Today we are going to talk about one of the most fascinating concepts of Artificial Intelligence(AI) systems that is powering everything from our smartphones to our AI assistants.
Yes, we are talking about a facet of deep learning in the form of neural networks. Neural networks have been with us for the last 70 years but the advancements are much more apparent in the last couple of decades.
It is really important to talk about neural networks at this time of history simply because the global neural network market is estimated to reach $152.61 billion by 2030.
Now that’s a growth we technology lovers cannot ignore. That is why it is very important that we understand the concept of neural networks now so that we can better utilise its uses later on.
Let us start this blog by understanding what are neural networks as well as a brief history of the evolution of neural networks.
What Are Neural Networks?
In order to understand neural networks, we need to understand the inspiration for the name and how it came about and the entire philosophy behind it.
The human brain is connected by neurons with signals being transmitted from one part of the brain to the other with the help of these new neurons. They form a very complicated interconnected network that allows the smooth transfer of electric signals in the brain i.e., data.
Now imagine replicating that artificially with the help of machine learning algorithms to basically mimic the functioning of the human brain and how it is interconnected.
And there you have it, neural networks.
Neural networks are computational models with interconnected nodes just the neurons of the human brain.
This allows the transfer of data at incredible speeds and can help these computational models do complex tasks such as pattern recognition as well as decision-making just as a human does. You can even call it the basis of AI.
A neural network consists of multiple hidden layers with an input layer. These layers are interconnected with nodes which makes it possible for each hidden layer to be connected with each other as well as the input and output layer.
This Is How Neural Networks Evolved
Inception Stage(1940s-1950s)
This is the very early stage of neural network development and this is when the first theoretical model of a neurone was developed by McCulloch and Pitts in 1943.
This is when the world came to know of the first mathematical model of artificial neurons. However, the problem with this generation was that there was no infrastructure capable enough of computing these models.
Perceptrons(1950s-1960s)
This era is defined by Perceptrons, a single-layered feedforward neural network developed by Frank Rosenblatt in the 1950s. While this was still quite the stage of infancy for neural networks, this model helped in simple pattern recognition.
Along with that, we must also mention other models developed by Bernard Widrow and Marcian Hoff that could actually be used for adaptive signal processing.
AI Winter(1970s)
While it is important to talk about the advancements of neural networks, it is also important to talk about when these advancements started slowing down.
That is exactly what happened in the 70s when Minsky and Papert’s revelations highlighted the limitations of single-layer perceptrons. This was a blow to the entire interest in neural network research.
Backpropagation(1980s)
The backpropagation algorithm was brought into the public limelight by the relentless work of Williams, Rumelhart and Hinton. This finally allowed multi-layer networks with interconnected notes to be trained properly.
This helped stop the AI winter and people grew interested in computational algorithms and connectionism.
Architectures(1990s)
This is when we first see the development of the Convolutional Neural Networks (CNNs) developed by Yann LeCun. This paved the way for the development of neural networks capable of image recognition.
This is also the period when we see the development of Recurrent Neural Networks (RNNs) by Hochreiter and Schmidhuber with Long Short-Term Memory (LSTM) networks. This was developed primarily to address long-term dependencies.
Deep Learning(2000s)
The 2000s gave rise to the development of the first proper strides in Deep Belief Networks based on Hinton’s work on deep learning networks.
One of the key factors that ushered in this era of Deep Learning was the improvement in computational power of GPUs which finally made training deep networks possible.
Computer Vision and NLP (2010s)
As we slowly inch award way towards the present generation, we begin to see the real advancements in neural networks because the foundations and the concepts behind the algorithms for these networks have already been set.
This is when we see the introduction of Generative Adversarial Networks (GANs) in 2014 by Ian Goodfellow which finally allowed the generation of realistic data.
Along with that we also see the success of ImageNet in 2012 and the introduction of Transformers in 2017. These were instrumental in the development of GPT and BERT.
Present Trends (2020s – now)
The 2020s saw the development of the Large Language Models (LLMs) like GPT-3 by OpenAI. This was one of the best usages and implementations of neural networks.
This was also the time when we saw the development of self-supervised learning as well as very efficient architectures such as EfficientNet.
This then helps in the development of concepts such as Ethical AI as well as bias mitigation.
Here Are A Few Types of Neural Networks
Convolutional Neural Network (CNN)
When we talk about convolutional neural networks, they are actually a very specialised kind of neural network and are mostly used for image processing.
