding plans: Building beyond GPTs and expanding the team

The $37.5 million seed funding will support Liquid AI's mission to develop best-in-class liquid foundation models beyond GPTs. The startup plans to expand its team from 12 to 20 members by early next year.

Liquid AI aims to offer a platform allowing customers to build their models for various use cases while prioritizing accountability, safety, and reliability in large AI models.

Edited by Shruti Thapa