“We’re here to talk about really substantive changes, and we want you to be a participant in that,” said Desirée Plata, the School of Engineering Distinguished Professor of Climate and Energy in MIT’s Department of Civil and Environmental Engineering, at Energizing@MIT: the MIT Energy Initiative’s (MITEI) Annual Research Conference that was held on Sept. 9-10….
In this article, you will learn how the ReAct (Reasoning + Acting) pattern works and how to implement it with LangGraph — first with a simple, hardcoded loop and then with an LLM-driven agent. Topics we will cover include: The ReAct cycle (Reason → Act → Observe) and why it’s useful for agents. How to…
Traditional software is deterministic primarily. You write code, you specify inputs and outputs, you audit logic branches. Security people can threat-model that. LLMs are different in a few critical ways: They are probabilistic.Given the same prompt, an LLM might respond slightly differently each time. There is no simple “if X then Y” logic to audit….
Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and…
A language model is a mathematical model that describes a human language as a probability distribution over its vocabulary. To train a deep learning network to model a language, you need to identify the vocabulary and learn its probability distribution. You can’t create the model from nothing. You need a dataset for your model to…
Veo 3.1 is Google’s upgraded AI video generation model, designed for more realistic, longer, and higher-fidelity results. It supports single clips up to one minute, full HD (1080p) resolution, and synchronized, natural-sounding audio. Veo’s improved prompt adherence and detail control make it ideal for cinematic content, professional storytelling, and creative exploration. Key upgrades include: First…
What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives? These questions may be deeply philosophical, but for Phillip Isola, finding the answers is as much about computation as…
In this article, you will learn practical, advanced ways to use large language models (LLMs) to engineer features that fuse structured (tabular) data with text for stronger downstream models. Topics we will cover include: Generating semantic features from tabular contexts and combining them with numeric data. Using LLMs for context-aware imputation, enrichment, and domain-driven feature…
TL;DR: Artificial Intelligence learns patterns from data and uses them to make predictions, generate content, or solve problems. Generative AI, such as ChatGPT or image and video generators, takes this a step further by creating new things, text, art, music, and more, that have never existed before. People often ask: “How does AI actually work?”…
With global power demand from data centers expected to more than double by 2030, the MIT Energy Initiative (MITEI) in September launched an effort that brings together MIT researchers and industry experts to explore innovative solutions for powering the data-driven future. At its annual research conference, MITEI announced the Data Center Power Forum, a targeted research effort for…