These neural networks utilise convolutional layers in order to understand and study different features from the images inputted so as to understand and do image recognition and classification quite easily.
The CNN is divided into multiple layers and data in the form of images get access to the input layer and then this data passes through different convolutional layers which helps in the processing of essential information. This is very important and actually quite effective in interpreting images.
CNNs can be very useful for image recognition as well as video analysis, especially when it comes to areas of high importance such as medical imagery analysis.
They have their advantages and disadvantages but the primary advantage is that these networks are self-understanding and very adaptable.
The disadvantages are that they are very power-hungry when it comes to computational needs.
Recurrent Neural Network (RNN)
If you are looking for neural networks that are designed to recognise sequences of data and can be excellent when it comes to sequential data processing then you get Recurrent Neural Networks (RNN).
RNNs are excellent when it comes to applications where having a memory of previous inputs is important and this kind of a neural network is excellent when context from the previous inputs is very essential.
These kinds of neural networks are used for speech recognition as well as language modelling and they are also instrumental in the development of temporal dynamics applications.
They are also very beneficial when it comes to machine translation and video processing applications however, they are very intensive on computational resources.
Multilayer Perceptron (MLP)
These are the types of neural networks with multiple layers of neurons with all of these layers being interconnected with each other.
The data in these networks flow from the input layer to the output layer with a lot of intermediary layers and these types of neural networks actually learn with the help of backpropagation.
MLPs are used for things like regression problem solving as well as binary classification and are used for their excellent deep learning capabilities.
They are excellent for complex problem solving but the primary disadvantage of MLPs is that they are computationally intensive.
Long Short-Term Memory (LSTM)
If you are looking for a neural network that does well with the problem of vanishing and exploding gradients in Recurrent Neural Networks, a Long Short-Term Memory Network (LSTM) is one of the best ways to solve it.
It runs on gates with the four gates being the forget gate, input gate, input modulation gate and output gate as well as memory cells which are used to read, write and erase data.
This is an excellent neural network for language models, video analysis, speech recognition as well as time series forecasting.
Feedforward Networks
This is one of the simplest neural networks out there are Feedforward networks with input layers as well as output layers with hidden layers in between. However, the one thing to notice about this neural network is that it has no feedback loops or cycles.
The information in these neural networks moves in one direction and these networks are trained with backpropagation.
While this is not the most complex type of network out there, these can be good for basic pattern recognition and classification along with function approximation.
This Is How a Neural Network Works
Neural networks basically work in 4 essential steps.
Association
This is the training stage of the process where the network is taught to remember different patterns and ascertain how to understand patterns and remember patterns.
Classification
The classification stage is all about organising data into different classes and it is about the recognition of patterns into predefined classes.
Clustering
Clustering is about the identification of different aspects of each data instance without the help from the context and it is when the network truly begins to do its work.
Prediction
Finally, we come to the prediction stage of the process where network engineers compare the expected results with the results that were produced by the network so as to understand the accuracy of the network.
These 4 stages are executed successfully with the help of forward propagation and backpropagation.
Forward Propagation
The forward propagation consists of the input layer where the initial data is received and is represented by a node and this is followed by different weights and connections that connect the input layer to all the other layers.
Speaking of the other layers, they consist of hidden layers which improve the processing of information and this actually allows the network to recognise different kinds of patterns in the data.
This is followed by the output layer and this is when we get the result that we are expecting or rather the result that we want to reach towards.
Backpropagation
Backpropagation is different from forward propagation in that it is basically an evaluation process that is carried out after a result has been reached in the forward propagation process.
This process uses a loss function as well as a gradient descent to reduce the loss and lower the inaccuracy. Additionally, the backpropagation process is also utilised for adjusting and adapting the different weights and is vital for reaching a desirable effect.
When it comes to training a neural network, both backpropagation, as well as forward propagation, are needed to improve the accuracy of the network.
These Are the Advantages of Neural Networks
Pattern Recognition
One of the most usable and one of the most helpful advantages of neural networks are definitely going to be pattern recognition and this can be excellent for complex tasks such as that in the field of healthcare.
Natural Language Processing(NLP) is one other area where the limits are endless when it comes to using neural networks in identifying and recognising complex patterns.
High Adaptability
Neural networks are highly adaptable which means you can utilise them wherever you like for any kind of activity and they can be utilised in situations where input and output relationships are complicated.
