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DishBrain project awarded almost $600,000 to pursue lifelong learning for AI

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Funding of nearly $600,000 from the National Intelligence and Security Discovery Research Grants Program has been awarded to Monash University-led research involving growing human brain cells onto silicon chips. 

A statement from Monash University on Friday describes 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 – as involving the growth of about 800,000 brain cells living in a dish, which are then “taught” to perform goal-directed tasks. 

The team’s research, showing the cells’ ability to play the video game Pong and published in the journal Neuron, received global attention last year.

Razi said using cells embedded on chips, “merges the fields of artificial intelligence and synthetic biology to create programmable biological computing platforms. 

“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.”

According to the release, the funding was awarded due to the new generation of applications for machine learning, such as self-driving cars and trucks, autonomous drones, delivery robots, intelligent hand-held and wearable devices, which will require a new variety of machine intelligence able to learn over its lifetime.

Machine learning systems can currently suffer from a compromising of old skills when learning new ones, and struggle to apply previously learned knowledge to new tasks, known as “catastrophic forgetting”.

The aim is to use the “DishBrain system” to understand “the various biological mechanisms that underlie lifelong continual learning.”

“This will help us scale up the hardware and methods capacity to the point where they become a viable replacement for in silico computing,“  added Razi.

Picture: credit Cortical Labs

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