Makhfi.com  
Fascinating World of Neural Nets  
add to favorites   tell a friend
Home
Introduction to KCM
Why Neural Nets?
Introduction to ANN
ANN in Real World
Tools & Utilities
Blog Articles
Discussion Board
Resources
Experts
News
Events
Contact
NNDef Toolkit
Knowledge Models
nBank Project
 

Why Neural Networks?



That is a valid question. Why Neural Networks?
Neural Network is a fascinating technology, 50 years old, but still not fully employed. And the question is why? Why didn't Neural Network progress as fast as many other technologies?
Let us first take a look back …

The concept of neural networks has been around since the early 1950s, but was mostly dormant until the mid 1980s. One of the first neural networks developed was the perceptron created by a psychologist named Frank Rosenblatt in 1958. The perceptron was a very simple system used to analyze data and visual patterns, which generated a great deal of interest in AI community.

Unfortunately, these earlier successes caused people to exaggerate the potential of neural networks, particularly in light of the limitation in the electronics then available.
Rosenblatt and other scientists claimed that eventually, with enough complexity and speed, the perceptron would be able to solve almost any problem.

In 1969, Marvin Minsky and Seymour Papert of MIT published an influential book, which showed that the perceptron could never solve a class of problems, and hinted at several other fundamental flaws in the model.

Their analysis combined with unfulfilled, outrageous claims convinced the AI community; and the bodies that fund it; of the fruitlessness of pursuing work with neural networks, and the majority of researchers turned away from the approach.

The result was to halt much of the funding and scientists working on neural network type devices found it almost impossible to receive funding.

This period of stunted growth lasted through eighties where several events caused a renewed interest. In 1982 John Hopfield of Caltech presented a paper to the national Academy of Sciences. With clarity and mathematical analysis, he showed how such networks could work and what they could do.

By 1985 the American Institute of Physics began what has become an annual meeting of Neural Networks for Computing. By 1987, the Institute of Electrical and Electronic Engineer's (IEEE) first International Conference on Neural Networks drew more than 1,800 attendees.

And the 1990 US Department of Defense Small Business Innovation Research Program named 16 topics, which specifically targeted neural networks.

By then, the wheel turned again and growth started, but not with the pace that one would wish to see. Over shadowed by Internet explosion, processing limitations also contributed to the slow growth.

In meantime Internet hype has settled down and processing power is no showstopper anymore. Computerization of business and personal transactions generate the flood of data that would certainly contribute to machine learning and other modern data analysis methods.

Thanks to the availability of cheap microprocessors and recent discoveries about DNA and human brain, artificial intelligence has gone from being a fantasy to becoming a reality. In fact, most AI researchers believe that it's only a matter of 20 to 30 years before machines become at least as intelligent as humans.

Already over 80% of Fortune 500 have Neural Net R&D programs and others are realizing its importance.

Now ... Neural Network is back and this time ... to stay ...
Yet, its future, indeed the very key to the whole technology, lies in commercial use.


Where is computer industry headed?

In 1982 IBM introduced the first PC with 64 KB Memory and 5 MB hard-drive for about $3000. In 1991 the same money could buy you a PC with 16 MB Memory and 1 GB hard-drive. In other words in just 15 years disk storage has increased 200 times. A detail look at the trend indicates exponential growth in both performance and storage space of computers.
That will obviously create demand for smarter and more flexible software solutions. As example:
Businesses are already gathering gigabyte of data daily and that is also rapidly growing. The old query and reporting tools are losing their ability to keep up with amount of data and the information they can provide. The newer technologies such as free-form query and OLAP are certainly helping, but if you know what you are looking for. But what about unknown patterns and variable dependencies buried in Terra Bytes of data?

Yet everybody would agree that a natural direction for computers would be Soft Computing and Artificial Intelligence. A path that will lead us to solutions for our demanding future needs.

Trendy applications such as Data Mining, Business Intelligence and Robotics have recognized that fact and are already utilizing AI in many different ways. From Heuristic Algorithms, Fuzzy Logic, Genetic and Evolutionary Algorithms to Neural Nets.
Neural Networks satisfies many requirements of futuristic applications such as:
- Parallel Processing
- Fault tolerance
- Self-organization
- Generalization ability
- Continuous adaptivity
A perfect match for Applications such as:
Intelligent Agents, Monitoring and Warning Systems, Advance Decision Support System (ADSS), Process Automation, Intelligent Personal Assistant and Smart devices.

But yet, Neural Nets are not without problems.

- The inner workings of neural networks are like "black boxes." Some people have even called the use of neural networks "voodoo engineering".
They learn and model based on experience, but they cannot explain and justify their decisions (not that we human can!).

- Neural networks might have high accuracy, but not 100%, as we would want. And unfortunately, not all applications can tolerate that level of error.
But again, even if not exact, they still can be used in conjunction with traditional methods to, for example, cutting down on time in search.

- Neural networks require lot of (sample) data for training purpose. It may have been an obstacle few years ago, but as we move forward that wouldn't be a problem. We are moving to an age where many of our activities are digitally recorded. From bank transactions to phone conversations and from medical history to lab results.

- As popularly portrayed, design and modeling of Neural Network is an Art, rather than a science. But in contrast to other Arts, the outcome of this one can be measured!

To summarize, Neural Network is not the perfect solution for every problem, but also there are lot more applications that can benefit from ANN characteristics than we see today.

 

Last Update: 09/19/2003