Efficient and scalable bio-integrated AI system using brain organoids

This technology is a computing system that integrates brain organoids with CMOS interfaces to enhance artificial intelligence (AI) scalability and efficiency.

Unmet Need: Scalable, energy-efficient AI that mimics human brain processes

The current standard in AI systems, based on artificial neural networks, faces significant shortcomings due to its heavy memory usage and energy consumption, making scalability a challenge. These systems also do not closely mimic the complex computational processes of the human brain. Addressing these limitations is critical as it allows for the development of more sustainable and efficient AI models that can handle the increasing volume of global data, while potentially offering a computational approach that more accurately reflects human cognitive processes.

The Technology: Bio-integrated AI with enhanced efficiency and scalable network capabilities

This technology integrates biological components into computing systems by employing brain organoids derived from induced pluripotent stem cells, coupled with high-resolution electrophysiological CMOS interfaces. These organoids serve as dynamic, high-dimensional reservoirs for information processing, utilizing principles of reservoir computing to bypass extensive training required by traditional artificial neural networks. The approach reduces operational voltage and enhances energy efficiency while enabling more complex and adaptable interconnection networks. This hybrid model offers significant improvements in scalability and efficiency for AI systems and data centers, compared to existing solid-state systems.

Applications:

  • Autonomous system processing
  • Adaptive AI learning systems
  • Medical diagnostics and disease modeling
  • Decision-making process in robotics
  • Cognitive science simulation tools
  • Neural network architectures
  • Research tool for neuroscience and brain mapping

Advantages:

  • Enhanced scalability
  • Cost and energy effective
  • Accurate mimicry of human brain processes
  • Faster processing speeds with minimal training
  • Computing performed both biologically and in silico

Lead Inventor:

Kenneth Shepard, Ph.D.

Patent Information:

Patent Pending

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