Software Engineering Insights

Vision AI (also known as Computer Vision) is a field of computer science that trains computers to replicate the human vision system. This enables digital devices (like face detectors, QR Code Scanners) to identify and process objects in images and videos, just like humans do. 

Have you noticed the ‘Smart Compose’ feature in Gmail that gives auto-suggestions to complete sentences while writing an email? This is one of the various use-cases of language models used in Natural Language Processing (NLP).

Ever since the intense scrutiny faced by organizations that developed COVID vaccines, pharmacovigilance has become a hotly discussed area of interest. The practice of pharmacovigilance basically aims to reduce the entry of drugs with adverse side effects into regular circulation. Artificial Intelligence (AI) has been permeating this field in recent years due to the immense potential for automated pharmaceutical discovery that it offers.

AI, a technology that enables computers to replicate human-like thinking and problem-solving, has captured our attention. Meanwhile, Cloud Computing, with its capability to deliver abundant computing resources over the internet, has changed how businesses handle their IT infrastructure.

Today's business world relies heavily on software, which is widely used in enterprise applications and products. As technology evolves rapidly, software development teams are under increasing pressure to deliver solutions that are both faster and of superior quality. They often grapple with issues such as functional issues, security vulnerabilities, and technical debt.

Conversational systems that leverage Artificial Intelligence (AI) have helped automate a wide range of business processes, especially those involving interactions with the customer. Natural Language Processing (NLP) comes into play for a majority of these processes, but it is often hindered by functional hurdles. Reinforcement learning is a method for navigating these hurdles to make NLP-driven business processes more seamless.

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.

Natural Language Processing (NLP) is a pre-eminent AI technology that enables machines to read, decipher, understand, and make sense of human languages. From text prediction and sentiment analysis to speech recognition, NLP is allowing machines to emulate human intelligence and abilities impressively.

The Artificial Intelligence (AI) ecosystem today is geared towards optimizing the capabilities of generative AI across various industries and everyday utility. Generative AI services are utilized for creating endless variations of ad copy, realistic image generation, refining low-quality images, and much more. Setting up the pace of generative AI advancement are Diffusion Models which help develop AI solutions that are pushing the boundaries of innovation.

In the healthcare domain, there are two significant challenges that come into view: the concern of high costs and the ongoing effort to enhance patient experiences. Despite the abundance of data gathered from various sources like health records, insurance claims, medical trials, and interconnected devices, finding practical insights to improve patient outcomes and operational efficiency remains a hurdle.