All quantities have the properties of value, significance, and
dimensionality. However, the IEEE floating point standards arbitrarily
discard significance, even though the methodology of manipulating
significance was worked out in 1959. These methods are fairly simple -
in many cases simply omitting the post-normalization operation. However,
this can't be done when the position of the most significant bit is
assumed as in the IEEE standards.

Now, this universal absence of capability plays havoc on financial
predictive applications and some neural network applications, and may be
partially responsible for the present world economic problems via
short-circuiting predictions by losing the computable significance of
results.

I am a co-author of a peer-reviewed paper explaining these issues and
discussing some work-arounds at the upcoming AI conference in Las Vegas.
If you would like an advance draft of this paper, to get your early
comments in on, then please email me and I'll email you one.

It is my hope that something can be worked out to expand the IEEE
standards to incorporate significance, to avoid the ugly prospect of
having to completely replace the standard.

Steve Richfield
408-230-4935