Today, Artificial Intelligence (AI) has found a significant place in our lives and across a broad range of industries and businesses. But most of us, including industry stakeholders, have a very vague understanding of how AI systems make the decisions that they do. That is where Explainable AI (XAI) comes in handy, producing transparent and detailed explanations for the way AI functions.
Healthcare organizations looking to optimize patient outcomes are increasingly adopting Electronic Medical Records (EMR) software. In fact, EMR is now virtually an integral part of healthcare and a widely agreed-upon standard. Taking full advantage of this technology requires proper EMR integrations with third-party ancillary platforms and systems.
The impact of the various applications of AI in automation testing is well known. Moreover, AI is widely accepted as an essential accompaniment for the seamless delivery of any software product these days.
Businesses often need to analyze highly subjective data such as customer feedback, reviews, and recommendations to aid in their brand decision-making. But simply automating data analysis leads to the nuances of this data being overlooked. Sentiment analysis with Machine Learning (ML) models provides a more comprehensive solution to this problem.
User experience (UX) designers rely on a variety of research methods and analytics to understand what the end-user prefers in the design of a software solution or product. Ethnographic research is a tool with great potential that UX designers are finding to be extremely insightful in discovering user preferences and pain points regarding product design.
Automotive brands need to make their customer journey more personalized and innovative to maintain a competitive edge in the market. Artificial Intelligence (AI)-based chatbots have become the go-to technological innovation for these brands to make the end-user experience a notch above the rest.
Referring to some of the most pivotal technologies as mere buzzwords is a disservice to innovation. The Internet of Things (IoT) is one such domain of technologies most people tend to refer to, without fully understanding its nuances or its technical aspects.
When handling voluminous data that is highly sensitive, it is always preferable to group it into categories or classes. That is primarily where classification algorithms make themselves useful. Classification algorithms are one of the most widely implemented classes of supervised machine learning algorithms.
There are Yottabytes of sensitive data being generated from the interfacing of humans with machines. For cost-effective and optimal enrichment of this data, Machine Learning (ML) algorithms are our best bet. One of the most reliable categories of ML algorithms is clustering algorithms, irrespective of the complexity of data.