As technology evolves, intelligent automation has become a top priority for many executives in 2020. Forrester predicts that the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. Businesses are gradually being reshaped as more companies identify and implement artificial intelligence (AI) and machine learning (ML).
Industries around the world are combining AI and ML with enterprises to enable rapid changes in key processes such as marketing, customer relationship and management, product development, production and distribution, quality inspection, order fulfillment, resource management, and more. Artificial intelligence includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), speech recognition, etc. to create intelligent automation combined with robotics for organizations across multiple industrial fields.
Let’s take a look at how some of these technologies can help automate industries around the world.
Machine Learning Anomaly Detection
Machine learning has recently been applied to detect anomalies in the manufacturing process. Using machine learning, device health monitoring can be automated, where characteristics of sensor device data, such as vibration, sound, temperature, etc., can be learned from the collected data through training.
This is useful for identifying early wear and tear of equipment and avoiding catastrophic damage. It catches the smallest defects that the human eye might miss. Techniques can be selected based on the type of attributes required to extract features, and various machine learning algorithms can be applied based on features to detect anomalies.
Automotive Deep Learning
One of the main tasks of any machine learning algorithm in a self-driving car is to continuously render the surrounding environment and predict how those environments might change. Self-driving cars must recognize objects or pedestrians on the road, day or night. For the success of autonomous vehicles, automotive companies combine advanced driver assistance systems (ADAS) with thermal imaging.
Pedestrians can be identified in any weather conditions by executing deep learning algorithms on image datasets captured by thermal cameras. It can cover larger or smaller parts of the image depending on distance. Few deep learning algorithms like Fast R-CNN or YOLO can help with this kind of automation, making self-driving cars safer and more efficient on the road.
Automatic verification using OCR
OCR is another technique that uses deep learning to recognize characters. It is useful in manufacturing to automate processes that are prone to human error due to fatigue or haphazard behavior. These activities include verifying batch numbers, batch codes, expiration dates, and more. Various CNN architectures such as LeNet, Alexnet, etc. can be used for this automation or can be customized to achieve the desired accuracy.
Machine Learning in Finance and Banking
Lending is a huge business for financial institutions. The value and approval of a loan is entirely dependent on the likelihood that the individual or business will be able to repay. Determining creditworthiness is the most important decision for the success of this business. In addition to the credit score, various other parameters are considered to make such a decision, making the whole process very complicated and time-consuming.
To save time and speed up the process, well-trained machine learning algorithms can be used to predict and classify an applicant’s creditworthiness. This can simplify the classification of applicants and improve loan sanctions decisions.
AI and ML are creating new visions of human-machine collaboration and taking the enterprise to the next level. Machine learning helps organizations in various industrial sectors to develop intelligent solutions based on proprietary or open source algorithms/frameworks that process data and run complex algorithms on the cloud and at the edge. Machine learning models can be built, trained, validated, optimized, deployed, and tested using the latest tools and techniques. This ensures faster decision making, higher productivity, business process automation and faster business anomaly detection.
Reviewing Editor: Guo Ting