But are there limitations to the capabilities of neural networks or will they be the solution to creating strong-AI? Artificial neural networks are biologically inspired but that does not mean that they are necessarily biologically plausible. Many Scientists have published their thoughts on the intrinsic limitations of using neural networks; one book that received high exposure within the Computer Scientist community in 1969 was Perceptron by Minsky and Papert. Perceptron brought clarity to the limitations of neural networks, although many scientists were aware of limited ability of an incomplex perceptron to classify patterns, Minsky’s and Papert’s approach of finding “what are neural networks good for?” illustrated what is impeding future development of neural networks. Within its time period Perceptron was exceptionally constructive and its identifiable content gave the impetus for later research that conquered some of the depicted computational problems restricting the model. An example is the exclusive-or problem. The exclusive-or problem contains four patterns of two inputs each; a pattern is a positive member of a set if either one of the input bits is on, but not both. Thus, changing the input pattern by one-bit changes the classification of the pattern. This is the simplest example of a linearly inseparable problem. A perceptron using linear threshold functions requires a layer of internal units to solve this problem, and since the connections between the input and internal units could not be trained, a perceptron could not learn this classification. Eventually this restriction was solved by incorporating extra “hidden” layers. Although advances in neural network research have solved many of the limitations identified by Minsky and Papert, numerous still remain such as networks using linear threshold units still violate the limited order constraint when faced with linearly inseparable problems Additionally, the scaling of weights as the size of the problem space increases remains an issue.

It is clear that the dismissive views about neural networks disseminated by Minsky, Papert and many other Computer Scientists have some evidential support, but still many researchers have ignored their claims and refused to abandon this biologically inspired system.

There have been several recent advances in artificial neural networks by integrating other specialised theories into the multi-layered structure in an attempt to improve the system methodology and move one step closer to creating strong-AI. One promising area is the integration of fuzzy logic. invented by Professor Lotfi Zadeh. Other admirable algorithmic ideas include quantum inspired neural networks (QUINNs) and “network cavitations” proposed by S.L.Thaler.

The history of artificial intelligence is replete with theories and failed attempts. It is in inevitable that the discipline will progress with technological and scientific discoveries, but will they ever reach the final hurdle?

Tommy Connolly, Undergraduate at the University of Exeter reading Computer Science