Software Engineering Insights

The latest innovation in AI, known as deep learning, helps AI models learn from a fairly large dataset of examples. This type of learning, however, does not generalize for conditions that were left out or missed out during the training process. The alternative to this is Transfer Learning, which is extensively witnessing its application in the field of Natural Language Processing (NLP).

Every industry and business sector in the world has been making breakthroughs in widespread Artificial Intelligence (AI) implementation. This has been so prevalent that the very landscape of AI has been constantly transforming. The accumulation of so many breakthroughs is ushering in a new stage for AI; referred to popularly as AI 2.0.

The scope of innovation in Artificial Intelligence (AI) is vast and the field of Natural Language Processing (NLP) is setting new benchmarks for it every day. What is redefining the way we approach the completion of tasks through NLP is a novel architecture known as Transformer-based architecture. The dynamic problem solving employed using NLP is enhanced tenfold through this novel architectural model.

Log analysis is an integral part of any software development lifecycle's ultimate success. Developers and engineers use logs to assess what is happening at every layer of a software system and track down the root cause of issues. As the software development process produces a large amount of distributed log data, it is often difficult to analyze it all sufficiently.

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.