The assumption that physically interfacing the human brain with machines will give humanity a competitive edge over increasingly advanced robots is doubtful because of various factors, writes Michael Milford, a professor at the Queensland University of Technology in Australia.
He cites the limitations of hardware as one obstacle, although overcoming them will not mitigate a persistent lack of understanding of how innovative deep-learning neural networks function.
The susceptibility of machine-learning technologies to reflect human-like prejudices also has ramifications for how humans might interface with and trust a machine.
"In the long term, the issue is whether, and how, humans will need to be involved in processes that are increasingly determined by machines," Milford says.
He also notes machines themselves could become more advanced via such brain-machine interfaces by having humans "fill in the gaps" for jobs currently beyond the algorithms' capabilities, such as making subtle contextual choices.
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