The Human Side of Artificial Intelligence and Analytics

The Human Side of Artificial Intelligence and Analytics

“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.”

— Stephen Hawking told the BBC

In the era of Artificial Intelligence and Machine Learning, will people be better off than they are today? Yes, we do fast production, we save lots of time and energy with the help of AI robots but we lose lots of jobs and its continuously increasing. On the otherside due to increase in such

demand, in R&D sector more creative and better paying jobs are opening which can be done only by human beings.

According to many experts and researchers’ neural technology will amplify human effectiveness but there is a question mark that will it reduce human capabilities, will it affect the human autonomy? The growing Artificial Neural Network Model will help computers to exceed human intelligence and capabilities on tasks such as complex decision-making, reasoning and learning, sophisticated analytic and pattern recognition, visual

acuity, speech recognition , language translation and many more. Easier to use customized features will be offered

to the communities whether it is in

business processes or in vehicles or in agriculture or in securities or in health sectors.

For this all we need is Data, the fuel of AI and ML and Analytics helps us to identify, interpret and communicate the meaningful patterns of the data. The whole process can be parted into several stages according to Gartner Analytic Ascendancy Model. Gartner Analytic Ascendancy Model is divided into four stages of increasing difficulty and value. These are

• Descriptive analytics • Diagnostic analytics • Predictive analytics

• Prescriptive analytics

Predictive Analytics Prescriptive Analytics

Value

Descriptive Analytics Diagnostic Analytics

Difficulty

Fig: Gartner Analytic Ascendancy Model

The amalgamation of analytics and AI is termed as AI analytics. This is subset of business intelligence that helps the data analyst’s capabilities in terms of speed, scale of data that can analyzed and the granularity of the data that can be monitored.

Traditional analytics often involves a high degree of manual labor for data like data pre-processing, visualization, coming up with different hypotheses and applying several statistical techniques. But AI analytics will solve this with no time. It can help in forecasting demand, fraud detection and even business monitoring.

AI Analytics in Banking Sector

Objective: To detect fraud checks in real time during deposit and to minimize manual review of deposited checks.

Aim: To find a solution which will reduce

the workload of the employees, fraud risk and also help the organization in lowering processing cost.

Approach: Collecting all the historical data of past transactions and using AI analytics, a model is prepared to solve the issue.

Solution: An artificial intelligence driven ML solution is developed to cut out potential fraud by analyzing scanned images of handwritten checks. This

technology already has a pre-defined data base which will match the

scanned images and detect if any fraud is there in no time.

According to Mordor Intelligence, the global AI fintech market is predicted to reach $22.6B in 2025, achieving a CAGR of 23.37% between 2020 and 2025.

AI driven Chat Bot:

This is a very new trend in our daily life. We often do calls to customer care for

any silly requirement. But to respond that an organization has to drain employee productivity, morale and operating cost.

To reduce this, AI driven chat box is introduced. It reduces workload, operation costs and helps to responses common questions where possible.

The model is made by collecting several data regarding the customers’ demands, feedbacks and organizations’ motto. It also uses several natural languages to meet all client’s requirements. By identifying similar keywords and finding pattern in the chat it responses accordingly.

PSFK says that 74% of customers prefer chatbots when they are looking for instant answers. With companies that use chatots in retail seen as efficient (47%), innovative(40%) and helpful(36%)

AI and ML automate Point of sale data:

Understanding sales trends and customer preferences is very important for any retail outlet or e-commerce organization to maximize the profit.

By collecting data of different transactions, sales of different products, customers preferences a AI driven model is made. It uses text analytics and natural language processing to categorize data. Th cognitive engine on Microsoft Azure scans and identifies

merchant and transaction information-

including products, retailers, vendors, promotional offers from retail receipts using natural language processing.

By this technique future sales pattern and customers preferences can be predicted.

The AI in retail Market was valued at $1.80 billion in 2020 and is expected to reach $10.90billion by 2026, at a CAGR of 35% over the forecast period 20212026. AI will drive faster business

decisions in marketing, e-commerce, product management and other areas of the business by decreasing the gap from insights to action.

AI driven power system maintenance, cuts costs for electric utility:

It is time consuming to locate such a huge distribution network if any electrical or mechanical fault occurs.

But with the help of technology, it becomes more feasible to locate the

faults. Now a days drones are used to collect the images. AI driven image analytics application that assesses drone captured photos in real time to identify problems.

An optimal cognitive computer vision model has been employed to provide the highest accuracy and ease of implementation to seamlessly scale and accommodate the alerting pipeline.

Improving Patient Insights, Care through AI Data analytics:

Economic stability, education, healthcare system and physical environment are considered to be

important factors in addressing patients’ holistic healthcare needs and outcomes.

By mining the unstructured data found in doctors notes book or from medical

reports an AI-ML solution is made which will identify and analyses which social determinants of health have a

significant impact on a patient’s health. This will help any patients to take proper care at proper time. By this almost 11% of the total population is identified with homelessness and food

insecurity related social determinants.

SUKRITI ROY MBA 2020 - 2022