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Australian DishBrain researchers receive more than $400K to merge AI with human brain cells

A team of researchers in Australia has been awarded more than $403,000 in federal funding to merge human brain cells with artificial intelligence.

Melbourne's Monash University, which led the research into growing human brain cells on silicon chips, said in a release that the money came from the National Intelligence and Security Discovery Research Grants Program.

The program was led by Turner Institute Associate Professor Adeel Razi and is a collaboration with start-up Cortical Labs. 

It involves growing around 800,000 brain cells living in a dish, which are then "taught" to perform goal-directed tasks. 

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Last year, the cells' ability to play the game Pong while living in a dish received international attention. The scientists published those findings in the journal "Neuron."

"This new technology capability in the future may eventually surpass the performance of existing, purely silicon-based hardware," Razi explained. 

He predicted that the work's outcomes would have "significant implications" across fields, including planning, robotics, advanced automation, brain-machine interfaces and drug discovery.

Razi said this project had received the money because the new generation of applications of machine learning "will require a new type of machine intelligence that is able to learn throughout its lifetime."

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Comparatively, current artificial intelligence tech cannot acquire new skills without compromising old ones, adapt to changes and apply previously learned knowledge to new tasks while conserving limited resources, he noted, and it suffers from "catastrophic forgetting."

Brains, however, excel at "continual lifelong learning."

The researchers aim 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," Razi concluded. 

The modern term "in silico" is usually used to mean experimentation performed by a computer.



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