Unlocking the Potential of Unstructured Data with Reducto Unstructured data, which makes up about 80% of all company data, including spreadsheets and PDFs, often poses challenges in digital workflows. Reducto, an AI-powered startup, offers a practical…

Researchers from the University of Washington and Google have developed a new technology called “Distilling Step-by-Step” to train small machine learning models with less data. This approach involves extracting informative natural language rationales from large language…

Post-Training Quantization (PTQ) for Large Language Models (LLMs) Post-training quantization (PTQ) aims to make large language models smaller and faster for real-world applications. However, these models need large amounts of data, and the uneven distribution of…

Enhancing Cross-Cultural Image Captioning with MosAIC Large Multimodal Models (LMMs) are great at various vision-language tasks, but they struggle with cross-cultural understanding. This is primarily due to biases in their training data, which hampers their ability…

Language models, such as Gemma by Google DeepMind, are pivotal in AI research, enabling machines to understand and generate human-like language. Gemma’s open and optimized models mark a significant leap forward, achieving superior performance across various…

Revolutionizing Radiology with AI: Introducing a2z-1 Enhancing Quality Assurance in Abdominal-Pelvis CT Scans a2z Radiology AI introduces a2z-1, an AI tool designed to improve radiology practices by providing a safety net for radiologists. This innovative solution…

The author discusses how to succeed in your first data role. They emphasize the importance of becoming comfortable with workflow and data structure, mastering the company’s toolbox, learning the business, sharpening your skills, and becoming self-sufficient.…

Understanding the Challenges of Evaluating Large Language Models (LLMs) Large Language Models (LLMs) are essential in various AI applications like text summarization and conversational AI. However, evaluating these models can be tough. Human evaluations can be…

Reinforcement Learning for Large Language Models Challenges with Traditional Methods Traditional reinforcement learning (RL) for large language models (LLMs) uses outcome-based rewards, giving feedback only on the final results. This approach creates difficulties for tasks that…

Understanding Large-Scale Neural Language Models Large-scale neural language models (LMs) are great at handling tasks similar to what they’ve been trained on. However, it’s unclear if they can tackle new problems that require advanced reasoning or…

Language models like GPT and LLaMa have shown impressive performance but struggle with tasks involving tables. To address this, researchers propose table-tuning, which involves training models like GPT-3.5 and ChatGPT with table-related tasks. These table-tuned models,…

Understanding the Importance of Quality in AI Training A strong link exists between the quality of an LLM’s training data and its performance. Researchers are focusing on gathering high-quality datasets, which currently require detailed human input.…

Meter-to-cash solutions are crucial in the utilities sector for revenue generation and efficient operations. These solutions have become indispensable, offering a comprehensive guide for businesses in 2023. AIMultiple provides information and tools to help businesses grow.

SEC Chairman Gary Gensler emphasizes the importance of regulating AI in order to prevent a financial crisis. He expresses concerns about the potential for overreliance on AI tools by financial institutions, which could lead to a…

Practical Solutions and Value of Generalizable Reward Model (GRM) Improving Large Language Models (LLMs) Performance Pretrained large models can align with human values and avoid harmful behaviors using alignment methods such as supervised fine-tuning (SFT) and…

Researchers have successfully integrated 2D layered material into a compact electronic chip using a monolithic 3D approach for AI computing, enhancing multi-functional integration and advancing AI processing capabilities.

This text discusses the HyperHuman framework, which aims to generate realistic and diverse human images. It highlights the challenges faced by previous models in creating coherent anatomical structures and proposes a unified framework that incorporates structural…

The text discusses the separation between first launch and reboot when using startup scripts in cloud servers. It explains how user data is used to configure instances during the first launch and reboot, and provides an…

Practical Solutions for AI-Driven Software Engineering Addressing the Challenge of Large Code Repositories Large Language Models (LLMs) struggle with handling entire code repositories due to the complexity of code structures and dependencies. Current methods like similarity-based…