Monash University and Cortical Labs Secures Defence Funding for AI-Human Brain Cell Fusion Research

Monash University and Melbourne start-up Cortical Labs have secured nearly $600,000 AUD from the National Intelligence and Security Discovery Research Grants Program for a project merging human brain cells with AI. Led by Associate Professor Adeel Razi, the research involves growing around 800,000 brain cells on silicon chips and teaching them to perform tasks. The aim is to create a new type of machine intelligence capable of continual lifelong learning, surpassing the performance of existing silicon-based hardware. This could have implications for fields such as robotics, automation, brain-machine interfaces, and drug discovery.

Monash University Research on Merging Human Brain Cells with AI

Monash University is leading a research project that involves growing human brain cells onto silicon chips. This project, which has received nearly $600,000 AUD from the National Intelligence and Security Discovery Research Grants Program, aims to enhance machine learning capabilities. The research is being led by Associate Professor Adeel Razi from the Turner Institute for Brain and Mental Health, in collaboration with Melbourne-based start-up Cortical Labs.

The project involves growing approximately 800,000 brain cells in a dish and then training them to perform goal-directed tasks. Last year, the team’s research gained global attention when the brain cells were able to play a simple computer game similar to tennis, called Pong.

The Intersection of AI and Synthetic Biology

The research program is working on embedding lab-grown brain cells onto silicon chips. This process combines the fields of artificial intelligence and synthetic biology to create programmable biological computing platforms. According to Associate Professor Razi, this new technology may eventually outperform existing, purely silicon-based hardware.

The implications of this research could be significant across various fields, including planning, robotics, advanced automation, brain-machine interfaces, and drug discovery. This could potentially give Australia a strategic advantage.

The Need for Continual Lifelong Learning in AI

The research project received funding from the Australian grant body due to the potential applications of machine learning. These applications include self-driving cars and trucks, autonomous drones, delivery robots, and intelligent handheld and wearable devices. According to Associate Professor Razi, these applications will require a new type of machine intelligence that can learn throughout its lifetime.

This concept of “continual lifelong learning” means that machines can acquire new skills without forgetting old ones, adapt to changes, and apply previously learned knowledge to new tasks, all while conserving limited resources such as computing power, memory, and energy. Current AI technology cannot do this and suffers from “catastrophic forgetting”. In contrast, human brains excel at continual lifelong learning.

The DishBrain System: Growing Human Brain Cells in a Lab

The project’s aim is to grow human brain cells in a laboratory dish, in a system known as the DishBrain system. The goal is to understand the various biological mechanisms that underlie lifelong continual learning.

Developing Better AI Machines

The grant will be used to develop better AI machines that replicate the learning capacity of these biological neural networks. This will help scale up the hardware and methods capacity to the point where they become a viable replacement for in silico computing, according to Associate Professor Razi.

According to Associate Professor Razi, the research program’s work using lab-grown brain cells embedded onto silicon chips, “merges the fields of artificial intelligence and synthetic biology to create programmable biological computing platforms,” he said. “This new technology capability in future may eventually surpass the performance of existing, purely silicon-based hardware. “The outcomes of such research would have significant implications across multiple fields such as, but not limited to, planning, robotics, advanced automation, brain-machine interfaces, and drug discovery, giving Australia a significant strategic advantage.”

“We will be using this grant to develop better AI machines that replicate the learning capacity of these biological neural networks. This will help us scale up the hardware and methods capacity to the point where they become a viable replacement for in silico computing,“ Associate Professor Razi said.

Summary

Monash University has secured nearly $600,000 AUD in funding for a research project that aims to grow human brain cells on silicon chips, merging artificial intelligence and synthetic biology to create programmable biological computing platforms. The project, which could have significant implications for fields such as robotics, automation, and drug discovery, seeks to develop AI machines that can replicate the learning capacity of biological neural networks, overcoming the current limitations of AI in terms of continual lifelong learning.

• The Monash University team, led by Associate Professor Adeel Razi, has secured nearly $600,000 AUD from the National Intelligence and Security Discovery Research Grants Program for a project merging human brain cells with AI.
• The research, in collaboration with Melbourne start-up Cortical Labs, involves growing approximately 800,000 brain cells in a dish and teaching them to perform tasks. The cells’ ability to play a simple computer game gained global attention last year.
• The project uses lab-grown brain cells embedded onto silicon chips, merging artificial intelligence and synthetic biology to create programmable biological computing platforms. This technology could potentially outperform existing silicon-based hardware.
• The research could have significant implications across various fields, including planning, robotics, advanced automation, brain-machine interfaces, and drug discovery.
• The project aims to develop a new type of machine intelligence capable of continual lifelong learning, a feature current AI lacks. This would allow machines to acquire new skills without forgetting old ones, adapt to changes, and apply previously learned knowledge to new tasks while conserving resources.
• The goal is to understand the biological mechanisms that underlie lifelong continual learning by growing human brain cells in a laboratory dish, known as the DishBrain system. This will aid in developing better AI machines that replicate the learning capacity of these biological neural networks.

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