Neural Networks: A Solution for Complex Problems in Science and Engineering

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The integration of technology into daily life is not a new idea. Examples include smart cities, smart homes, prosthetics, and robots that mimic human intelligence. These advancements demonstrate the extent to which humans have progressed.

Neural networks, known as Artificial Neural Networks (ANN) or Simulated Neural Networks (SNNs), are the next step in that progress. 

In this article, we will cover the potential of neural networks in solving complex problems in science and engineering as well as their types, benefits and challenges. 

What are Neural Networks?

Artificial Neural Networks consists of layers of connected nodes, called artificial neurons, which can receive, process, and transmit data and are designed to work like the human brain.

Used to solve complex problems in artificial intelligence (AI), they are based on the concept of machine learning, more specifically, deep learning. In this system, the computer can learn and make decisions without being explicitly programmed or guided by a human.

What makes neural networks different from regular computers is that they can continue to learn even if some parts are damaged or lost.

Video Source: Youtube/Simplilearn

Furthermore, system architectures that learn to perform essential tasks or look out for irregularities can present a significant asset for engineers. Experts have displayed the versatility of this artificial intelligence form in a wide variety of projects, and a number of them could hold tremendous significance for the healthcare and transportation future.

Neural Networks in Action: Real-World Applications

Neural networks are used in many types of technology such as Google’s search engine. They are swift and accurate, which makes them very useful for solving complex problems.

Applications of Neural Networks

One of the best-known neural network applications is in self-driving cars. These vehicles use neural networks to gather sensory information about the road ahead, such as the location of other vehicles and obstacles and make decisions about navigating the environment.

A good example is Michigan State University’s Connected and Autonomous Networked Vehicles for Active Safety (CANVAS) group, which developed a neural network called the CANVAS Brain.

Other examples of neural network applications in science and engineering include:

  • Predicting weather patterns and forecasting the weather
  • Identifying and classifying objects in images or videos
  • Analyzing and interpreting speech
  • Designing and optimizing materials for various applications
  • Predicting the behavior of systems, such as traffic patterns or financial markets.

Also, in the healthcare industry, neural networks have the potential to improve delivery and outcomes by predicting how often aging populations need to visit the hospital, detecting early signs of Alzheimer’s disease in MRI scans, and automating the processing of clinical notes.

Types of  Neural Networks

Neural networks can be classified based on how the data flows from the input nodes (where the data enters the network) to the output nodes (where the results are produced).

There are several different types of neural network, each with its strengths and limitations. For example, some neural networks are better at solving problems with precise input-output mapping, while others are better at processing data with a grid-like structure, such as images. 

Types of Neural Networks

The most commonly used types of the neural network include:

Feedforward N eural Networks

These are good at solving problems with a clear relationship between the input and the output. For example, if you have a picture of a cat and you want the computer to recognize it as a cat, a feedforward neural network can do that. However, these models of networks could be better at figuring out more complex relationships.

Convolutional Neural Networks

These are used for tasks that involve data with a grid-like structure, like pictures. They are excellent at object recognition in images and have been used in many different applications. But, they can be slow and need a lot of data from which they can learn.

Recurrent Neural Networks

These are used for tasks involving data in a sequence, like words in a sentence. They are good at understanding the relationships between different pieces of data and have been used for language translation and speech recognition. However, they can be hard to train and sometimes need help learning long-term relationships.

Generative Adversarial Networks

These are made up of two neural nets that work together to generate synthetic data that looks real. For example, they have been used to create realistic images and sounds. However, they can be hard to train and also need a lot of data to work well.

Autoencoder Neural Networks

These are used to reduce the complexity of data and learn essential features. They have been used in tasks like image and speech recognition. However, they can be sensitive to the settings we use and may only sometimes learn meaningful patterns in the data.

What are the Advantages and Disadvantages of Neural Networks?

Neural networks are a potent tool for data analysis and machine learning, and they have a number of unique characteristics that make them well-suited for specific tasks. However, like any tool, neural networks also have advantages and disadvantages. 

