use case of AI/ML

Artificial Intelligence (AI) is the emulation of human intelligence process by machines be it a computer or an IOT device. The process involves learning (extracting meaningful insights and patterns), predicting (use the gathered information to help devices make plausible future decisions) and self-correcting (the art of getting more efficient every day).

This is made possible by the three major wings of Artificial Intelligence – Machine Learning (ML), Natural Language Processing (NLP) and Deep Learning (Neural Networks).

AI has established itself in every business sphere around the world -encompassing everything from Rule-based machine learning to visual classification. Its applications range from object recognition to preventing high-end cybersecurity threats.

As we are not acquainted with the denotation of Artificial Intelligence (AI), let’s analyse few cases where AI has enabled businesses to secure their core values

Customer Relations via NLP and automation:

 Maintaining and continuously improving Customer Relations has always been the core value of any businesses. With the advent of Artificial Intelligence and automation, multiple new avenues have opened to gain more and more traction, personalization in customer reengagement and overall bringing a great impact in overall business revenue. (anchor text ?)

Sending queries via forms or emails and then waiting for your queries or issues to be resolved is no longer the face of the game. Instant Messaging Platforms, Chatbots are the latest trend in the field of Customer Relations. Machine Learning and AI have made businesses more accessible to users on every platform be it Online or Offline. Artificial Intelligence uses Natural Language Processing (NLP) techniques to interpret words, data and apply contextual and reasoning algorithms to aid, run surveys and at the same time generate useful insights and provide relevant data so that analysts can focus on growing data needs and what main attributes need to be tracked.

AI-backed Data Analytics:

Artificial Intelligence has already revolutionized the data-processing workflows and has been delivering actionable insights for quite some time. It helps the employees by presenting before them trends, patterns, relationships, and anomalies which serve as major decision-driving metrics.

It has shifted the businesses’ concept of data from Describing to Monetizing. Ai is not only classifying the data in a presentable form for users but also have been giving business the directions to turn new found knowledge or insights into visible profits. It can help in expanding the business verticals or the type of customers you have been serving. It has also been contributing to making company workflows more efficient and cost-effective.

Sentimental Analysis and textual Analysis play a major role in this data gathering. Parallel Dots has been contributing in this arena for a long period of time (Anchor Text). Their powerful APIs can comprehend a huge amount of unstructured text and visual data to empower various products and companies. It also empowers market researchers to automate mundane and time-exhausting data-analysis tasks and provide them with insightful and actionable data. Currently, Parallel Dots is providing 9 different APIs for various text analysis tasks and changing the way text analysis has been handled to date. They also provide AI consulting services to explore the “what, why, how and who about deploying AI in businesses.

Artificial Intelligence in Agriculture

The industry is turning to Artificial Intelligence technologies to help yield healthier crops, control pests, monitor soil, and growing conditions, organize data for farmers, help with the workload, and improve a wide range of agriculture-related tasks in the entire food supply chain.

Use of weather forecasting: With the change in climatic condition and increasing pollution it’s difficult for farmers to determine the right time for sowing seed, with help of Artificial Intelligence farmers can analyze weather conditions by using weather forecasting which helps they plan the type of crop can be grown and when should seeds be sown.

Soil and crop health monitoring system: The type of soil and nutrition of soil plays an important factor in the type of crop is grown and the quality of the crop. Due to increasing, deforestation soil quality is degrading and it’s hard to determine the quality of the soil.

A German-based tech start-up PEAT has developed an AI-based application called Plantix that can identify the nutrient deficiencies in soil including plant pests and diseases by which farmers can also get an idea to use fertilizer which helps to improve harvest quality. This app uses image recognition-based technology. The farmer can capture images of plants using smartphones. We can also see soil restoration techniques with tips and other solutions through short videos on this application.

Similarly, Trace Genomics is another machine learning-based company that helps farmers to do a soil analysis to farmers. Such type of app helps farmers to monitor soil and crop’s health conditions and produce healthy crops with a higher level of productivity.

Analyzing crop health by drones: SkySqurrel Technologies has brought drone-based Ariel imaging solutions for monitoring crop health. In this technique, the drone captures data from fields and then data is transferred via a USB drive from the drone to a computer and analyzed by experts.