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.…
Large Language Models (LLMs) have gained significant prominence in recent years, driving the need for efficient GPU utilization in machine learning tasks. However, researchers face a critical challenge in accurately assessing GPU performance. The commonly used metric, GPU Utilization, accessed through nvidia-smi or integrated observability tools, has proven to be an unreliable indicator of actual…
One of the core challenges in semilocal density functional theory (DFT) is the consistent underestimation of band gaps, primarily due to self-interaction and delocalization errors. This issue complicates the prediction of electronic properties and charge transfer mechanisms. Hybrid DFT, incorporating a fraction of exact exchange energy, offers improved band gap predictions but often requires system-specific…
A significant challenge in AI-driven game simulation is the ability to accurately simulate complex, real-time interactive environments using neural models. Traditional game engines rely on manually crafted loops that gather user inputs, update game states, and render visuals at high frame rates, crucial for maintaining the illusion of an interactive virtual world. Replicating this process…
Large-scale language models have made significant progress in generative tasks involving multiple-speaker speech synthesis, music generation, and audio generation. The integration of speech modality into multimodal unified large models has also become popular, as seen in models like SpeechGPT and AnyGPT. These advancements are largely due to discrete acoustic codec representations used from neural codec…
Large Language Models (LLMs) have made remarkable strides in multimodal capabilities, with closed-source models like GPT-4, Claude, and Gemini leading the field. However, the challenge lies in democratizing AI by making these powerful models accessible to a broader audience. The current limitation is the substantial computational resources required to run state-of-the-art models effectively. This creates…
Large Language Models (LLMs) have emerged as powerful tools for understanding and generating human-like text. This paper explores the potential of LLMs to shape human perspectives and influence decisions on particular tasks. The researchers investigate using LLMs in persuasion across various domains such as investment, credit cards, insurance, retail, and Behavioral Change Support Systems (BCSS).…