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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Neural Networks have the ability to learn on their own and produce results that are not limited by the data supplied to them. In addition, because the input is kept in its own networks rather than a database, it does not suffer from data loss.
With the help of a Neural network, you can store information on the entire network, work with insufficient knowledge, and learn automatically.
Many machine learning algorithms can be used to perform supervised and unsupervised learning in the context of Deep Learning. However, neural networks generally require far more data than traditional machine learning methods; for example, thousands if not millions of labeled examples are needed. This is a complex problem to tackle, and many decision.
Neural networks are computing systems with linked nodes that mimic the functioning of neurons in the human brain. They can discover hidden patterns and relationships in raw data, group and categorize it, and ? over time ? learn and improve using algorithms.