iAsk Ai has quickly become a leader in AI search. iAsk Ai’s search engine is powered by iAsk Pro, their latest model that has outperformed top competitors like OpenAI’s GPT-4o, Anthropic’s Claude 3.5 Sonnet, and Google’s Gemini Pro, as shown by its record-breaking results on the MMLU Pro benchmark test. In less than two years,…
Text-to-video generation is rapidly advancing, driven by significant developments in transformer architectures and diffusion models. These technologies have unlocked the potential to transform text prompts into coherent, dynamic video content, creating new possibilities in multimedia generation. Accurately translating textual descriptions into visual sequences requires sophisticated algorithms to manage the intricate balance between text and video…
Many modern applications, such as recommendation systems, image and video search, and natural language processing, rely on vector representations to capture semantic similarity or other relationships between data points. As datasets grow, traditional database systems need help handling vector data efficiently, leading to slow query performance and scalability issues. These limitations create the need for…
Sarcasm detection is a critical challenge in natural language processing (NLP) because of sarcastic statements’ nuanced and often contradictory nature. Unlike straightforward language, sarcasm involves saying something that appears to convey one sentiment while implying the opposite. This subtle linguistic phenomenon is difficult to detect because it requires understanding beyond the literal meaning of words,…
3D computer vision has gained immense traction recently due to its robotics, augmented reality, and virtual reality applications. These technologies demand an extensive amount of high-quality 3D data to function effectively. However, acquiring such data is inherently complex, requiring specialized equipment, expert knowledge, and significant time investments. Unlike 2D data, which is relatively easier to…
Retrieval-augmented generation (RAG) has emerged as a prominent application in the field of natural language processing. This innovative approach involves breaking down large documents into smaller, manageable text chunks, typically limited to around 512 tokens. These bite-sized pieces of information are then stored in a vector database, with each chunk represented by a unique vector…
Document ranking remains one of the most important issues in information retrieval & natural language processing development. Effective document retrieval and ranking are highly important in enhancing the performance of search engines, question-answering systems, and Retrieval-Augmented Generation (RAG) systems. Traditional ranking models often need help finding a good balance between the precision of results and…
Digital Twin (DT) technology is becoming more and more popular as a method that gives Internet of Things (IoT) devices dynamic topology mapping and real-time status updates. However, there are difficulties in deploying DT in industrial IoT networks, especially when significant and dispersed data support is required. This frequently results in the creation of data…
The main focus of existing Multimodal Large Language Models (MLLMs) is on individual image interpretation, which restricts their ability to tackle tasks involving many images. These challenges demand models to comprehend and integrate information across several images, including Knowledge-Based Visual Question Answering (VQA), Visual Relation Inference, and Multi-image Reasoning. The majority of current MLLMs struggle…
This paper introduces Show-o, a unified transformer model that integrates multimodal understanding and generation capabilities within a single architecture. As artificial intelligence advances, there’s been significant progress in multimodal understanding (e.g., visual question-answering) and generation (e.g., text-to-image synthesis) separately. However, unifying these capabilities in one model remains a challenge. Show-o addresses this by innovatively combining…