Convolutional Neural Networks: Here is How Deep Learning Changed

When we hear the term AI or Artificial Intelligence, we envision self-aware fictional robots such as Terminator or even the highly intelligent Sophia. However, the advancements in artificial life form have infinite possibilities for the future, but as of now, AI is more about behind the scene automation and innovation. In fact, even we come across AI every day in our daily lives and most of us are not even aware of it. From interacting from business or website chatbots to NPC’s in online games, artificial intelligence is all around us.

Ever since the emergence of the COVID-19 pandemic, AI has been widely accepted in business automation to increase enterprise efficiency as well. AI was a huge part of every business automation, even before the pandemic. But the huge demand for automating processes cost reduction, and employee/customer satisfaction reinforced the need for artificial intelligence.

This recent development of AI in business is extensively due to progress in deep learning, which is a part of another innovative technology; machine learning (ML). To understand the true potential for artificial intelligence in business efficiency, and to distinguish its purpose from the myths, we must first understand deep learning and what makes it game changing.

What is Deep Learning

As mentioned earlier, deep learning is a part of ML, which is basically a convolutional neural network with at least 3 or more layers. The purpose of these neural networks in deep learning is to study and mimic the behavior of the human brain. The input to study is provided by artificial intelligence engineers, who integrate codes and programs into these so-called neurons. Deep learning has the capability to identify and match these huge amounts of data, allowing it to “learn” from them. We had initially mentioned that it consists of a minimum of three layers, the additional layers can help to optimize and refine the data for accuracy. Therefore a neural network with more layers will learn and stimulate more accurately than a network with just a single layer.

In today’s business environment, deep learning operates several innovative artificial intelligence (AI) applications and services that are leveraged to enhance enterprise automation, perform big data analytical and other tasks without the need for human intervention. It is the part of AI that helps learn and understand; therefore responds better with each iteration. Everything from virtual assistants, industrial machinery, self driving cars, and even voice-enabled IoT devices integrate AI based deep learning.

How Does Deep Neural Networks Learn

The main advantage of business in using deep learning is that developers can teach the neural networks to learn and adapt. Therefore, even if there happens to be a transition in business process or architecture, deep learning too can evolve with the changing needs. This second transition of deep learning does not require the engineers to again teach the network, because convolutional networks can learn by themselves from the data without the need for human intervention.

For example, traditionally, if a business operating in vehicle manufacturing wanted the network to identify a picture of a vehicle and decide the manufacturing type and vehicle model, an engineer would initially need to identify all the features the neural network would need in order to perform the activity. The engineer would then have to program this into the neural network in order for the AI application to accurately identify the vehicle and provide details. This is significantly time consuming as the AI engineer will have to initially identify the features of the neural network and then teach the network to perform tasks. This can be extremely challenging for networks that have to perform a lot of automation tasks, such as identifying vehicle type, model, year, drive type, etc. It would also be difficult to make changes based on new updates, such as changes of rules or new vehicle management scenarios. In such situations, the engineer will have to re-engineer the model from the beginning to adapt to the new changes.

In contrast, the modern deep neural networks for business automation, such as the convolutional neural network, can automatically study all the new features or requirements without the need for an engineer to provide inputs. The convolutional neural network can do this by simply being exposed to a vast amount of big data or previous history. Therefore, unlike the traditional neural network, engineers working on the convolutional neural networks do not have to identify each feature individually and remodel the network to meet business demands. Hence, making it less time-consuming, efficient, and inexpensive when compared to traditional methods.

How Is Deep Learning Empowering Businesses Processes

Because deep learning is so well suited to solving complicated business challenges and automating repetitive tasks, it is widely accepted in businesses all around the world.

Here are some of the benefits of using Deep Learning in Business.

1. Works With Unstructured Data

Working with unstructured data may not sound to be of significant relevance, but in the business environment, this is particularly essential. This is because the majority of business data is unstructured and is accumalated from thousands of sources. Unstructured data such as images, voice, or texts are the various well-known data formats that businesses use. Traditional machine learning algorithms are restrained in their capacity to analyze unstructured data, indicating this huge collection of business relevant information often goes unnoticed. This is where deep learning makes the most impact.

2. Better Self-Learning Capabilities

As mentioned earlier, deep learning is made up of multiple layers of neural networks. Each layer of deep neural networks allows the deep learning models to become more precise at learning complex features and achieving more intensive computational responsibilities.

This is because of deep learning algorithms’ capability to significantly learn from their own mistakes or errors. Therefore, it can validate the accuracy and determine the efficiency of output.

3. Cost Effectiveness

While the initial training of deep learning models using AI engineers can be quite expensive, once trained, the system can self-sustain and help businesses reduce unnecessary expenditure. Especially in manufacturing industries and large scale product manufacturing sectors, the damage of an incorrect prediction or product malfunction can cause massive losses in the budget as well as reputation. Trained deep learning models can significantly reduce error margins across industries and verticals and provide efficient results.

Conclusion

AI services and deep learning are considered to be the most influential technologies in determining business efficiency and cost optimization. With AI becoming ever more pervasive for industrial automation, having a fundamental strategy to introduce innovative technologies is a must for continued business success.

Author Bio: Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies, a software services company focusing on technologies like BigData, Mobile App Development, IoT, and AI services. He is also a contributor to several prominent online publishing platforms such as Medium.com and HubSpot.