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.
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.
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.
Artificial Intelligence is resolving real-world, complex challenges at scale. With its transformative capabilities, AI has managed to enter some of the sensitive areas including healthcare, finance, cyber security, etc.
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.
The exponential growth and development that AI technology has showcased in the past two decades have brought several science fiction movies into reality. Statistics suggest that the global AI market is predicted to reach $190 billion by 2025. The overall market includes a wide array of AI applications including Natural Language Processing, Machine Learning, Conversational AI, Robotic Process Automation, Neural Networking, etc. | Source: Statista
The average long-distance traveler cites congestion as the primary hindrance to their travel plan. With advancements in technology, the decision-making process that guides route planning has become much more streamlined. Artificial Intelligence (AI) backed route optimization systems give travelers the shortest and most congestion-free routes upon user request.
Out of all the stages of the supply chain, logistics deals with the most unpredictable issues. These include factors like weather patterns, fuel prices, and last-mile delivery charges. The gradual adoption of demand forecasting with Artificial Intelligence (AI) is helping the logistics sector to alleviate some of these uncertainties.