Large Language Models (LLMs) have long been at the forefront of artificial intelligence, leveraging vast amounts of textual data to perform tasks ranging from language translation to content generation. These models have laid the foundation for numerous applications, fundamentally transforming the way machines understand and generate human-like text. However, as the landscape of AI continues to evolve, a shift is occurring with the rise of Multimodal LLMs.
The era of guessing your size and hoping for the best when ordering clothes online is becoming a thing of the past. In today’s digital world, shoppers are looking for a more tailored experience, one that mirrors the personal touch of in-store shopping but the convenience of doing it from their own home. This is where the idea of trying on clothes virtually, with the help of smart technology, comes into play.
The introduction of chatbots revolutionized customer and brand interaction. With the ability to mimic conversations and offer instant, digital connection, chatbots made their way into businesses like a wildfire. In fact, Gartner predicts that more than 50% of enterprises will spend more annually on bots and chatbot creation than traditional mobile app development.
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.