Research to merge human brain cells with AI secures national defence funding
Summary
Monash University-led research into growing human brain cells onto silicon chips, with new continual learning capabilities to transform machine learning, has been awarded almost $600,000 AUD in the prestigious National Intelligence and Security Discovery Research Grants Program.The new research program, led by Associate Professor Adeel Razi, from the Turner Institute for Brain and Mental Health, in collaboration with Melbourne start-up Cortical Labs, involves growing around 800,000 brain cells living in a dish, which are then taught to perform goal-directed tasks. Last year the brain cells ability to perform a simple tennis-like computer game, Pong, received global attention for the teams research.According to Associate Professor Razi, the research programs 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 project garnered funding from the prestigious Australian grant body because the new generation of applications of machine learning, such as self-driving cars and trucks, autonomous drones, delivery robots, intelligent hand-held and wearable devices, will require a new type of machine intelligence that is able to learn throughout its lifetime, Associate Professor Razi said.This continual lifelong learning means machines can acquire new skills without compromising old ones, adapt to changes, and apply previously learned knowledge to new tasksall while conserving limited resources such as computing power, memory and energy. Current AI cannot do this and suffers from catastrophic forgetting.The projects aim is to grow human brain cells in a laboratory dish, called the DishBrain system, to understand the various biological mechanisms that underlie lifelong continual learning.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.