weidlich@LUDWIG.SCC.COM (Bob Weidlich) (01/11/88)
A PROPOSAL TO THE NEURAL NETWORK RESEARCH COMMUNITY
TO BUILD A
MULTI-MODELED LAYERED NEURAL NETWORK SIMULATOR TOOL SET (MLNS)
Robert Weidlich
Contel Federal Systems
January 11, 1988
The technology of neural networks is in its infancy. Like all other major new
technologies at that stage, the development of neural networks is slowed by
many impediments along the road to realizing its potential to solve many sig-
nificant real world problems. A common assumption of those on the periphery
of neural network research is that the major factor holding back progress is
the lack of hardware architectures designed specifically to implement neural
networks. But those of us who use neural networks on a day to day basis real-
ize that a much more immediate problem is the lack of sufficiently powerful
neural network models. The pace of progress in the technology will be deter-
mined by the evolution of existing models such as Back Propagation, Hopfield,
and ART, as well as the development of completely new models.
But there is yet another significant problem that inhibits the evolution of
those models: lack of powerful-yet-easy-to-use, standardized, reasonably-
priced toolsets. We spend months of time building our own computer simula-
tors, or we spend a lot of money on the meager offerings of the marketplace;
in either case we find we spend more time building implementations of the
models than applying those models to our applications. And those who lack
sophisticated computer programming skills are cut out altogether.
I propose to the neural network research community that we initiate an
endeavor to build a suite of neural network simulation tools for the public
domain. The team will hopefully be composed of a cross-section of industry,
academic institutions, and government, and will use computer networks, pri-
marily Arpanet, as its communications medium. The tool set, hereinafter
referred to as the MLNS, will ultimately implement all of the significant
neural network models, and run on a broad range of computers.
These are the basic goals of this endeavor.
1. Facilitate the growth and evolution of neural network technology by
building a set of powerful yet simple to use neural network simula-
tion tools for the research community.
2. Promote standardization in neural network tools.
3. Open up neural network technology to those with limited computer
expertise by providing powerful tools with sophisticated graphical
user interfaces. Open up neural network technology to those with
limited budgets.
4. Since we expect neural network models to evolve rapidly, update the
tools to keep up with that evolution.
This announcement is a condensation of a couple of papers I have written
describing this proposed effort. I describe how to get copies of those docu-
ments and get involved in the project, at the end of this announcement.
The MLNS tool will be distinctive in that will incorporate a layered approach
to its architecture, thus allowing several levels of abstraction. In a sense,
it is a really a suite of neural net tools, one operating atop the other,
rather than a single tool. The upper layers enable users to build sophisti-
cated applications of neural networks which provide simple user interfaces,
and hide much of the complexity of the tool from the user.
This tool will implement as many significant neural network models (i.e., Back
Propagation, Hopfield, ART, etc.) as is feasible to build. The first release
will probably cover only 3 or 4 of the more popular models. We will take an
iterative approach to building the tool and we will make extensive use of
rapid prototyping.
I am asking for volunteers to help build the tool. We will rely on computer
networks, primarily Arpanet and those networks with gateways on Arpanet, to
provide our communications utility. We will need a variety of skills - pro-
grammers (much of it will be written in C), neural network "experts", and
reviewers. Please do not be reluctant to help out just because you feel
you're not quite experienced enough; my major motivation for initiating this
project is to round-out my own neural networking experience. We also need
potential users who feel they have a pretty good feel for what is necessary
and desirable in a good neural network tool set.
The tool set will be 100% public domain; it will not be the property of, or
copyrighted by my company (Contel Federal Systems) or any other organization,
except for a possible future non-commercial organization that we may want to
set up to support the tool set.
If you are interested in getting involved as a designer, an advisor, a poten-
tial user, or if you're just curious about what's going on, the next step is
to download the files in which I describe this project in detail. You can do
this by ftp file transfer and an anonymous user. To do that, take the follow-
ing steps:
1. Set up an ftp session with my host:
"ftp ludwig.scc.com"
(Note: this is an arpanet address. If you are
on a network other than arpanet with a gateway
to arpanet, you may need a modified address
specification. Consult your local comm network
guru if you need help.)
[Note: FTP generally does not work across gateways. -- KIL]
2. Login with the user name "anonymous"
3. Use the password "guest"
4. Download the pertinent files:
"get READ.ME" (the current status of the files)
"get mlns_spec.doc (the specification for the MLNS)
"get mlns_prop.doc (the long version of the proposal)
If for any reason you cannot download the files, then call or write me the
following address:
Robert Weidlich
Mail Stop P/530
Contel Federal Systems
12015 Lee Jackson Highway
Fairfax, Virginia 22033
(703) 359-7585 (or) (703) 359-7847
(leave a message if I am not available)
ARPA: weidlich@ludwig.scc.com