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Large Language Models (LLMs) built on the Transformer architecture have recently attained important technological milestones. The remarkable skills of these models in comprehending and producing writing that resembles that of a human have had a significant impact on a variety of Artificial Intelligence (AI) applications. Although these models function admirably, there are many obstacles to…
Collecting, monitoring, and maintaining a web data pipeline can be daunting and time-consuming when dealing with large amounts of data. Traditional approaches’ struggles can compromise data quality and availability with pagination, dynamic content, bot detection, and site modifications. Building an in-house technical staff or outsourcing to a low-cost nation are two common options for companies…
Transformer-based LLMs like ChatGPT and LLaMA excel in tasks requiring domain expertise and complex reasoning due to their large parameter sizes and extensive training data. However, their substantial computational and storage demands limit broader applications. Quantization addresses these challenges by converting 32-bit parameters to smaller bit sizes, enhancing storage efficiency and computational speed. Extreme quantization,…
In robotics, understanding the position and movement of a sensor suite within its environment is crucial. Traditional methods, called Simultaneous Localization and Mapping (SLAM), often face challenges with unsynchronized sensor data and require complex computations. These methods must estimate the position at discrete time intervals, making it difficult to handle data from various sensors that…
Large Language Models (LLMs) like GPT-4 exhibit impressive capabilities in text generation tasks such as summarization and question answering. However, they often produce “hallucinations,” generating content that is factually incorrect or contextually irrelevant. The problem is particularly acute when the LLMs are provided with correct facts but still produce inaccurate outputs, termed “contextual hallucinations.” These…
The Retrieval-Augmented Generation (RAG) pipeline includes four major steps— generating embeddings for queries and documents, retrieving relevant documents, analyzing the retrieved data, and generating the final response. Each of these steps. requires separate queries and tools, resulting in a cumbersome, time-consuming, and potentially error-prone process. For example, generating embeddings might involve using a machine learning…
Large Language Models (LLMs) have become critical tools in various domains due to their exceptional ability to understand and generate human language. These models, which often contain billions of parameters, require extensive computational resources for training and fine-tuning. The primary challenge lies in efficiently managing the memory and computational demands to make these models accessible…
A fundamental topic in computer vision for nearly half a century, stereo matching involves calculating dense disparity maps from two corrected pictures. It plays a critical role in many applications, including autonomous driving, robotics, and augmented reality, among many others. According to their cost-volume computation and optimization methodologies, existing surveys categorize end-to-end architectures into 2D…
In an effort to track its advancement towards creating Artificial Intelligence (AI) that can surpass human performance, OpenAI has launched a new classification system. According to a Bloomberg article, OpenAI has recently discussed a five-level framework to clarify its goal for AI safety and future improvements. Level 1: Conversational AI AI programs such as ChatGPT…
Computer vision enables machines to interpret & understand visual information from the world. This encompasses a variety of tasks, such as image classification, object detection, and semantic segmentation. Innovations in this area have been propelled by developing advanced neural network architectures, particularly Convolutional Neural Networks (CNNs) and, more recently, Transformers. These models have demonstrated significant…