Five Cognitive Technologies in Shaping the Future

Cognitive Technology is an AI-based, advanced, driver assistance systems. A study suggested by Hackett Group has suggested that 85 percent of the procurement leaders are engaged in the study of cognitive technologies which will evolve the operational programme over the next three-five years. Only 32 percent of all have the strategy to implement the technologies and among them, 25 percent have enough capital and wit to execute the technologies. I am suggesting you the most potential cognitive technologies that can shape up your future.

As AI becomes the foundation in the advancement of our daily lives. IT organizations need to adopt this newly emerged technology in order to hold their position in the market. In service management, to escalate the holistic system it is important to integrate cognitive technologies. This approach offers huge potential benefits in shaping the future of both users and service management. By integrating cognitive technologies, you can offer your users personalized, advanced and conversational experiences that resulted in better and faster outcomes. Just like smartphone users command their assistant in helping various day-to-day tasks, you will get the exact experience with the service desk asking chatbots to help with different activities without any human intervention. This is how you can earn high customer satisfaction.

Big Data Analytics

Big Data Analytics is the process of managing the huge amount of data to draw patterns, trends and actionable insights with the help of advanced technologies and computational functionalities. It is a form of advanced analytics involving intricate applications with predictive models and statistical algorithms, these tasks are done by high-performance analytics systems. This specialised analytics systems and software offer plenty of benefits which include better revenue opportunities, remarking marketing base, advanced customer service, operational efficiency, better competitive edge. Applications based on Big Data analytics applications give rooms to data analysts, predictive modellers, statisticians, other professionals in this fields to analyze the growing volumes of structured transaction data and other forms of data which are not practised by traditional BI and analytics programs. It surrounds an amalgamation of structured and unstructured data. Through sensor connection, these data are collected and connected to IoT (Internet of Things). A host of tools and technologies are used:

• NoSQL databases
• Hadoop
• YARN
• MapReduce
• Spark
• Hbase
• Hive
• Pig

Big data analytics apps include data from internal systems and external sources like weather data on consumers compiled by third-party information services providers. Streaming analytics application has become common in big data environments to do real-time analysis on data that fed into Hadoop systems through stream processing engines, like Spark, Flink and Storm. Intricate analytic systems are integrated with this technology to manage and analyse a large amount of data. Big Data has become extremely beneficial in supply chain analytics. By 2011, big data analytics has begun to take a firm position in organisations and the public eye. With Big Data Hadoop and other related big data technologies had started to emerge around it. Primarily, the Hadoop ecosystem started to take shape and get matured with time. Big Data were primarily the platform of large internet system and electronic commerce companies. Presently has been embraced by retailers, financial services firms, insurers, healthcare organizations, manufacturing, and other potential enterprises. In some cases, Hadoop clusters and NoSQL systems are used at the preliminary level as landing pads and staging areas for data. The whole action is done before it gets loaded into an analytical database to analyse generally in a composed form. When the data is ready it can be analyzed with software that is used for advanced analytics processes. Data mining, predictive analytics, machine learning, deep learning are the typical tools to conclude the whole action. In this spectrum, it is very important to mention that Text Mining and Statistical Analysis Software plays a pivotal role in the big data analytics process. For both ETL and analytics applications, queries are scripted in MapReduce with various programming languages like R, Python, Scala and SQL.

Machine learning:

Machine Learning is an advance continuous process where machines are developed in a way so that it can perform their task as human. These machines are developed using high-tech data to perform their task without any human intervention. Machine Learning is an application of AI which gives a machine the ability to learn and improve programme without any direct and explicit action. It is basically focused on the development of a computer programme which can access data and utilise it to learn for themselves. Its prime goal is to allow the machines to learn automatically without any human assistance. Machine Learning is closely related to computational statistics, with that the study of mathematical optimization the task of machine learning is being done. The task of machine learning can be classified into several broad categories.

• Supervised Learning.
• Semi-supervised learning.
• Unsupervised Machine Learning.
• Reinforcement Machine Learning.

All these classified machine learning categories offer the different shade of task in analysing data and information and take essential decisions:

• The learning algorithm creates a surmised function for making predictions about the output value. The learning algorithms can be compared its output with calculated output and find errors for modification of the model as per the requirement.
• Unsupervised machine learning algorithms cannot rectify the right output rather it can explore data and draws inferences from the dataset for describing hidden structure from unlabelled data.
• The semi-supervised machine learning algorithm is used for both labelled and unlabeled data.
• Reinforcement machine learning algorithms interact with the environment to produce action and discover rewards and errors. Trial and error process is happened to be the most significant feature of this learning. For enabling this process simple reward feedback is essential for learning which action is best which is generally termed as reinforcement signal.

