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Scientists Teach Bees How To Play Soccer (smithsonianmag.com)
Clint Perry, a biologist who studies the evolution of cognition in insects at Queen Mary University of London, and his colleagues have released the results of a creative new experiment in which they essentially taught bumblebees how to play "bee soccer." "The insects' ability to grasp this novel task is a big score for insect intelligence, demonstrating that they're even more complex thinkers than we thought," reports Smithsonian. From the report: For the study, published in the February 23 issue of Science, researchers gave a group of bees a novel goal (literally): to move a ball about half their size into a designated target area. The idea was to present them with a task that they would never have encountered in nature. Not only did the bees succeed at this challenge -- earning them a sugary treat -- but they astonished researchers by figuring out how to meet their new goal in several different ways. Some bees succeeded at getting their ball into the goal with no demonstration at all, or by first watching the ball move on its own. But the ones that watched other bees successfully complete the game learned to play more quickly and easily. Most impressively, the insects didn't simply copy each other -- they watched their companions do it, then figured out on their own how to accomplish the task even more efficiently using their own techniques. The results show that bees can master complex, social behaviors without any prior experience -- which could be a boon in a world where they face vast ecological changes and pressures.
@Tashkinov
Khorosho skazano.
The problem is that our so-called 'modern' CPUs can only do exactly what they're programmed to do. Yes, they can perform incredibly complex calculations, such as pattern search and recognition, many orders of magnitude faster than humans. But that's not really the same kind of 'intelligence' that we can clearly see in the behavior of living creatures.
The behavior of CPUs is deterministic, i.e. tend to produce the same output, for a given set of inputs. Biological creatures, OTOH, tend to behave non-deterministically, that is their behaviors, given identical inputs, tend to produce varying sets of output behaviors, with ranges of variances that are difficult to predict.
Nature itself is only partially predictable. (We like to call the part we can't predict "noise".) So the behavior of electro-mechanical robots is very noisy because robots must process noisy sensor data using deterministic methods. Their programs merely react to input, so the humans who write their programs must somehow 'teach' them how to anticipate and react to all possible input scenarios. Which of course is computationally intractable, even for a planet-sized digital computer.
So, mathematically, robots are modeled as servomechanisms, which can operate automatically (more or less) by measuring responses received on their sensors and applying a kind of negative-feedback to reduce the variance of possible behaviors caused by 'recognizing noise' in a non-deterministic world.
We living creatures are much better at this kind of 'automatic behavior' because we are intrinsically non-deterministic machines, whose behaviors don't always 'make sense', but get us, sometimes, to some desired goals, more effectively and efficiently than simple 'random' behavior.
I believe there is a 'Life Principle', which is not yet fully understood, that makes this possible, by imbuing living creatures with mechanisms for consciousness (self-awareness) and motivation (desires and fears). So living creatures tend to have real-time 'situational awareness', which allows them, in effect, to connect to reality and understand and react to the world in terms of their own fears and desires. More or less.
Humans seem to have a lot of this kind of intelligence. Bumblebees not as much. But even the humblest earthworm seems to perceive a buzz of reality which helps them find the dark moist places they love (and avoid the dry, prickly places they fear). Digital computers perform more poorly in these simple reality tasks. (But can compute Pi to a million places easily!)
Will humans be able to build robots with this same kind of Life Principle? I think so, but first we have to study biological life more and actually figure out how it works, up to understanding how consciousness, fear, love and hate operate at a microscopic level.
Currently we can't even draw the simplest circuit diagram for 'consciousness', or build any simple device that 'understands' reality like we (think) we do.