Applications of AI

AI is an important business tool that cannot be left to bottom-up whimsy. Leading organizations are already devoting considerable financial resources to AI, and necessary skills and experience are too rare to assume that they will be scattered around the organization with little coordination or collaboration. Just as e-commerce led to Digital Officers and groups to support online commerce it is believed that AI will engender new Centers of Excellence (CoE), and new roles within them. The applications of AI are plenty and few important ones can be listed as follows.

a. Natural Language Processing (NLP): It is an area of AI concerned with the interaction between the computer and human language. In simple words, NLP is a software that enables machines to process human language, thus making it possible for humans to communicate effectively with machines. NLP uses text analytics to understand sentence structure, meaning & intent through machine learning and statistical methods. It is used in data mining, security and fraud detection.

b. Natural Language Generation (NLG): It is a sub-discipline of AI that converts all types of data into human-readable text. This software converts data into text at a rapid pace, enabling machines to communicate effectively. Currently it is used in customer service, automating business intelligence insights, product description, and financial reports.

c. Machine Learning (ML): The fundamental goal of machine learning is to develop intelligent machines that can teach themselves and improve from data without explicit programming or any other human interference. This latest technology is a top priority for most organization and businesses are now investing in it right now to redefine their business edge.

d. Robotic Process Automation (RPA): RPA is the automation of rule-based tasks. It is possible because of scripts and methods that mimic the way humans perform tasks. Enterprises are currently employing RPA in business areas where it is too expensive and inefficient to employ human workers.

e. Speech Recognition: This is a software that is increasingly being utilized by mobile phones and applications. It facilitates the transcription and transformation of human speech into a form that can be easily understood by a computer i.e., it allows the application/program which uses it to convert human language and phrases into data. Organizations can use speech recognition for voice dialing, voice search, call routing, voice search, and speech-to-text processing.

f. Virtual Agents: Siri, Cortana & Alexa are few examples of virtual agents with whom one can interact. To define, they are computer programs or agents capable of interacting with humans; it can range from chatbots to other advanced systems. Virtual agents are widely used in smart home manager, customer service, and support. These agents are able to make intelligent conversation, can respond to queries and work 24/7.

g. Hardware Integrated with AI: The addition of AI technologies in hardware has sped the next-gen applications. It means new graphics and central processing units and devices tailored to execute AI oriented tasks. At present they are making an impact on Deep Learning Applications.

h. Business Decision Management (BDM): BDM framework includes everything about the design, building, and management of automated systems for decision making. Automating decision making would aid organizations to make consistent, efficient and information-driven decisions. Organizations use it to manage their customer, employee and supplier interaction with a view to enhance operational decisions. Banking, Insurance and financial sectors are examples of organizations using software to aid in their decision-making process.

i. Biometrics: It enables natural interaction between machines and humans. Biometrics & Biometric data comprises of things like fingerprints, voice recognition, retinal pattern, and face structure and thus is unique to each individual. It involves identifying, measuring and analyzing the body’s physical structure, form, and behavior.

j. Deep Learning: Deep learning is a subtype of machine learning. It mimics the way the human brain works and uses artificial neural networks for processing data and aids in decision making. Information passes through this artificial network altering their structure based on the input and output. The learning thus comes here from observing large data. Deep learning is currently used for recognizing patterns and classifying apps that are compatible with large data.

k. Neuromorphic Computing (NC): NC is a highly interdisciplinary emerging technology encompassing material science, mathematics, electronics engineering, science, computer science and neuroscience to create hardware for artificial neural systems whose functioning is similar to the architecture of the neural networks in the human brain. NC is based on unconventional computing and is not based on the traditional von-neumann architecture as encountered in the present generation computers. Machine learning currently is performed on prefabricated chips whereas brain dynamically programs the interconnections between neurons. Real time on-chip learning and on site prediction is the need of the hour which can be achieved through neuromorphic engineering.

Based on the above presented brief activities and applications it can be noted that AI is one of the future disciplines which are expected to re-shape our lives. In this era, software writes itself and machines learn. Soon, hundreds of billions of devices will be infused with intelligence. AI will revolutionize every industry. Deep learning has become an increasingly important part of AI and with the accelerated penetration of use witnessed in business and research, it has started to inspire changes that affect other systems and processes as well as business requirements in software development.