Artificial intelligence (AI) is focused on developing systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding. These technologies have various applications across various industries, including healthcare, finance, transportation, and entertainment, making it a vital area of research and development. A significant challenge in AI is…
Artificial intelligence’s large language models (LLMs) have become essential tools due to their ability to process and generate human-like text, enabling them to perform various tasks. These models rely heavily on high-quality instruction datasets for fine-tuning, which enhances their ability to understand and follow complex instructions. The success of LLMs in various applications, from chatbots…
Large language models (LLMs) face a significant challenge in accurately representing uncertainty over the correctness of their output. This issue is critical for decision-making applications, particularly in fields like healthcare where erroneous confidence can lead to dangerous outcomes. The task is further complicated by linguistic variances in freeform generation, which cannot be exhaustively accounted for…
NVIDIA has recently unveiled the Nemotron-4 340B, a groundbreaking family of models designed to generate synthetic data for training large language models (LLMs) across various commercial applications. This release marks a significant advancement in generative AI, offering a comprehensive suite of tools optimized for NVIDIA NeMo and NVIDIA TensorRT-LLM and includes cutting-edge instruct and reward…
As AI-generated data increasingly supplements or even replaces human-annotated data, concerns have arisen about the degradation in model performance when models are iteratively trained on synthetic data. Model collapse refers to this phenomenon where a model’s performance deteriorates significantly when trained on synthesized data generated using the model. This problem is significant because it hinders…
A wide variety of areas have demonstrated excellent performance for large language models (LLMs), which are flexible tools for language generation. The potential of these models in medical education, research, and clinical practice is not just immense, but transformative, offering a promising future where natural language serves as an interface. Enhanced with healthcare-specific data, LLMs…
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…