IBM AI Team Releases an Open-Source Family of Granite Code Models for Making Coding Easier for Software Developers

Understanding Retrieval-Augmented Generation (RAG) Retrieval-augmented generation (RAG) is gaining popularity for addressing issues in Large Language Models (LLMs), such as inaccuracies and outdated information. A RAG system includes two main parts: a retriever and a reader.…

Google Bard introduces an AI image generator leveraging Imagen 2, enabling users to create images from text descriptions. Accessible in the United States, it prompts users to describe the desired image, providing a straightforward and free…

BONE: A New Approach to Machine Learning Researchers from Queen Mary University of London, the University of Oxford, Memorial University of Newfoundland, and Google DeepMind have introduced BONE, a framework for Bayesian online learning in changing…

Advancements in large language models (LLMs) have impacted various fields, yet the legal domain lags behind. Equall.ai’s researchers introduce SaulLM-7B, a public legal LLM specialized for legal text, leveraging extensive pretraining on dedicated legal corpora. It…

Introduction to AlphaGeometry2 The International Mathematical Olympiad (IMO) is a prestigious competition for high school students, focusing on challenging math problems. Geometry is a key area in this competition, and automated solutions have evolved significantly. Advancements…

Microsoft introduces Orca 2, an advanced reasoning model for smaller language models. Unlike traditional imitation learning, Orca instructs models in different reasoning techniques to improve their reasoning and comprehension skills. Orca 2 outperforms other models in…

Prompting techniques like chain of thought (CoT) and tree of thought (ToT) have drastically improved the problem-solving capabilities of large language models (LLMs). However, they assume linear reasoning, in contrast to the non-linear patterns characteristic of…

Open-Source MLLMs: Enhancing Reasoning with Practical Solutions Open-source Multimodal Large Language Models (MLLMs) show great potential for tackling various tasks by combining visual encoders and language models. However, there is room for improvement in their reasoning…

Understanding In-Context Learning (ICL) In-Context Learning (ICL) is a key feature of advanced language models. It enables these models to answer questions based on examples provided without specific instructions. By showing a few examples, the model…

The text describes the use of a user-friendly tool for creating intricate visualizations. For further details, refer to the original article on Towards Data Science.

Empower Your Decision-Making with AI Enhancing Decision-Making with PlanRAG PlanRAG is a revolutionary technique that empowers large language models (LLMs) to make optimal decisions by analyzing structured data and business rules. It enhances decision-making performance by…

At the “Generative AI: Shaping the Future” symposium, keynote speaker Rodney Brooks highlighted the risk of overhyping AI’s capabilities, emphasizing the need for responsible development. The event at MIT included discussions on generative AI’s potential for…

The Evolution of Robotics The development of robotics has faced challenges due to slow and costly training methods. Traditionally, engineers had to manually control robots to gather specific training data. However, with the introduction of Aria…

Optimizing Graph Neural Network Training with DiskGNN: A Leap Toward Efficient Large-Scale Learning Introduction Graph Neural Networks (GNNs) are essential for processing complex data from domains like e-commerce and social networks. However, as graph data scales,…

The 2024 World Economic Forum in Davos focused on AI, with concerns about AI-driven misinformation and election interference. UN Secretary-General urged collaborative governance to address AI risks, while the European Commission President emphasized AI’s opportunities. Chinese…

Importance of Sampling from Complex Probability Distributions Sampling from complex probability distributions is crucial in fields like statistical modeling, machine learning, and physics. It helps generate representative data points to solve problems such as: Bayesian inference…

Practical Solutions for Visual Mathematical Problem-Solving Challenges in Visual Mathematical Problem-Solving Large Language Models (LLMs) and their multi-modal counterparts (MLLMs) face challenges in visual mathematical problem-solving, particularly in interpreting geometric figures and integrating complex mathematical concepts…