Information Retrieval (IR) systems for search and recommendations often utilize Learning-to-Rank (LTR) solutions to prioritize relevant items for user queries. These models heavily depend on user interaction features, such as clicks and engagement data, which are highly effective for ranking. However, this reliance presents significant challenges. User Interaction data can be noisy and sparse, especially…
While writing the code for any program or algorithm, developers can struggle to fill gaps in incomplete code and often make mistakes while trying to fit new pieces into existing code snippets or structures. These challenges arise from the difficulty of fitting the latest code with the prior and following parts, especially when the broader…
DeepSwap DeepSwap is an AI-based tool for anyone who wants to create convincing deepfake videos and images. It is super easy to create your content by refacing videos, pictures, memes, old movies, GIFs… You name it. The app has no content restrictions, so users can upload material of any content. Besides, you can get a 50%…
Large language models (LLMs) have emerged as powerful tools capable of performing complex tasks beyond text generation, including reasoning, tool learning, and code generation. These advancements have sparked significant interest in developing LLM-based language agents to automate scientific discovery processes. Researchers are exploring the potential of these agents to revolutionise data-driven discovery workflows across various…
In the rapidly evolving landscape of artificial intelligence, the quality and quantity of data play a pivotal role in determining the success of machine learning models. While real-world data provides a rich foundation for training, it often faces limitations such as scarcity, bias, and privacy concerns. These challenges can hinder the development of accurate and…
In today’s tech-driven world, data science and machine learning are often used interchangeably. However, they represent distinct fields. This article explores the differences between data science vs. machine learning, highlighting their key functions, roles, and applications. What is Data Science? Data science is the practice of extracting insights from large datasets. It leverages techniques from…
Advanced Micro Devices (AMD) has made a bold move in the competitive AI hardware market by launching its new MI325x AI chip, a powerful accelerator aimed squarely at rivaling Nvidia’s latest Blackwell series. The new chip, announced on October 10, 2024, marks AMD’s latest effort to expand its share in the lucrative artificial intelligence computing…
The field of multimodal artificial intelligence (AI) revolves around creating models capable of processing and understanding diverse input types such as text, images, and videos. Integrating these modalities allows for a more holistic understanding of data, making it possible for the models to provide more accurate and contextually relevant information. With growing applications in areas…
Developing therapeutics is costly and time-consuming, often taking 10-15 years and up to $2 billion, with most drug candidates failing during clinical trials. A successful therapeutic must meet various criteria, such as target interaction, non-toxicity, and suitable pharmacokinetics. Current AI models focus on specialized tasks within this pipeline, but their limited scope can hinder performance.…
Problem Addressed ColBERT and ColPali address different facets of document retrieval, focusing on improving efficiency and effectiveness. ColBERT seeks to enhance the effectiveness of passage search by leveraging deep pre-trained language models like BERT while maintaining a lower computational cost through late interaction techniques. Its main goal is to solve the computational challenges posed by…