They can be made to learn from existing data quite easily and they will be able to start producing results quite quickly as well and they are very friendly with new environments and new challenges.
Parallel Processing
If you have the need for processing large amounts of data simultaneously then neural networks are the best option for you because they are able to do that with quite fluidity.
They are able to do multiple tasks all at once which might be of a different nature and it is also quite possible that this can improve the efficiency of the computations as well.
High-Dimensional Data Handling
If you are looking for machine learning networks that are able to process data with a large number of features and dimensions then neural networks are the best solution for it.
This makes them very effective at image and video processing with a lot of dimensions.
These Are the Disadvantages of Neural Networks
Computational Requirements
One of the most well-known drawbacks of neural networks is that they can be quite stressful computationally because they require a lot of processing threshold and computational power to operate.
This is actually one of the most prominent barriers to entry for people looking to develop new kinds of neural networks. This is slows down development and advancement.
Need for Large Datasets
You can’t just start training neural networks because neural networks always require very large datasets in the form of large amounts of labelled data.
If you cannot provide that level of high-quality data then that might compromise the performance and the results.
Black box Nature
Although we know how neural networks work but there is still a lot more to learn about how these networks actually make decisions.
We are still in the dark when it comes to actually understanding how this works because it is just like doctors understanding how the human body works, while they have an excellent idea but they are still not able to create things like blood and organs.
Ethical and Bias Issues
Your neural network is only going to be as ethical as the data you feed it because if the training data has biases present in it then the results are also going to have biases and the network is also going to have bias.
This is very difficult to avoid and sometimes it can lead to the amplification of these bad biases and it can actually lead to an ethical concern because a new neural network does not know what is right and what is wrong.
Real-World Applications of Neural Networks
Social Media
One of the key industries that can see the use of neural networks is definitely going to be social media because social media can become even more effective at recommending content by understanding user behaviour with the help of neural networks.
These networks can allow the processing of a lot of user-generated content which can also be excellent when it comes to the promotion and marketing of other content in the form of advertisements.
Facial Recognition
Neural networks at all about understanding and recognising patterns and we are talking about image recognition.
This can give you an idea and how important neural networks can be when it comes to facial recognition technology in security systems.
The use of neural networks is not just limited to facial recognition in security systems because it can be used for anything from unlocked phones to law enforcement.
Aerospace
Aerospace has a lot of use for neural networks whether it is for the development of autonomous navigation or whether it is during the manufacturing process in the form of better drag coefficient analysis and much more.
Neural networks can also be excellent for creating even more realistic flight simulators for training future pilots.
Stock Market Prediction
Neural networks are excellent at understanding and recognising patterns and that is just the case with stock market prediction.
These networks can be excellent in understanding and the assessment of investing risks and they can also be utilised to create better-automated trading algorithms.
And since they are excellent at processing a lot of data, they can also be excellent for predicting market trends.
Healthcare
The most significant usage of neural networks can be in the form of medical imaging by the analysis of medical imagery and pattern recognition to detect diseases before they can be diagnosed with conventional techniques.
It can also be excellent in creating new kinds of drugs and medicines and also custom-creating medicines and dosages for patients.
These networks will also be excellent at predictive analysis of patients.
Healthcare
Defence is another area that can see tremendous benefits from neural networks and machine learning in the form of threat analysis as well as better predictions when it comes to military operations.
This technology can be utilised in creating better systems in the form of autonomous vehicles and also better drone technology and it can also benefit when it comes to cyber security.
Weather Forecasting
While all the other industries will definitely benefit from neural networks, the most immediate benefit can be expected in the department of meteorology where a lot of predictions through imagery is needed.
Neural networks can completely change the game when it comes to analysing climate patterns in the form of better climate modelling and this can also be very helpful when it comes to making weather predictions.
Signature and handwriting analysis
AI has made it quite easy to replicate things and signatures are one such piece of outdated verification that can be replicated quite easily.
However neural networks can be excellent at fraud detection as well as improved character recognition because of their superior image processing capabilities.
So, What Does the Future of Neural Networks Look Like?
We can see a lot of advancements when it comes to neural networks and their integration throughout every kind of industry.
We might also see a solution to the Black Box issue that is currently one of the biggest problems for such machine learning networks.
We are also going to see a lot more interest in these networks when it comes to all industries because everyone can benefit from these networks.
We hope this blog has helped you get a better understanding of neural networks and how they can be beneficial for everyone.
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