Learning
How to Build an End-to-End Data Science Workflow with Machine Learning, Interpretability, and Gemini AI Assistance?
In this tutorial, we walk through an advanced end-to-end data science workflow where we combine traditional machine learning with the power of Gemini. We begin by preparing and modeling the diabetes dataset, then we dive into evaluation, feature importance, and partial dependence. Along the way, we bring in Gemini as our AI data scientist to…
Building a Hybrid Rule-Based and Machine Learning Framework to Detect and Defend Against Jailbreak Prompts in LLM Systems
In this tutorial, we introduce a Jailbreak Defense that we built step-by-step to detect and safely handle policy-evasion prompts. We generate realistic attack and benign examples, craft rule-based signals, and combine those with TF-IDF features into a compact, interpretable classifier so we can catch evasive prompts without blocking legitimate requests. We demonstrate evaluation metrics, explain…
Simpler models can outperform deep learning at climate prediction
Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better. The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models. Their…
Zhipu AI Unveils ComputerRL: An AI Framework Scaling End-to-End Reinforcement Learning for Computer Use Agents
In the rapidly evolving landscape of AI-driven automation, Zhipu AI has introduced ComputerRL, a groundbreaking framework designed to empower agents with the ability to navigate and manipulate complex digital workspaces. This innovation addresses a core challenge in AI agent development: the disconnect between computer agents and human-designed graphical user interfaces (GUIs). By integrating programmatic API…
Nebius AI Advances Open-Weight LLMs Through Reinforcement Learning for Capable SWE Agents
The landscape of software engineering automation is evolving rapidly, driven by advances in Large Language Models (LLMs). However, most approaches to training capable agents rely on proprietary models or costly teacher-based methods, leaving open-weight LLMs with limited capabilities in real-world scenarios. A team of researchers from Nebius AI and Humanoid introduced a reinforcement learning framework…
Zhipu AI Releases GLM-4.5V: Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Zhipu AI has officially released and open-sourced GLM-4.5V, a next-generation vision-language model (VLM) that significantly advances the state of open multimodal AI. Based on Zhipu’s 106-billion parameter GLM-4.5-Air architecture—with 12 billion active parameters via a Mixture-of-Experts (MoE) design—GLM-4.5V delivers strong real-world performance and unmatched versatility across visual and textual content. Key Features…
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,…