Federated Learning (FL) is a technique that allows Machine Learning models to be trained on decentralized data sources while preserving privacy. This method is especially helpful in industries like healthcare and finance, where privacy issues prevent data from being centralized. However, there are big problems when trying to include Homomorphic Encryption (HE) to protect the…
Chain-of-thought (CoT) prompting has emerged as a popular technique to enhance large language models’ (LLMs) problem-solving abilities by generating intermediate steps. Despite its better performance in mathematical reasoning, CoT’s effectiveness in other domains remains questionable. Current research is focused more on mathematical problems, possibly overlooking how CoT could be applied more broadly. In some areas,…
Artificial intelligence (AI) has given rise to powerful models capable of performing diverse tasks. Two of the most impactful advancements in this space are Retrieval-Augmented Generation (RAG) and Agents, which play distinct roles in improving AI-driven applications. However, the emerging concept of Agentic RAG presents a hybrid model that utilizes the strengths of both systems.…
Transformer models have revolutionized sequence modeling tasks, but their standard attention mechanism faces significant challenges when dealing with long sequences. The quadratic complexity of softmax-based standard attention hinders the efficient processing of extensive data in fields like video understanding and biological sequence modeling. While this isn’t a major concern for language modeling during training, it…
Artificial intelligence has significantly enhanced complex reasoning tasks, particularly in specialized domains such as mathematics. Large Language Models (LLMs) have gained attention for their ability to process large datasets and solve intricate problems. The mathematical reasoning capabilities of these models have vastly improved over the years. This progress has been driven by advancements in training…
Whale species produce a wide range of vocalizations, from very low to very high frequencies, which vary by species and location, making it difficult to develop models that automatically classify multiple whale species. By analyzing whale vocalizations, researchers can estimate population sizes, track changes over time, and help develop conservation strategies, including protected area designation…
Machine Learning in Membrane Science:ML significantly transforms natural sciences, particularly cheminformatics and materials science, including membrane technology. This review focuses on current ML applications in membrane science, offering insights from both ML and membrane perspectives. It begins by explaining foundational ML algorithms and design principles, then a detailed examination of traditional and deep learning approaches…
Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, computer vision, and data analytics but have significant computational challenges. Specifically, as models grow larger, they require vast computational resources to process immense datasets. Techniques such as backpropagation are essential for…
The release of the FC-AMF-OCR Dataset by LightOn marks a significant milestone in optical character recognition (OCR) and machine learning. This dataset is a technical achievement and a cornerstone for future research in artificial intelligence (AI) and computer vision. Introducing such a dataset opens up new possibilities for researchers and developers, allowing them to improve…
Large language models (LLMs) are increasingly used in domains requiring complex reasoning, such as mathematical problem-solving and coding. These models can generate accurate outputs in several domains. However, a crucial aspect of their development is their ability to self-correct errors without external input, intrinsic self-correction. Many LLMs, despite knowing what is necessary to solve complex…