Like Big Data analytics Machine learning also enables to analyse the massive volume of data. It tends to deliver fast and most accurate outcome for identifying beneficial opportunities or managing risk management system. However, it can also require extra time and resource to execute the whole programme properly. It is a very effective process to manage and monitor a huge amount of data and information.

Natural Language Processing (NLP)

Natural Language Processing is to train machines with human intelligence to beget changes in their language and responses to make them more human-like. It actually refers to how we communicate with each other. NLP is defined as the automatic manipulation of natural language by the use of the software. The study of Nature Language Processing has been started more than 50 years ago. It is different from other types of data. Nevertheless, yet after working for so many years the challenge of Natural Language process is not solved in a Mathematical Linguistic journal it was published by a keen scientist: “it is hard from the standpoint of the child, who must spend many years acquiring a language… it is hard for the adult language learner, it is hard for the scientist who attempts to model the relevant phenomena, and it is hard for the engineer who attempts to build systems that deal with natural language input and output. These tasks are hard that Turing could rightly make fluent conversation in natural language the centrepiece of his test for intelligence”.

As machine learning scientists and researchers are interested to work with data, and linguistics can work in the process of NLP. Modern developers suggested that: “the aim of linguistic science is to be able to characterize and explain the multitude of linguistic observations circling around us, in conversations, writing, and other media. Part of that has to do with the cognitive size of how humans acquire, produce and understand language, part of it has to do with understanding the relationship between linguistic utterance and the world, and part of it has to do with understand the linguistic structures by which language communicates”

Artificial Intelligence

AI drives automation of primary tasks with computers to serve as advanced digital assistants. Human intelligence is grounded to sense the environment, to learn from the environment and to process information from the environment. That means AI incorporates:

• Deception of human sense, like touch, taste, sight, smell and hear.
• Deception of human responses: Robotics.
• Deception of learning and processing: Machine learning and deep learning.

Cognitive Computing generally focuses on mimicking human behaviour and working out to solve problems that can be done potentially even better than human intelligence. Cognitive Computing simply supplements the information to make decisions easier than ever. While Artificial Intelligence is responsible to make the decision for their own and minimize the role of humans. The technologies which work behind Cognitive Computing are akin to the technologies behind AI which includes, Deep learning, machine learning, neural networks, NLP, etc. Though Cognitive computing is closely associated with Artificial Intelligence, when their practical use has come into light, they are completely different. AI is defined as “the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach an approximate or definite conclusion) and self-correction”. AI is an umbrella term under which a host of technologies, algorithms, theories and methods enables the computer or any smart device to perform with high-tech technologies with human intelligence. Machine Learning, Robotics are all goes under Artificial Intelligence that allows machines to offer augmented intelligence and can surpass human insight and accuracy. AI tool offers a range of new functionalities in your business. The deep learning algorithms that is integrated with most advanced AI tools. Researchers and marketers believe that the introduction of augmented intelligence has a more neutral connotation that will enable us in understanding that AI is used in a way to improve product and service. AI can be categorized in four categories:

Reactive Machines: IBM’s Deep Blue chess-playing computer has the capacity to identify pieces on the chessboard and predictions accordingly though it cannot access the past experiences to inform future ones. It can manage and analyse the possible moves. Google’s AlphaGO is another example though is designed to work for narrow purposes and cannot be applied to another situation.

Theory of Mind: Though, this types of AI’s are developed in a way so that machines can make individual decisions. Though this AI technology had been developed quite a long back. Presently it does not have any practical use.

Limited Memory: This Artificial Intelligence Technology was developed to perform a task in future in the respect of past experiences. It has the ability to take and give you advanced hints about any important decision regarding your tasks. For example: if you are driving, AI designed navigation system can provide you directly to change the lane to reach your destination.

Self Awareness: AI is developed that can truly have sense and consciousness like that of a human body has. Machines integrated with self-awareness can understand the current state using the information to inter what a third person is feeling.

Process Automation

Process Automation enables to interlink the various functions, process the automation of workflow and have minimal errors. Process automation is the use of technology for business automation. The first step is to start by recognising the processes which need automation. When you have a perfect understanding of the automation process then you should plan the goals for automation. Before you roll the automation, you need to check the loopholes and errors in the process. Here is a list where you can decipher why you need an automation process in your business:

• To Standardize and streamline the processes.
• To solve the process with agility by reducing the cost.
• To develop a better allocation of resources.
• For improving customer experience.
• To improve compliance to regulate and standardise your business processes.
• To provide high employee satisfaction.
• To improve visibility for processing performance.

A set of departments can adopt the business process to automate their process and ease the cycle of a complicated nature.

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