Optical Character Recognition (OCR) is a technology that transforms images of text into editable and searchable data. In the modern digital era, OCR becomes a prevalent tool, but it often presents challenges for users due to its complex coding. Developers and researchers often find it difficult making it work smoothly for them.
To address these challenges,…
Language models such as GPT-3 have demonstrated impressive general knowledge and understanding. However, they have limitations when required to handle specialized, niche topics. Therefore, a deeper domain knowledge is necessary for effectively researching specific subject matter. This can be equated to asking a straight-A high school student about quantum physics. They might be smart, but…
In the field of Natural Language Processing (NLP), optimizing the Retrieval-Augmented Generation (RAG) pipeline often presents a significant challenge. Developers strive to strike the right balance among various components such as large language models (LLMs), embeddings, query transformations, and re-rankers in order to achieve optimal performance. With a lack of effective guidance and user-friendly tools,…
Open foundation models like BERT, CLIP, and Stable Diffusion signify a new era in the technology space, particularly in artificial intelligence (AI). They provide free access to model weights, enhancing customization, and accessibility. While this development brings benefits to innovation and research, it also introduces fresh risks and potential misuse, which has initiated a critical…
Machine Learning (ML) and Artificial Intelligence (AI) are fields that have made significant progress due to the use of larger neural network models and training these models on massive data sets. This progression has occurred through data and model parallelism techniques and pipelining methods, which distribute computational tasks across multiple devices at the same time.
Despite…
Researchers from several esteemed institutions, including DeepWisdom, have launched a groundbreaking tool for data science problem-solving called the Data Interpreter. This solution leverages Large Language Models (LLMs) to address intricate challenges in the field of data science, marking a novel approach to navigating the vast and ever-changing data world. The Data Interpreter was conceived through…
GitHub, a popular platform that provides hosting for software development and version control using Git, recently launched its code scanning autofix feature, a significant development in the world of digital security. Available to all GitHub Advanced Security customers, the new feature merges GitHub's Copilot real -time support with the analytical abilities of its semantic code…
Optical flow estimation aims to analyze dynamic scenes in real-time with high accuracy, a critical aspect of computer vision technology. Previous methods of attaining this have often stumbled upon the problem of computational versus accuracy. Though deep learning has improved the accuracy, it has come at the cost of computational efficiency. This issue is particularly…
Reinforcement Learning from Human Feedback (RLHF) is a technique that improves the alignment of Pretrained Large Language Models (LLMs) with human values, enhancing their usefulness and reliability. However, training LLMs with RLHF is a resource-intensive and complex task, posing significant obstacles to widespread implementation due to its computational intensity.
In response to this challenge, several methods…
OpenAI’s development of GPT-5 has garnered considerable interest in the tech community and business sector due to its predicted enhancements over the previous iteration, GPT-4. Notably, GPT-4 made considerable strides toward human-like communication, logical reasoning, and multimodal input processing.
As revealed in Lex Fridman's podcast with Sam Altman, GPT-5 is expected to further advance these…
Enhancing Large Language Models (LLMs) capabilities remains a key challenge in artificial Intelligence (AI). LLMs, digital warehouses of knowledge, must stay current and accurate in the ever-evolving information landscape. Traditional ways of updating LLMs, such as retraining or fine-tuning, are resource-intensive and carry the risk of catastrophic forgetting, which means new learning can override valuable…