Artificial Intelligence and Machine Learning Path to Intelligent Automation – Embedded Computing Design

With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise.

Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.

Let us see how some of these technologies help industries globally to implement automation.

Machine learning has recently been applied to detect anomalies in manufacturing processes. Using machine learning, health monitoring of the equipment can be automated where the specialties of the sensor devices data like vibrations, sound, temperature, etc. from the collected data can be learned through training.

This is useful to identify early wear and tear of equipment and avoid catastrophic damage. It can catch the smallest flaw that the human eye may miss. Techniques can be selected depending on the type of attributes required to extract the features and based on the features various machine learning algorithms can be applied to detect the anomalies.

One of the main tasks of any machine learning algorithm in the self-driving car is a continuous rendering of the surrounding environment and the prediction of possible changes to those surroundings. It is essential for autonomous cars to recognize objects or pedestrians on the road, irrespective whether it is day or night. For the success of autonomous cars, automobile companies integrate advanced driver assist systems (ADAS) with thermal imaging.

By executing deep learning algorithms on the image data set that are captured by thermal cameras, it is possible to identify pedestrians in any weather condition. It can cover a larger or small part of the image based on distance. There are few deep learning algorithms like Fast R-CNN or YOLO that can help achieve this automation making autonomous cars safer and efficient on roads.

OCR is another technology which uses deep learning to recognize characters. It is of great use in manufacturing to automate processes which are subject to human errors due to fatigue or casual behavior. These activities include verifications of lot code, batch code, expiry date etc. Various CNN architectures like LeNet, Alexnet etc. can be used for this automation and it can also be customized to achieve the desired accuracy.

Loaning money is a huge business for financial institutions. The value and approval of the loans is entirely based on how likely an individual or business will be able to repay. Determining creditworthiness is most important decision for this business to succeed. Along with credit score various other parameters are considered for making such decisions which makes the whole process very complex and time consuming.

To save on time and accelerate the process, trained machine learning algorithms can be used to predict and classify the creditworthiness of the applicant. This can simplify the classification of applicants and improve decision making for loan sanction.

AI and ML is creating a new vision of machine-human collaboration and taking businesses to new levels. Machine learning helps organizations across various industrial domains to develop intelligent solutions based on proprietary or open source algorithms/frameworks that processes data and runs sophisticated algorithms on cloud and edge. Machine Learning models can be built, trained, validated, optimized, deployed and tested using latest tools and technologies. This ensures faster decision making, increased productivity, business process automation, and faster anomaly detection for the businesses.

Kaumil Desai is associated withVOLANSYSas a Delivery Manager past 3 years. He has vast experience in product development, Machine Learning on edge, complex algorithms design & development for various industries including Industrial Automation, Electrical safety, Telecom, etc.

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Artificial Intelligence and Machine Learning Path to Intelligent Automation - Embedded Computing Design

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