Large Language Models (LLMs) have become increasingly important in cybersecurity, particularly in their application to secure coding practices. As these AI-driven models can generate human-like text, they are now being utilized to detect and mitigate security vulnerabilities in software. The primary goal is to harness these models to enhance the security of code, which is…
Accurately transcribing spoken language into written text is becoming increasingly essential in speech recognition. This technology is crucial for accessibility services, language processing, and clinical assessments. However, the challenge lies in capturing the words and the intricate details of human speech, including pauses, filler words, and other disfluencies. These nuances provide valuable insights into cognitive…
Artificial intelligence is rapidly advancing, with a significant focus on improving models that process and interpret complex datasets, particularly time series data. This type of data involves sequences of data points collected over time and is critical in various fields, including finance, healthcare, and environmental science. The ability to accurately predict and classify time series…
Label-efficient segmentation has emerged as a crucial area of research, particularly in point cloud semantic segmentation. While deep learning techniques have advanced this field, the reliance on large-scale datasets with point-wise annotations remains a significant challenge. Recent methods have explored weak supervision, human annotations, and techniques such as perturbed self-distillation, consistency regularization, and self-supervised learning…
Understanding social interactions in complex real-world settings requires deep mental reasoning to infer the underlying mental states driving these interactions, known as the Theory of Mind (ToM). Social interactions are often multi-modal, involving actions, conversations, and past behaviors. For AI to effectively engage in human environments, it must grasp these mental states and their interrelations.…
Artificial intelligence (AI) has increasingly relied on vast and diverse datasets to train models. However, a major issue has arisen regarding these datasets’ transparency and legal compliance. Researchers and developers often use large-scale data without fully understanding its origins, proper attribution, or licensing terms. As AI continues to expand, these data transparency and licensing gaps…
Artificial intelligence, particularly the development of large language models (LLMs), has been rapidly advancing, focusing on improving these models’ reasoning capabilities. As AI systems are increasingly tasked with complex problem-solving, it is crucial that they not only generate accurate solutions but also possess the ability to evaluate and refine their outputs critically. This enhancement in…
Artificial intelligence (AI) has witnessed rapid advancements over the past decade, with significant strides in NLP, machine learning, and deep learning. Among the latest and most notable developments is the release of Llama-3.1-Storm-8B by Ashvini Kumar Jindal and team. This new AI model represents a considerable leap forward in language model capabilities, setting new benchmarks…
CausalLM has released miniG, a groundbreaking language model designed to bridge the gap between performance & efficiency. This innovative model stands out for its powerful capabilities and compact design, making advanced AI technology more accessible to a wider audience. As industries increasingly seek cost-effective and scalable AI solutions, miniG emerges as a transformative tool, setting…
The success of ANNs stems from mimicking simplified brain structures. Neuroscience reveals that neurons interact through various connectivity patterns, known as circuit motifs, which are crucial for processing information. However, most ANNs only model one or two such motifs, limiting their performance across different tasks—early ANNs, like multi-layer perceptrons, organized neurons into layers resembling synapses.…