Making business sense of neural networks. — protonAutoML

How come neural networks are redefining everything and what does it mean for a business? Let’s dive

From Alexa to making business predictions, from predicting climate changes to translating hundreds of languages, artificial intelligence (AI) makes our daily life a breeze. For example, Gmail has recorded an accuracy of 99.99% for detecting spam emails, which helps user saves a lot of time and have a good experience.

According to Accenture, robot-assisted surgery will help the US healthcare industry save nearly $40 billion by 2026.

As per Gartner, by 2023, 40% of the companies will ultimately adopt AI in their business to increase scalability, which they believe will help in expanding the firm’s productivity.

AI’s advancement is beyond any of the above-mentioned things, as it has reached the pinnacle to replicate a human brain.

This artificial human brain is designed and programmed so that it can perform many complex problems in just a fraction of seconds- this connection of multiple artificial neurons is known as Artificial Neural Networks (popularly known as ANN).

ANN- the main motive is to impersonate the functionalities of a human brain. As a human brain transmits signals using neurons that helps an individual react to specific circumstance (output) similarly, ANN is crafted using various perceptron layers that communicate an actual number or a continuous value (signal) that presents an output.

Structure of a neural network

A neural network has three main layers:

1. Input layer: the layer which accepts inputs from the user

2. Hidden layer: layer which is located between the input layer and the output. Here all the essential and intricate processing (unknown to the user) takes place.

3. Output layer: Layer which, after a series of transformation and optimized results, is delivered.

These nodes move the data in one direction only- in the linear combination.

Each dense layer contains nodes, which are joined to different layers using edges. These edges are assigned with “weights, “ representing the value of information assigned to each node. A positive weight means that the node is in an “excitatory connection”, whereas a negative weight represents an “inhibitory connection”.

When a node receives information from the previous nodes, it sums up the total weight; if the total weight is greater than the threshold of that node (set to random numbers), then the information is moved onto the next layer.

The study of a structure with more than three-layer including input and output layer is known as deep learning. Each layer is processed further using the results of the previous layer’s output. Here more layers, more remarkable the ability to solve a complex problem.

How does a neural network work?

Each neurons’ calculations are based on different mathematical calculations. Thus, there are three steps to get the results.

STEP 1: Label the input

All the input samples are labelled and are trained by processing these examples. Each example is marked with the “input” and the “consequence”.

STEP 2: Calculate Error

Training a labelled example involves differentiating between the output of the network and the target output. The difference between these two components is known as error. The network then optimizes itself using this error value, which will cause it to give the final output close to the target result.

STEP 3: Update the model

These systems learn and adapt to perform specific tasks for the labelled examples without being programmed. In other words, these examples modify themselves as per the initial training. For instance, in image recognition, the system can tag an object as “dog” or “no dog” using the previous results to identify other images.

Applications of Neural Networks

The finance industry is booming because of predictive analytics these days. It helps predict the currency exchange rates and helps in forecasting a large variety of volatile investments like stocks and cryptocurrency. Here the neural networks will help in reducing the reiteration of work, lowering human efforts and false-positive results.

As per Business Insider, by the year 2025, nearly all of North America would have the potential to save $70 billion, which otherwise would have been used in middle-level office manual office tasks.

Predicting customer churn to converting leads to purchasers using prospective analytics can be a boon to a company. Nearly 40% of businesses have said that there is an increase in customer experience with the acceptance of AI; because of this, most marketers get encouraged to use their data to achieve business goals.

Chatbots have increased customer interaction by a whopping 85%, and talking about sales and marketing, 35% of marketers believe that AI with neural networks is a game-changer.

Many social media platforms like LinkedIn apply neural networks to detect spam emails and messages. In addition, these networks assist in seeing appropriate contents to create a better recommendation flow for its users.

These artificial neurons aid in recognizing the tone and the language of the speech, often known as voice/speech recognition systems. These networks classify inbound calls into predetermined categories or assign a lead quality score.

Many other topics are yet to be invented using neural networks, but we all need to agree that utilizing this technology offers real benefits to organizations. For example, they help out in processing complex issues, preventing fraud and maintain security.

Originally published at https://protonautoml.com on June 6, 2021.