Top Open Source Large Language Models (LLMs) Available For Commercial Use

Advancements in Language Modeling Recent developments in language modeling have improved natural language processing, allowing for the creation of coherent and contextually relevant text for various uses. Autoregressive (AR) models, which generate text sequentially from left…

Understanding Regression Tasks and Their Challenges Regression tasks aim to predict continuous numeric values but often rely on traditional approaches that have some limitations: Limitations of Traditional Approaches Distribution Assumptions: Many methods, like Gaussian models, assume…

Recent research by EPFL and Meta introduces the Chain-of-Abstraction (CoA) reasoning method for large language models (LLMs) to enhance multi-step reasoning by efficiently leveraging tools. The method separates general reasoning from domain-specific knowledge, yielding a 7.5%…

Discover Kolmogorov-Arnold Networks (KANs) Enhancing Interpretability and Accuracy in Deep Learning Explore how KANs offer a compelling alternative to MLPs, leveraging mathematical concepts to enhance interpretability and accuracy in deep learning. With ongoing research aiming to…

Researchers from the University of Michigan propose new benchmarks and evaluation protocols to assess the Theory of Mind capability of Large Language Models (LLMs). They advocate for a holistic evaluation approach that categorizes machine ToM into…

This article discusses the expectations and responsibilities of junior, mid-level, and senior data scientists. It emphasizes the importance of experience and technical expertise in defining these roles, but also highlights the need for clarity on business…

Neuro-symbolic Artificial Intelligence (NeSy AI) Neuro-symbolic AI combines neural networks’ perceptive abilities with symbolic systems’ logical reasoning strengths to address complex tasks. Challenges in NeSy AI Development Integrating learning signals from neural and symbolic components presents…

Transforming Tabular Data with Deep Learning Understanding the Challenge Deep learning has revolutionized fields like finance, healthcare, and e-commerce by processing complex data. However, using deep learning for tabular data (data organized in rows and columns)…

Practical Solutions and Value of XR-Objects Seamless Integration of Real and Virtual Worlds XR-Objects revolutionize by blending physical and digital realms effortlessly using AI. Augmented Object Intelligence Introduces AI-driven extraction of digital data from real-world objects…

The development of Multi-modal Large Language Models (MLLMs) such as Google’s Gemini presents a significant shift in AI, combining textual data with visual understanding. A study evaluates Gemini’s capabilities compared to leader GPT-4V and Sphinx, highlighting…

Understanding the Challenges of Large Language Models in Mathematics Large Language Models (LLMs) struggle with mathematical reasoning, which includes tasks like understanding math concepts, solving problems, and making logical deductions. While there are methods to improve…

“NeRFiller,” a 3D inpainting approach from Google Research and UC Berkeley, innovatively completes missing portions in 3D captures by controlling the process through reference examples. It enhances scenes by addressing reconstruction failures or lack of observations,…

The text discusses DATACOMP, a dataset testbed featuring 12.8 billion image-text pairs from Common Crawl. Researchers can use it to design filtering techniques, curate data, and assess datasets for improving multimodal models. DATACOMP-1B achieves a 3.7…

The method of Image Quality Assessment (IQA) standardizes image evaluation by incorporating subjective studies and large multimodal models (LMMs). LMMs capture nuanced understanding of data, improving performance across tasks. Researchers from multiple universities proposed Co-Instruct, a…

Lincoln Laboratory is working to reduce the energy requirements of AI models by promoting energy usage transparency and improving training efficiency.

Understanding Artificial General Intelligence (AGI) Artificial General Intelligence (AGI) aims to create systems that can learn and adapt like humans. Unlike narrow AI, which is limited to specific tasks, AGI strives to apply its skills in…

The challenges of developing instruction-following agents in grounded environments include sample efficiency and generalizability. Reinforcement learning and imitation learning are common techniques but can be costly and rely on trial and error or expert guidance. Language…

Importance of Medical Question-Answering Systems Medical question-answering (QA) systems are essential tools for healthcare professionals and the public. Unlike simpler models, long-form QA systems provide detailed answers that reflect the complexities of real-world clinical situations. These…

Improving Inference in Large Language Models (LLMs) Inference in large language models is tough because they need a lot of computing power and memory, which can be expensive and energy-intensive. Traditional methods like sparsity, quantization, or…