Mr. McNamara said that within just two decades, technology may have advanced so much that humans and machines are effectively ‘melded’ together, allowing for huge leaps forward in human consciousness and cognition.
“We may see AI nano-machines being injected into our bodies,” he told Peers. “These will provide huge medical benefits, such as being able to repair damage to cells, muscles, and bones – perhaps even augment them.
“Beyond this, utilising technology which is already being explored today we see the creation of technology that can meld the biological with the technological, and so be able to enhance human cognitive capability directly, potentially offering greatly improved mental, as well as being able to utilise vast quantities of computing power to augment our own thought processes.
“ Using this technology, embedded in ourselves and in our surroundings, we will begin to be able to control our environment with thought and gestures alone.”
Mr. McNamara also predicted ‘Political Avatars’ which will scour all available data from news sites and government debates to provide people with a recommendation on who to vote for and why based on their worldview.
However, he also warned that the rise of AI could bring ‘huge disruption’ to those working in the retail and service sectors and spark widespread unemployment.
“Whereas today, being poor means being unable to afford the latest smartphone, tomorrow this could mean the difference between one group of people potentially having an extraordinary uplift in physical ability, cognitive ability, health, lifespan and another much wider group that do not,” said Mr. McNamara.
In separate evidence to the committee, Noel Sharkey, Emeritus Professor of AI and Robotics, University of Sheffield, who is now a director at the Foundation for Responsible Robotics, said artificial intelligence comes with a cost.
“The immediate concern is that by ceding decisions or control to machines, the humans start accepting their decisions as correct or better than their own and stop paying attention,” he said.
“There is a growing body of evidence that the learning machine decision makers are inheriting many invisible biases among their correlations.”