emergent behaviour

Why are drug induced hallucinations so compelling that they apparently provided much of the inspiration for early forms of abstract art? Researchers suggest that the answer hinges on an interplay between the mathematics of pattern formation and a mechanism that generates a sense of value and meaning.

To understand how spacetime might have emerged in the early cosmos we need to heat up the equations, and thaw the space and time dimensions.

On the face of it the Universe is a fairly complex place. But could mathematics ultimately lead to a simple description of it? In fact, should simplicity be a defining feature of a "theory of everything"? We ponder the answers.

A traditional view of science holds that every system — including ourselves — is no more than the sum of its parts. To understand it, all you have to do is take it apart and see what's happening to the smallest constituents. But the mathematician and cosmologist George Ellis disagrees. He believes that complexity can arise from simple components and physical effects can have non-physical causes, opening a door for our free will to make a difference in a physical world.

One of the amazing things about life is its sheer complexity. How can a bunch of mindless cells combine to form something as complex as the human brain, or as delicate, beautiful and highly organised as the patterns on a butterfly's wing? Maths has some surprising answers you can explore yourself with this interactive activity.

Researchers have unveiled the first prototypes of robots that can develop emotions and express them too. If you treat these robots well, they'll form an attachment to you, looking for hugs when they feel sad and responding to reassuring strokes when they are distressed. But how do you get emotions into machines that only understand the language of maths?
How does complexity arise from simplicity?
If you've ever watched a flock of birds flying at dusk, or a school of fish reacting to a predator, you'll have been amazed by their perfectly choreographed moves. Yet, complex as this behaviour may seem, it's not all that hard to model it on a computer. Lewis Dartnell presents a hands-on guide for creating your own simulations — no previous experience necessary.
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