As the enterprises across industries are growing, there is a paradigm shift in terms of technology used by them. Artificial Intelligence (AI) is one such buzzing term in the computing industry and is all set to revolutionize various industry sectors. AI is defined as the system that combines computer science and neuroscience to compute and analyse data so that it can act in a way similar to the human brain. Cognitive capabilities could be learnt with the help of an expert (supervised) or on its own (unsupervised). It includes various types of hardware, machine learning software, AI platforms, and services involved in cognitive computing of data in the form of image, video, speech, text, computer logs, big data, and sensor data. An increasing demand for the AI technology is fuelled by affective computing, cognitive computing, virtual reality, perceptual interface, affective agents, augmented reality, facial recognition, emotion detection and recognition, and chat bots service. Due to its vast application areas, the AI technology has occupied the main stream discussion among industry experts and researchers. As a result, several enterprises have increased their R&D investments in studying AI application areas.

The major challenge for any business today is the gap between data collection, insights, and action; and the AI technology helps businesses to bridge this gap in real time. Data generated across the organization by natural human language, machines, and business processes is increasing rapidly. At the same time, the volume of structured and unstructured data is also growing at an exponential rate. Machine Learning (ML) and Natural Language Processing (NLP) are the integral parts of the AI technology to help businesses to gain valuable insights hidden in unstructured data, analyze the information, and then find the relevant answers to make more confident and effective business decisions that impact their performance and revenue. With the advancement in AI technology hardware manufacturers are able to replace lager hardware devices with smaller, efficient, and powerful neuromorphic chips-based systems. These hardware solutions includes MPUs, GPUs and FPGAs that are being widely used to implement the machine learning algorithms. Moreover, AI applications such as computer vision and predictive analytics requires a large volume of data to train, test, and validate, which may present the storage challenges. Hardware vendors are coming up with low latency and high performance memory solutions for theses AI applications.

The innovative technologies including IoT, advanced analytics, and big data are giving a new leash to the AI technology, and are widely used with many applications, such as image recognition, speech recognition, face recognition, and risk and fraud detection. In addition, the proliferation of large and multidimensional data sets, an increasing focus on solving real-time problems by analysing data, and an increasing need for transforming the business processes from reactive to proactive are some of the major factors driving the adoption of the AI technology across the globe. Owning to its vast applications from robotics to forecast and decision-making, the demand for AI-powered solutions is expected to increase in various industry verticals including BFSI; manufacturing; retail; government and defense; energy and utilities; healthcare and life sciences; telecommunication. In the BFSI vertical, application areas for the AI technology include fraud and risk management, customer segmentation, sales and marketing campaign management, investment prediction, and digital assistance. In the healthcare and life sciences vertical, the AI technology finds its application in disease identification and diagnosis, image analytics, personalized treatment, and drug discovery/manufacturing. Major AI application areas in the manufacturing vertical include predictive maintenance, revenue estimation, demand forecasting, and supply chain management.

The major use cases for AI technology prevailing in the telecommunication industry includes customer analytics, network optimization, and network security. In customer analytics, the telecom vendors can understand customer behavior pattern, analyse Call Detail Records (CDRs), and customer churn prediction from the customer data generated every second, which includes plan details, demographic details, services consumed, and spending pattern. AI powered solutions help the telecom vendors to use advanced analytics on customer data to generate a real-time view of customer preferences. Moreover, AI powered solutions enable telecom operators to acquire a deeper understanding of users and improving their experience at every touchpoint through high performance services, fast feedback, customized offerings, and thus make near real-time and fact-based decisions.

As the enterprises across industries are growing, there is a paradigm shift in terms of technology used by them. Artificial Intelligence (AI) is one such buzzing term in the computing industry and is all set to revolutionize various industry sectors. AI is defined as the syste ....see more

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