There is a long way from Elon Musk's vision of symbiosis between man and machine, which would require a much more granular understanding of the brain network that goes beyond the basics of motor control to more complex cognitive faculties like language and metaphor.
Professor Panagiotis Artemiadis of Arizona State University has been trying to get more bandwidth using a 128-electrode EEG cap to allow a human to control a swarm of flying robots with their brain.
Humans won't become irrelevant until machines can replicate the human brain something Nicolelis believes is not possible.
Nicolelis argues that the brain contrary to what Musk and Singularity proponents like Ray Kurzweil say is not computable because human consciousness is the result of unpredictable, nonlinear interactions among billions of cells.
He agrees with Musk that if we can interface directly with machines we can produce a "quantum leap" in what digital infrastructure has produced today, but predicts that humans will retain ultimate control.
Under these circumstances human skills diminish and people become subservient to machines.
Better communication between humans and machines, particularly the transmission of emotional signals from humans, will be a powerful tool for building trust in automated systems, added Artemiadis.
"It's about making the machine more intuitive using brain signals to understand whether the human is distracted or tired."
Computer scientists from the Google-owned firm have studied how their AI behaves in social situations by using principles from game theory and social sciences. During the work, they found it is possible for AI to act in an "aggressive manner" when it feels it is going to lose out, but agents will work as a team when there is more to be gained.
For the research, the AI was tested on two games: a fruit gathering game and a Wolfpack hunting game.
These are both basic, 2D games that used AI characters (known as agents) similar to those used in DeepMind's original work with Atari.
Within DeepMind's work, the gathering game saw the systems trained using deep reinforcement learning to collect apples (represented by green pixels).
When a player, or in this case an AI, collected an apple, it was rewarded with a '1' and the apple disappeared from the game's map.
"Intuitively, a defecting policy in this game is one that is aggressive i.e., involving frequent attempts to tag rival players to remove them from the game," the researchers write in their paper.
For a deeper understanding check out Deepminds post: Understanding Agent Cooperation
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