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MIT spinoff Liquid debuts non-transformer AI models and they’re already state-of-the-art
The startup from MIT's CSAIL says its Liquid Foundation Models have smaller memory needs thanks to a post-transformer architecture.
The models are engineered to be competitive not only on raw performance benchmarks but also in terms of operational efficiency, making them ideal for a variety of use cases, from enterprise-level applications specifically in the fields of financial services, biotechnology, and consumer electronics, to deployment on edge devices. Unlike traditional deep learning models, which require thousands of neurons to perform complex tasks, LNNs demonstrated that fewer neurons—combined with innovative mathematical formulations—could achieve the same results. Labonne noted that while things are “not perfect,” the feedback received during this phase will help the team refine their offerings in preparation for a full launch event on October 23, 2024, at MIT’s Kresge Auditorium in Cambridge, MA.
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