There is presently great interest in the abilities of neural networks to mimic "qualitative reasoning" by manipulating neural incodings of symbols. Less work has been performed on using neural networks to process floating point numbers and it is sometimes stated that neural networks are somehow inherently inaccu(cid:173) rate and therefore best suited for "fuzzy" qualitative reasoning. Nevertheless, the potential speed of massively parallel operations make neural net "number crunching" an interesting topic to explore. In this paper we discuss some of our work in which we demonstrate that for certain applications neural networks can achieve significantly higher numerical accuracy than more conventional tech(cid:173) niques. In particular, prediction of future values of a chaotic time series can be performed with exceptionally high accuracy.

If research and advisory firm Gartner Inc. is right in its forecast, Artificial Intelligence (AI) technologies will become pervasive in almost every new software product and service by the year 2020. The growth in AI, broadly a set of computational technologies and methodologies aimed at helping machines emulate human intelligence, is being driven primarily by sophisticated algorithms, the availability of huge data sets, greater computing power, and advances in machine learning as well as deep learning. Machine learning, a subset of AI, is broadly about teaching a computer how to spot patterns and use mountains of data to make connections without any programming to accomplish the specific task. A recommendation engine is a good example. Deep learning, an advanced machine learning technique, uses layered (hence "deep") neural networks (neural nets) that are loosely modelled on the human brain.