That chess match and all its implications raised profound questions about neural networks. Many saw it as evidence that true artificial intelligence had finally been achieved. After all, “a man was beaten by a computer in a game of wits.” But it is one thing to program a computer to solve the kind of complex mathematical problems found in chess. It is quite another for a computer to make logical deductions and decisions on its own.

Using neural networks, to emulate brain function, provides many positive properties including parallel functioning, relatively quick realisation of complicated tasks, distributed information, weak computation changes due to network damage (Phineas Cage), as well as learning abilities, i.e. adaptation upon changes in environment and improvement based on experience. These beneficial properties of neural networks have inspired many scientists to propose them as a solution for most problems, so with a sufficiently large network and adequate training, the networks could accomplish many arbitrary tasks, without knowing a detailed mathematical algorithm of the problem. Currently, the remarkable ability of neural networks is best demonstrated by the ability of Honda's Asimo humanoid robot that cannot just walk and dance, but even ride a bicycle. Asimo, an acronym for Advanced Step in Innovative Mobility, has 16 flexible joints, requiring a four-processor computer to control its movement and balance. Its exceptional human-like mobility, are only possible because the neural networks that are connected to the robot's motion and positional sensors and control its 'muscle' actuators are capable of being 'taught' to do a particular activity.

The significance of this sort of robot motion control is the virtual impossibility of a programmer being able to actually create a set of detailed instructions for walking or riding a bicycle, instructions which could then be built into a control program. The learning ability of the neural network overcomes the need to precisely define these instructions. However, despite the impressive performance of the neural networks, Asimo still cannot think for itself and its behaviour is still firmly anchored on the lower-end of the intelligent spectrum, such as reaction and regulation.

Neural networks are slowly finding there way into the commercial world. Recently, Siemens launched a new fire detector that uses a number of different sensors and a neural network to determine whether the combination of sensor readings are from a fire or just part of the normal room environment such as dust. Over fifty percent of fire call-outs are false and of these well over half are due to fire detectors being triggered by everyday activities as opposed to actual fires, so this is clearly a beneficial use of the paradigm.