Benefits of Neural Networks

There are several benefits to using artificial neural networks (ANNs) for data analysis and machine learning tasks. Some of the pros include:

  • Neural networks are adaptable and can be used for regression and classification problems.
  • Any data converted to numbers can be used with a neural network, as it is a mathematical model with approximation functions.
  • Neural networks can model nonlinear data with many inputs, such as images.
  • They are reliable for tasks involving many features and split the classification problem into a layered network of more specific elements.
  • Neural networks make fast predictions once they are trained
  • They can customize them with any number of inputs and layers
  • Neural networks perform best with more data points.

Limitations of Neural Networks

A decision made by a computer is based on a select set of qualities, values, or requirements at a given time. These approximate results may sometimes lead to incorrect decisions. Because of their complex nature, several drawbacks to using neural networks need to be addressed. Some of the cons of neural networks involve the following:

  • Neural networks are known as “black boxes” because it is difficult to understand how much each input variable is influencing the output variables
  • Training neural networks can be computationally expensive and time-consuming using traditional CPUs
  • Neural networks rely heavily on the training data, which can lead to problems of overfitting and generalization. As a result, the model may be more attuned to the training data and may not perform as well on new or unseen data.

Is Deep Learning the Same as Neural Networks?

Deep learning and neural networks are closely related but are not the same. Deep learning uses computers to learn and make decisions independently, while neural networks use neurons to process and transmit information.

Deep learning is focused on finding patterns and relationships in data, similar to how the brain processes and responds to stimuli. It does this by using layers of neural networks to filter and analyze data.

Video source: YouTube/Toda.Network

On the other hand, neural nets transmit data in the form of input and produce output through various connections. Various tasks, such as recognizing patterns or making decisions based on the data, can be used.

So, what are the benefits and challenges of using deep learning techniques based on neural networks in problem-solving?

The benefits include the following:

  1. Processing large amounts of data
  2. Deep learning algorithms
  3. Learning and identifying complex patterns in data which leads to improved accuracy in predictions and decisions.

Moreover, these algorithms can learn from unstructured data, such as images and text, which can be helpful for tasks such as natural language processing.

However, one of the challenges to using deep learning techniques in problem-solving is that the algorithms require a large amount of data to learn from and a lot of computational power to process everything. All this can be difficult to obtain, expensive, and resource-intensive.

Another issue can be the difficulty in interpreting and understanding results, as they operate as a “black box,” and it is not always clear how they arrived at a particular decision or prediction.

Finally, deep learning algorithms can be prone to overfitting, which means they perform well on the training data but poorly on unseen data. A way to mitigate this is through proper data splitting and regularization techniques.

Ongoing Development and Future Potential of Neural Networks

Artificial neural networks and deep learning are currently popular techniques for developing solutions to specific problems, but they are not the only available options.

Some potential future developments in neural network technologies include the integration of: 

  • Fuzzy logic, which allows for more than just true or false values and can be designed for a variety of applications; 
  • Pulsed neural nets, which use the timing of pulses to transmit information and perform computations; 
  • Specialized hardware such as neural network processing units (GPUs) and Neurosynaptic architectures, which function more like a biological brain than a traditional computer; and
  • Improvements to existing technologies through faster and cheaper computing power and improved training methods.

There are also potential applications in robotics, where neural networks can be used to enable robots to think and make decisions in a more fluid and non-brittle way.

There may also be opportunities for human-machine brain melding, where neural networks have the potential to  connect human brains with artificial intelligence. 

However, there are still many challenges to overcome in these areas. These include ethical issues and the need for a better understanding of how neural networks work.

It is also possible that neural networks may become obsolete as new approaches to artificial intelligence and problem-solving emerge.

Neural Networks: Key Takeaways

Neural networks consist of layers of connected nodes, called artificial neurons, which can receive, process, and transmit data and are designed to work like the human brain.

They are based on the human brain’s structure and function and can learn and adapt to new information. Neural networks have already been used for various tasks, including speech and image, natural language processing, and predictive modeling.

There are several types of neural networks:

  • Feedforward neural networks 
  • Convolutional neural networks 
  • Recurrent neural networks 
  • Generative adversarial networks 
  • Autoencoder neural networks 

Overall, there are many future possibilities for neural networks for their use in robotics, brain-machine interfaces, and the creation of artificial general intelligence. However, there are challenges like the black box nature and the need to understand better how they work. 

Despite these challenges, the potential of neural networks to solve complex problems in science and engineering is significant and continues to be explored and developed. 

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.