"The way in which a system reacts to its environment betrays how much intelligence is built into the system."
Some biomimetic approaches seem to entail building in the kind of intelligence that minimises sensory resources in the interests of greater efficiency and speed:
"To give robots powers we don't have, such as flying, Ayers and his colleagues are copying the electrical activity of insect nervous systems, with the aim of creating an artificial bee that could pollinate plants. Previous attempts to imitate animal movements have relied on building equivalents of specific abilities, like magnetic compasses and air velocity sensors, but such robots are still controlled by computer algorithms. 'The problem with algorithms is that you have to anticipate every possible situation and have a determined escape strategy for each' says Ayers. That's a pain when you're doing something as prone to variation as flying. His team's secret is circuit boards that produce chaotic electrical signals - much as real neurons are thought to - allowing a multitude of possible solutions to be explored on the fly. 'Chaos allows you to explore your full parameter space until you find a solution that allows you to escape' says Ayers. . . . He showed how his team had controlled the flight of a small toy helicopter with a synthetic nervous system."
(New Scientist, "Machines come to life. By building robots using the principles of biology, we can sit back and wait for intelligent behaviour to simply emerge" 18 January 2014)
Is this predictive inference in action, or does it differ from ad hoc behaviour modification in which a range of random predictions are rapidly tested and discarded? In the latter case each prediction - discardment cycle seems to correspond to a biochemical reaction which is easily reversed and therefore thermodynamically efficient.
"The way in which a system reacts to its environment betrays how much intelligence is built into the system."
Some biomimetic approaches seem to entail building in the kind of intelligence that minimises sensory resources in the interests of greater efficiency and speed:
"To give robots powers we don't have, such as flying, Ayers and his colleagues are copying the electrical activity of insect nervous systems, with the aim of creating an artificial bee that could pollinate plants. Previous attempts to imitate animal movements have relied on building equivalents of specific abilities, like magnetic compasses and air velocity sensors, but such robots are still controlled by computer algorithms. 'The problem with algorithms is that you have to anticipate every possible situation and have a determined escape strategy for each' says Ayers. That's a pain when you're doing something as prone to variation as flying. His team's secret is circuit boards that produce chaotic electrical signals - much as real neurons are thought to - allowing a multitude of possible solutions to be explored on the fly. 'Chaos allows you to explore your full parameter space until you find a solution that allows you to escape' says Ayers. . . . He showed how his team had controlled the flight of a small toy helicopter with a synthetic nervous system."
(New Scientist, "Machines come to life. By building robots using the principles of biology, we can sit back and wait for intelligent behaviour to simply emerge" 18 January 2014)
Is this predictive inference in action, or does it differ from ad hoc behaviour modification in which a range of random predictions are rapidly tested and discarded? In the latter case each prediction - discardment cycle seems to correspond to a biochemical reaction which is easily reversed and therefore thermodynamically efficient.
Chris G