Advantages and Disadvantages of Neural Networks


Advantages and Disadvantages of Neural Networks
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The ever-changing technology is expected to bring the modernization of society to a whole new level. Neural Networks are one of such remarkable breakthroughs. Artificial Neural Networks (ANNs), or Simulated Neural Networks (SNNs), are an application of machine learning and, as the name, suggests are mechanical recreation of human-like AI systems.

Adaptation of technological concepts in real life is not new; Smart cities, smart homes, Prosthetics, and Robots based on human intelligence are some greatest examples of how far humans have come. However, all have one thing in common, that’s Neural Networks. The Advantages and Disadvantages of Neural networks evaluate the benefits and limitations of this technology from many perspectives.

About Neural Networks

b>Frank Rosenblatt created the first neural network in 1958, known as Perceptron. It may sound like the aliens in the movie Transformers, but only the idea seems to have any close resemblance to the actual execution. Nevertheless, it is a technical concept in Artificial Intelligence(AI) that has the goal of creating a computer program capable of learning and making brain-like responses.

While the responses and interpretation are fast, data storage is unlimited in the computer-based mode of operation. Many science-fiction movies have given a lot of interesting ideas and theories about the creation of it and its after-effects. But the actual achievement of the human brain-like program seems an impossible job for now.

As far as the researchers are concerned, the machines capable of accomplishing tasks automatically, halting operations, and detecting security risks are required to be designed. This will ease the job for humans, making it an automated appointment. The applications can be unlimited and can have various pros and cons.

What are the Advantages of Neural Networks?

The approximation theorem and various other mathematical tools are the basis of complex computer algorithms. However, the programmer needs a computer understandable set up of instructions to command it for tasks. It can be designed to perform the task quite conveniently. The key advantages of neural networks are as follows.

1. Efficiency

Unlike humans, a machine doesn't get tired if it runs within well-specified limits. Also, it can work continuously, saving a lot of time producing more remarkable results. If programmed correctly, a machine can complete a task quickly, which can take humans longer hours.

2. Continuous Learning

A neural network is designed to learn and improve its results continuously. Once the system is trained, it can produce output without the need for complete inputs. With the use, the program or applications become more user-friendly.

3. Data retrieval

The most crucial benefit of using cloud services or online data management is its retrieval. If the hardware suffers any damages or failure, the whole program backup is available online. So there is no need to provide training to the system again and again.

4. Multitasking is one of the common advantages of Neural Networks

New advanced programs are designed in a way that is capable of producing multiple results and multitasking. The user is free to scroll through different tasks at the same time. It is not possible in simpler networks or programs.

5. Wide Applications

Neural Networks are designed to make machines work like humans, and hence the replacement comes with many advantages of Neural Networks along with numerous applications. Medical, engineering, mining, agriculture, etc., can find numerous benefits from security to day-to-day tasks using this technology.

What are the Disadvantages of Neural Networks?

These algorithms are designed to recognize preferences and leave the unimportant ones to determine the output. These preferences can differ at different times resulting in a different decision. A computer-dependent decision is based on a fraction of essential qualities/values/requirements at a given time. These approximate results may lead to wrong decisions. Due to its complex nature, there are several disadvantages of Neural Networks that need to rectified.

1. Hardware dependent

Although the data is stored online, artificial networks still require hardware to create them in the first place. The hardware cost increases with the complexity of the problem, and its setup requires additional efforts to maintain them.

2. Complex Algorithms are foreseen disadvantages of Neural Networks

All the programming needed to be done initially requires lengthy and complex programs to be written. For example, it may require months to create an algorithm capable of working a specified task.

3. Black Box Nature

Even when the results are accurate human analysts can't track and check the derivations. Most neural networks are black-box systems generating results based on experience and not on specified programs, making it difficult for modifications.

4. Approximate Results

Various theorems are used to give only a probable value. All the theories used are not entirely suitable to give results possible for all situations, and the desired output may not be obtained. This uncertainty is among the eye-opening problems with Neural Networks.

5. Data-dependency

Whatever data is fed to the machine, it acts accordingly. The more amount of data is used during training, the more accurate the results are. Dependency on data is one of the leading disadvantages of Neural Networks, as some have to be on the maintenance side to watch it. Since there are errors in the data, the result will be faulty, which poses serious threats.

Conclusion on Pros and Cons of Neural Networks

Neural networks are the backbone of various applications providing users an automated robotic experience. There is a lot to modify in the present systems to understand the working conditions and generate desired outputs. There are various applications and problems, such as space exploration, which need more advanced mechanisms to study the conditions where human testing is restricted. In these scenarios, it has to evolve as an alternative to provide feasible outputs that can help researchers to move forward.

 
 
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