A subset of machine-learning neural networks (NN), also called artificial neural networks (ANN), are models that employ computation algorithms. Neural networks can use their unique ability to interpret complex or incomplete data, detect and distinguish pattern patterns too difficult for humans, or other techniques that computers employ. Neural networks have provided us with more accessibility in various ways, such as services for sharing rides, Gmail intelligence-based sorting, and even suggestions from Amazon.
The most exciting characteristic of neural networks is that once they are established, they can learn on their own. They can mimic human brains made up of neurons, the whole human brain’s primary components, and neural networks, which are the means of transmitting data.
An artificial intelligence company utilizes neural networks to create their natural language processing software, including the application to automatize grammar correction. “The neurons continuously alter how they respond to the stimulus. If you’ve done something right, you’ll receive positive feedback from your neurons, making them much more likely to occur in the subsequent instance. In contrast, if neurons receive negative feedback, they will become more likely not to trigger the future instance.”
The usage of Artificial Neural Networks:
The use of artificial neural networks for manufacturing
The NNs and ANNs in manufacturing sector has contributed a lot, and Artificial Intelligence (AI) has been used for some time in manufacturing. Still, companies have mainly focused on bespoke specialist systems and did not reap the advantages that ANNs provide, for example:
- The parallel computer architecture affects various disciplines and applications by processing natural language images, speech biomedical engineering, and related bioinformatics issues.
- The self-adapting capabilities of their networks, which can predict changes in the environment, increase the ability of networks to learn.
This makes ANNs an ideal tool to help with computer integrated manufacturing and applications in intelligent factories. Manufacturing has seen rapid change in recent years. It is pretty different from the traditional manufacturing factories that we think of in those of the Industrial Revolution or even manufacturing plants built 20 years ago.
The idea of feed-forward systems to identify handwritten letters is straightforward. A bitmap of the handwritten letters is input, with the right note or digit as the output. The user is required to train the network by giving it handwritten patterns.
Two of the most common handwriting applications used for recognition are:
Optical character recognition for data entry
Signature confirmation on bank cheques
Feed-forward networks are equipped with these features:
- First, they arrange perceptron into layers where the top layer is responsible for receiving inputs and the bottom layer gives outputs. The middle layer does not connect to the external world; hence they are called hidden layers.
- Each perceptron in one layer is linked with each perceptron in the layer following. The information is “fed forward” from one layer to the next continuously, which is why we call these feed-forward networks.
- There is no link between perceptron and a single layer.
Traveling Salesman Problem
The traveling salesman’s problem concerns finding the fastest route to each city within an area, and we could utilize Neural Networks to solve this problem.
Neural network algorithms, referred to as one that uses genetics start by randomly changing the networks’ directions to take on the challenge. The algorithm chooses a city randomly each time and finds the closest city, and this procedure repeats several times. Every time the form of the network is changed, the network transforms into an extended ring around each city.
The algorithm reduces any length of rings. This way, once can calculate the travel issue.
The Neural Network used for image compression comprises equal amounts of input and output layers. In the intermediary layer, the size is reduced. It is the ratio that acts as the input layer, and an intermediary layer decides the compression rate of the network.
It is possible to calculate the contrast ratio of compression on images by applying the formula below.
Comparison Ratio = Input Layer / Intermediate Layer
The basic idea behind neural networks to compress data is to create, store and decrypt images from the beginning. Within this network, once can make use of inputs for training and training purposes.
Stock Exchange Prediction
The accuracy of predictions made using neural networks made them helpful in forecasting the market for stocks. For large companies, making predictions on the market for stocks is an everyday practice. This is done by utilizing the latest developments, the political atmosphere, public opinion, and the recommendations of economic experts.
We can also use neural networks to determine the business and financial failures of the future, including debt risk assessment and acceptance of credit.
It is pretty common to experience this when shopping online when you contemplate buying something, but you buy many things because of some excellent suggestions? Sure, you can blame neural networks for this. By utilizing neural networks and their expertise, they develop Artificial Intelligence systems that know more about you than you do.
Companies such as MJ Futures have claimed excellent 199.2% returns over two years using their neural network methods for prediction.
Dean Barr and Walter Loick at LBS Capital Management achieved promising results with a primary neural network. It was built upon six financial performance indicators, which were utilized as inputs. They include ADX, the current value for the S&P 500, and the net change in the S&P 500 value over the past five days. ADX is the trend of change in the average over the past 18 days.
Cheque Deposits made through Mobile
A majority of central banks have removed the requirement to send an unpaid check to the bank by allowing customers to deposit checks using an app for smartphones. The technology behind these apps utilizes Neural Networks to decipher and transform the handwriting on cheques into text. Essentially, Neural Networks find themselves at the core of any application that requires handwriting/speech/image recognition.
What can an institution in the financial sector be doing to verify the legitimacy of a transaction? In most cases, the volume of transactions per day is too large to be manually analyzed. To help with this task, Artificial Intelligence is used to create systems training to recognize which types are authentic (speak learning or talk into Neural Networks!).
FICO is a company that develops credit scores that help determine the creditworthiness of individuals. Employs neural networks that power the company’s Artificial Intelligence to identify fraudulent transactions. Factors that influence the outcome include the frequency and size of the trade and the type of retailer involved.
How to Power Your Mobile Phones using ANN
A very well-known feature of smartphones today is converting your speech to text. Pressing a button or saying a specific phrase (“Ok, Google,” for example) will let you start calling your phone, and the phone will then transform your voice to text. Google utilizes artificial neural networks that are connected via the form of a recurrent connection to facilitate your voice to search. Microsoft claims to have developed a speech recognition technology made up of Neural Networks that can translate conversations more precisely than humans.
Smart Personal Assistants
The technology of voice-to-text is getting secure enough to be rely upon for basic conversations. It’s becoming the primary interface for controlling the new generation of personal digital assistants. At first, a few essential telephone assistants, such as Siri and Google Now, can perform searches on the internet and schedule reminders, and even be integrated into your schedule. Amazon further expanded this concept by launching two other components and devices, specifically Alexa and Echo (later, Dot).