These days, an embedded analytics solution can cost six figures. Users are never satisfied, regardless of how much effort is put in. They often express frustration with the complicated user interface or wish for more advanced analytics. It could have been better; however, most customers ended up extracting the data and doing their analyses. A… →

The digital age demands for automation and efficiency in the domain of software and applications. Automating repetitive coding tasks and reducing debugging time frees up programmers’ time for more strategic work. This can be especially beneficial for businesses and organizations that rely heavily on software development. The recently released AI-powered Python notebook Thread addresses the… →

Large Language Models (LLMs) have taken over the Artificial Intelligence (AI) community in recent times. In a Reddit post, a user recently brought attention to the startling quantity of over 700,000 large language models on Hugging Face, which sparked an argument about their usefulness and potential. This article is based on a Reddit thread, and… →

Controlling the language proficiency levels in texts generated by large language models (LLMs) is a significant challenge in AI research. Ensuring that generated content is appropriate for various proficiency levels is crucial for applications in language learning, education, and other contexts where users may not be fully proficient in the target language. Without effective proficiency… →

Large Language Models (LLMs) have made substantial progress in the field of Natural Language Processing (NLP). By scaling up the number of model parameters, LLMs show higher performance in tasks such as code generation and question answering. However, most modern LLMs, like Mistral, Gemma, and Llama, are dense models, which means that during inference, they… →

Large language models (LLMs) have enabled the creation of autonomous language agents capable of solving complex tasks in dynamic environments without task-specific training. However, these agents often face challenges when tasked with broad, high-level goals due to their ambiguous nature and delayed rewards. The impracticality of frequent model retraining to adapt to new goals and… →

The Galileo Luna represents a significant advancement in language model evaluation. It is specifically designed to address the prevalent issue of hallucinations in large language models (LLMs). Hallucinations, or instances where models generate information not grounded in the retrieved context, pose a significant challenge in deploying language models in industry applications. The Galileo Luna is… →