Learning
How to Use the SHAP-IQ Package to Uncover and Visualize Feature Interactions in Machine Learning Models Using Shapley Interaction Indices (SII)
In this tutorial, we explore how to use the SHAP-IQ package to uncover and visualize feature interactions in machine learning models using Shapley Interaction Indices (SII), building on the foundation of traditional Shapley values. Shapley values are great for explaining individual feature contributions in AI models but fail to capture feature interactions. Shapley interactions go…
Alibaba Qwen Introduces Qwen3-MT: Next-Gen Multilingual Machine Translation Powered by Reinforcement Learning
Alibaba has introduced Qwen3-MT (qwen-mt-turbo) via Qwen API, its latest and most advanced machine translation model, designed to break language barriers with unprecedented accuracy, speed, and flexibility. Trained on trillions of multilingual tokens, Qwen3-MT supports over 92 languages—covering more than 95% of the global population. Leveraging cutting-edge architecture, reinforcement learning, and rich customization options, it delivers…
Thought Anchors: A Machine Learning Framework for Identifying and Measuring Key Reasoning Steps in Large Language Models with Precision
Understanding the Limits of Current Interpretability Tools in LLMs AI models, such as DeepSeek and GPT variants, rely on billions of parameters working together to handle complex reasoning tasks. Despite their capabilities, one major challenge is understanding which parts of their reasoning have the greatest influence on the final output. This is especially crucial for…
Combining technology, education, and human connection to improve online learning
MIT Morningside Academy for Design (MAD) Fellow Caitlin Morris is an architect, artist, researcher, and educator who has studied psychology and used online learning tools to teach herself coding and other skills. She’s a soft-spoken observer, with a keen interest in how people use space and respond to their environments. Combining her observational skills with active community engagement,…
ether0: A 24B LLM Trained with Reinforcement Learning RL for Advanced Chemical Reasoning Tasks
LLMs primarily enhance accuracy through scaling pre-training data and computing resources. However, the attention has shifted towards alternate scaling due to finite data availability. This includes test-time training and inference compute scaling. Reasoning models enhance performance by emitting thought processes before answers, initially through CoT prompting. Recently, reinforcement learning (RL) post-training has been used. Scientific…
How AI Agents Are Transforming the Education Sector: A Look at Kira Learning and Beyond
Today’s classrooms are changing fast because of Artificial Intelligence (AI). AI agents are now part of how teaching and learning happen. They do more than automate tasks. These agents help teachers provide personal support and give students feedback that fits their own learning style. Kira Learning is a platform leading this change. It uses AI…
Learning how to predict rare kinds of failures
On Dec. 21, 2022, just as peak holiday season travel was getting underway, Southwest Airlines went through a cascading series of failures in their scheduling, initially triggered by severe winter weather in the Denver area. But the problems spread through their network, and over the course of the next 10 days the crisis ended up…
New tool evaluates progress in reinforcement learning
If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving. …
Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford University explored the generalization capabilities of these two…
A Coding Implementation on Introduction to Weight Quantization: Key Aspect in Enhancing Efficiency in Deep Learning and LLMs
In today’s deep learning landscape, optimizing models for deployment in resource-constrained environments is more important than ever. Weight quantization addresses this need by reducing the precision of model parameters, typically from 32-bit floating point values to lower bit-width representations, thus yielding smaller models that can run faster on hardware with limited resources. This tutorial introduces…