ARTIFICIAL INTELLIGENCE (AI) AUTOMATION

Artificial intelligence automation refers to the use of artificial intelligence to automate tasks, processes and systems.

There are as several types of artificial intelligence automation and they are as follows; robotic process automation (RPA), machine learning (ML) automation, natural language processing (NLP) automation and computer vision automation.

Robotic process automation (RPA) involves automating repetitive, rule-based task using software robots. It is a combination of hardware and software, networking and automation for doing very simple tasks. It is the automation of service that was previously performed by humans. Robotic process automation is the automation of repeatable and rule based tasks by the use of non-invasive software called BOT which can mimic actions performed by human users on computers to complete various businesses or work processes.

There are several types of robotic process automation and they are; rule-based RPA, decision based RPA and integration based RPA.

Machine learning (ML) automation uses machine learning algorithms to automate complex tasks such as predictive analytics and decision making.

There are several types of machine learning automation and they are; supervised learning, unsupervised learning, reinforcement learning and deep learning automation, each with its own unique method and technique.

Natural language processing (NLP) automation uses natural language processing techniques to automate tasks such as text analysis and sentiment analysis.

There are several types of natural language processing (NLP) automation and they are; text classification, sentiment analysis, named entity recognition (NER), machine translation, chat bots and virtual assistants automation each with its own unique flavor and usage pattern.

Computer vision automation uses computer vision techniques to automate tasks. Its use enables computers to interpret and understand visual information from images and videos.

There are several types of computer vision automation and they are; image classification, object detection, image segmentation and facial recognition automation each with its own unique goal and processes.

The advantages of artificial intelligence (AI) automation are as follows; AI automation can automate repetitive and mundane tasks freeing up human resources for more complex and creative tasks. AI automation can reduce labor costs and improve productivity, leading to cost savings.AI automation can handle large volumes of data and tasks making it ideal for applications that require scalability.

The disadvantages of AI automation are as follows; AI automation can displace human jobs especially those that involves repetitive and mundane tasks. AI automation can lack transparency making it difficult to understand how decisions are made. AI automation can perpetuate bias and discrimination if training data is biased. AI automation can introduce security risks especially if the systems are not designed with security in mind.

The application of AI automation is employed in various industries such as customer service, heath care, finance, manufacturing and others to numerous to mention. AI automation can be used to provide customer support and answer frequently asked questions. AI automation can be used to analyze medical images, diagnose diseases and develop personalized treatment plans. AI automation can be used to detect financial anomalies, predict market trends and automate trading decisions. AI automation can be used to predict equipment failures, optimize production processes and improve product quality.

The future of AI automation will depend on the trends and development in the following technologies; AI automation is expected to become mainstream niche with industries and applications incorporating them in their tasks and processes. Advancements in machine learning will enable more complex and sophisticated AI automation applications. There will be an increased focus on ethics and transparency in AI automation with efforts to address bias and ensure, robust security and accountability. The future AI automation will involve more human collaboration with AI automation systems augmenting human capabilities and freeing up time for more complex, creative and interesting human task and activities.

 

SOURCES:

• Artificial intelligence for dummies by Luca Massaron and John Mueller.
• Artificial intelligence basics: A non-technical introduction by Tom Taulli.
• Artificial intelligence: Principles and applications by Vinod Chandra S.S and Anad Hareendran S.
• Artificial intelligence: A modern approach by Stuart Russell and Peter Norvig.
• Deep learning by Ian Goodfellow, Yoshua Benigio and Aaron Courville.