Cupertino, CA, December 15, 2023 — A breakthrough in artificial intelligence (AI), achieved by Aizip, in collaboration with scientists from Massachusetts Institute of Technology (MIT), University of California, Berkeley (UC Berkeley), University of California, San Diego (UC San Diego), and University of California, Davis (UC Davis), demonstrates a fully automated AI-design pipeline, Aizipline, from data generation to model design to model testing, which involves a new application of the foundation AI models: to develop other AI models.
AI Designs AI: For the first time, we can achieve a full pipeline to use AI tools to design AI models.
Everyday Objects Enhanced: Common items become intelligent entities with ultra-efficient AI models, which are produced by AI design automation.
Practical and Personalized: TinyML and edge AI technology offer tailored experiences, from footwear to home appliances.
Transforming Home and Lifestyle: Smart technology integration reshapes efficiency and sustainability.
Envision a future where shoes don’t just fit but learn and adapt to your walk, adjusting to your body and environment. This AI innovation extends beyond footwear, promising transformative changes in items as diverse as toothbrushes and home security systems, each becoming a smart, evolving, and adapting companion.
In the future, trillions of sensors will be deployed into everything in the world. With AI design automation, we can enable artificial intelligence in anything, anywhere, and at any time. The ultimate goal of this AI design automation paradigm is to create an “AI Nanofactory,” where millions of specialized, efficient AI models can be generated with minimal human interaction. This technology will power the future of pervasive AI.
This development is more than a technological leap; it represents the dawn of a new era in which every item can become a smart, evolving, and adapting companion. The practical applications of this technology are vast, potentially touching every aspect of our lives, from comfort and convenience to efficiency and sustainability. Particularly transformative for smart home technology, this AI enables devices to become more intuitive and responsive to users’ needs.
The collaboration has produced a detailed white paper offering insights into the future of AI and its practical applications, inviting the world to explore the potential of this transformative technology.
– Yubei Chen, Ph.D., CTO of Aizip and Professor at UC Davis: “We’re at the forefront of transforming AI design. Our commitment is not just to speed up and refine the process but also to tackle the big questions in AI. Our ultimate goal of this AI-design automation paradigm is to create an “AI Nanofactory” where millions of specialized, efficient AI models can be generated with minimal human intervention to power the future of pervasive AI.”
– Yan Sun, Ph.D., Chairman and CEO of Aizip: “Our breakthrough in AI model development and deployment is pivotal, marking a significant stride towards pervasive AI.”
– Bruno Olshausen, Ph.D., Professor at UC Berkeley: “Nature evolved intelligent systems that operate with remarkable efficiency. Just as the tiny brains of small animals with fewer than a million neurons must utilize efficient wiring and neural algorithms to perceive and act in the world, the tiny AI systems powering tomorrow’s edge computing devices will require clever, efficient solutions to operate with minimal power and footprint. There is much progress to be made in this direction, and Aizip has taken an important step in leading the way.”
– Gert Cauwenberghs, Ph.D., Professor at UC San Diego:
“We’re witnessing a revolution in human-machine interaction and brain-computer interfaces fueled by advances in brain- and body-sensing technology. Making sense of the massive data streaming from these sensors despite the high levels of variability and noise in their biological operating environments is a major challenge that calls for powerful AI, down to the physiological interface.” “Brain and body sensing in a wearable format requires efficient AI models that can be deployed at the edge. The technology at Aizip enables transformative applications in bio- and neuro-engineering.”
– Boltzmann Li, Principal Machine Learning Architect at Aizip:
“Our fully automated keyword spotting (KWS) design pipeline showcases our AI design automation capabilities.”
– Brian Cheung, AI scientist at MIT and Chief Scientist of Aizip: “With the help of large foundation models, small models will evolve faster than big ones, so the trend of improvements favors the edge. This is a space where Aizip has been an early adopter, and Aizip now leads in leveraging the big to dramatically improve the small”
This announcement marks a significant moment in AI technology, signaling a shift towards more intelligent and responsive environments, both at home and beyond.