You can use ChatGPT for everything from mathematics, coding, editing, generating campaign ideas, creating reports, and more.  Here are some important use cases of ChatGPT for different functions at the workplace: ChatGPT in human resource management (HR)

Artificial Intelligence (AI) has revolutionized the smartphone experience, enhancing visual, text, and audio capabilities. Apple, Google, Samsung, and Huawei are at the forefront, using AI to push their devices beyond traditional functions. Here’s a closer look at

In a pioneering effort, doctoral students from Oregon State University have joined forces with Adobe researchers to develop a novel and cost-effective training technique to reduce social biases in AI systems. This innovative method, named FairDeDup, short

Today, data has largely replaced humans in driving critical strategic business decisions in enterprises. With data's growing importance, the demand for data experts has risen dramatically in recent years. Companies have a wide range of data-related employment

What is Low-Code Development? Low-code is a modern approach to software development that uses minimal coding and programming languages. Coding may be required, but it is used in very specific areas. Low-code development may require developers with

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing

Quick Preview ChatGPT, the seasoned pro, boasts a massive 570 GB of training data, offering three distinct performance modes and reduced harmful content risk. Enter Llama 2, the new kid on the block, trained by Meta AI

Introduction Large language models (LLMs) are becoming increasingly popular in natural language processing for their superior competence in various applications. Although LLMs demonstrate remarkable capabilities in zero-shot scenarios where a model performs tasks it hasn’t been directly

In the rapidly evolving landscape of AI frameworks, two prominent players have emerged: LlamaIndex and LangChain. Both offer unique approaches to enhancing the performance and functionality of large language models (LLMs), but they cater to the developer

Use case​ The popularity of projects like PrivateGPT, llama.cpp, Ollama, GPT4All, llamafile, and others underscore the demand to run LLMs locally (on your own device). This has at least two important benefits: Privacy: Your data is not sent to a third party,