Navigating Explainable AI in In Vitro Diagnostics: Compliance and Transparency Under European Regulations

IBM researchers have introduced LAB (Large-scale Alignment for chatbots) to address scalability challenges in instruction-tuning for large language models (LLMs). LAB leverages a taxonomy-guided synthetic data generation process and a multi-phase training framework to enhance LLM…

The Importance of Guardrails for Large Language Models (LLMs) The fast use of Large Language Models (LLMs) across industries needs strong measures to ensure they are used safely, ethically, and effectively. Here are 20 key guardrails…

Advancing MLLMs Through Realistic Chart Understanding Benchmarks Practical Solutions and Value: Multimodal large language models (MLLMs) integrate NLP and computer vision, essential for analyzing visual and textual data in scientific papers and financial reports. Enhancing MLLMs’…

Microsoft AI Releases Phi 3.5 Mini, MoE, and Vision Phi 3.5 Mini Instruct: Balancing Power and Efficiency Phi 3.5 Mini Instruct is a compact model with 3.8 billion parameters, supporting 128K context length for handling long…

The Financial Stability Oversight Council (FSOC) has identified AI as a significant risk factor in the US financial system. Treasury Secretary Janet Yellen highlighted concerns in a recent meeting, emphasizing the need for responsible innovation and…

Introducing VERSA: A Cutting-Edge Toolkit for Audio Evaluation Overview of VERSA The WAVLab Team has launched VERSA, an innovative and comprehensive evaluation toolkit designed to assess speech, audio, and music signals. As artificial intelligence continues to…

Understanding Language Models and Their Challenges Language models (LMs) are essential tools used in areas like mathematics, coding, and reasoning to tackle complex tasks. They utilize deep learning to produce high-quality results, but their effectiveness can…

Practical Solutions for Text Classification Revolutionizing Text Classification with Large Language Models (LLMs) Large language models like ChatGPT enable zero-shot classification without additional training, leading to widespread adoption in political and social sciences. Challenges and Solutions…

Understanding Intuitive Physics in AI Humans naturally understand how objects behave, such as not expecting sudden changes in their position or shape. This understanding is seen even in infants and animals, supporting the idea that humans…

Practical AI Solution: Elia – An Open Source Terminal UI for Interacting with LLMs People working with large language models often need a quick and efficient way to interact with these powerful tools. However, existing methods…

Revolutionizing Creativity with Generative AI Introduction to Generative AI Models Generative AI models, including Large Language Models (LLMs) and diffusion techniques, are changing creative fields such as art and entertainment. These models can create a wide…

Vision Transformers (ViTs) vs Convolutional Neural Networks (CNNs) in AI Image Processing The Rise of Vision Transformers (ViTs) Vision Transformers (ViTs) represent a revolutionary shift in image processing, adapting transformer architecture for visual data to capture…

MotionLM is a new approach for predicting the behavior of road agents in autonomous vehicles. It treats the prediction task as a language modeling task, similar to how language models capture complex language distributions. MotionLM outperforms…

Understanding Neural Machine Translation (NMT) Neural Machine Translation (NMT) is an advanced technology that translates text between languages using machine learning. It plays a crucial role in global communication, particularly for tasks like technical document translation…

Integrating No-Code AI in Non-Technical Higher Education Practical Solutions and Value Recent developments in ML underscore its ability to drive value across diverse sectors. To make ML more accessible to non-STEM students, a case-based approach utilizing…

Large language models (LLMs) like GPT-4 and PaLM 2 struggle with mathematical problem-solving due to the need for imagination, reasoning, and computation. However, with multiple attempts, LLMs show potential for improvement. Fine-tuning techniques such as supervised…