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Artificial Intelligence and Machine Learning Key to Automated Decision Making System

Prof. D. Mukhopadhyay
There is no clear cut dates available with regard to the birth of Al. However majority of the historians are found to be unanimous that the concept and theory of Al dates back 1956 in one of the Dartmouth Research projects when it was functionally responsible for finding out a problem solving method. The US department of Defense had taken initiative for training the managers on computers to mimic human reasoning. It may be mentioned the Defense Advanced Research Projects (DARPA) had completed the street mapping projects in 1970s and DARPA Produced intelligent personal assistants in 2003 that is long before google, Amazon or Microsoft tackled similar projects and it paved the path for installing automation and formal reasoning that is evidently visible in the computers today. On the other hand, Machine Learning (ML) automates analytical model building. The objectives of taking Al and ML is to take appropriate decisions with the help of the methods from neural networks, Statistics, operations research and Physics for finding hidden insights in data without explicitly programmed where to look and what to conclude.
Artificial Intelligence (Al) is meant for bringing about human-to machine interaction by dint of being intelligent. When a machine is elevated to the status of an intelligent human being, Al can understand any requests, connect data, can reason, observe and plan. On the other hand, Machine Learning (ML) is one the small version of Al. Al can help a business plan, organize and forecast sales revenue. The day to day management of the business entities is efficiently possible. Planning a direct sales promotion is possible and decision making also possible with the help of Al. Homo Sapiens’ ‘ wants are unlimited but the means to take care of such wants are certainly limited in supply and here lies the role of modern management. Management is the enabling technique to obviate wastes and losses of available resources and making optimum utilization of the resources. It needs hardly any mention that Homo sapiens are passing through digital era when the ABCD of the digital technology is essentially to be known or skilled in order to smoothen the journey of day to day technology driven life and earlier the society becomes digital Technology, more it becomes ease of managing the day to day socio-economic affairs. In this context, an honest effort is put on the process of deriving and applying the innovations of 21st Century and those epoch making innovations are none but Artificial Intelligence (Al) and Machine Learning (ML). Al is the big brother of Ml. it creates intelligent machine. Al can create intelligent machine in order to stimulate human thinking capability and behavior whereas ML is an application of Al. In other words, ML is in the back office of Al and ML is none but a subset of Al. Installation of both ofthe innovations are investment prone and survival and sustainability of the society at large invariably depends on how quickly it learns, applies to the cause of scarce economic resources management and makes the technological suitability of the society at large. The experts are of the views that ArtificialIntelligence and Machine Learning are the key success factors having dominance over the so called manual manipulation in resource management.
Moreover, AL and ML shall not only ensure decentralization of decision making process with the assistance of connected intelligence. Enterprises that just started their digital transformation are expected to reap the benefit cost optimization much later compared to those who did start to become AL and ML benefits reapers. Survival and long-term suitability of any business house depends on how quality product or services is made available at reasonable price.It is mentionable that an Executive Post such as Chief Digital Officer(CDO)’s emergence is certainly blissful enabling a significant integration among trade , industry and commerce in general and marketing, finance , human resource, research and development and technology functions in particular attributed with more focus on outcome. It has also influenced and facilitated partnership and collaboration which aims at providing seller’s landscape for efficient functioning of the business undertakings. It is worth mentioning that high possibility of start-ups and specialized vendors in the domains ofpredictive, prescriptive and deep learning are likely to generate immense quantifiable benefits. The acceptance of AI and ML across business functions requires buy-in from people. This is where cohesive and clear communication, supply of platforms to decision makers, and an overall culture of trying and failing ideas become Imagine a smart future! A future where machines are not merely dumb devices but intelligent creations that can work in tandem with human beings. A future that looks remarkably like the robotic utopia in I, Robot (Well, except the homicidal robots!). This future is not merely an imagination but a natural consequence of the two most dynamic technologies of today – Artificial Intelligence and Internet of Things.It is a thrilling experience to imagine that in near future where machines are not simply be devices devoid of sensibility but intelligent too and it is going to work as the substitute of human beings. Creations that can work in tandem with human beings.
The researchers worldwide are of the critical views that the Artificial Intelligent and Machine Learning are expected to be of high demand and collaborative process are sine qua in forthcoming days and industries such as non CYBAGE, Robotics 0.5, Robots3/. Smart Cities Real World Examples.Subsequently, organizations will exhibit more EQ by anticipating customer expectations through better segmentation (personalization) in real time. This involves – giving them (customers) more precise product recommendations, along with more intelligent and faster query and issue resolution through chat-bots powered by voice and NLP processing. All this will help free up enterprises’ support services for more advanced customer service needs.
These enterprises will enable decentralized, yet optimal and consistent decision making for its workforce through connected intelligence. Organizations that have just started their digital transformation journeys or are responding to competitors’ move and customer demands are expected to go after low hanging fruits with isolated projects such as customer sentiment analysis, churn analytics, IoT-enabled tracking solutions, and data management solutions in preparation for their digital 2.0 transformation to leverage the duo for better decision making and execution in the future.Concurrently, organizations that are in the middle of their transformation towards modern infrastructure will be looking for opportunities to recourse testing their digital foundations by moving investments to attack key decision areas/prediction problems for bottlenecked or troubled process flows with a view to embark on projects for better customer targeting and pricing-related areas. The introduction of the Chief Digital Officer’s (CDO) role in last few years has enabled a tighter integration between business, marketing, and technology functions with more focus on outcome based small wins at the back of the existing infrastructure versus earth moving digital transformations. It has also influenced partnership and vendor landscape for enterprises. There is a high possibility of start-ups and specialty vendors in areas of predictive, prescriptive, deep learning, IoT devices, and IoT ready platforms and services showing up in the enterprise list of partners alongside the large infrastructure and technology providers.
To sum up, 21st Century is the age of digital technology and everybody needs training or adequate practice of advanced technology so that necessary transformation of the society takes place.
(The author is FCMA. FCS..)
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