AI

Jim Sanborn’s inbox is flooded with amateur cryptographers who say they’ve cracked the code with chatbots like Grok 3.

Imagine that a robot is helping you clean the dishes. You ask it to grab a soapy bowl out of the sink, but its gripper slightly misses the mark. Using a new framework developed by MIT and NVIDIA researchers, you could correct that robot’s behavior with simple interactions. The method would allow you to point

Agents are revolutionizing the landscape of generative AI, serving as the bridge between large language models (LLMs) and real-world applications. These intelligent, autonomous systems are poised to become the cornerstone of AI adoption across industries, heralding a new era of human-AI collaboration and problem-solving. By using the power of LLMs and combining them with specialized

In the rapidly evolving landscape of artificial intelligence, Retrieval Augmented Generation (RAG) has emerged as a game-changer, revolutionizing how Foundation Models (FMs) interact with organization-specific data. As businesses increasingly rely on AI-powered solutions, the need for accurate, context-aware, and tailored responses has never been more critical. Enter the powerful trio of Amazon Bedrock, LlamaIndex, and

For over 30 years, science photographer Felice Frankel has helped MIT professors, researchers, and students communicate their work visually. Throughout that time, she has seen the development of various tools to support the creation of compelling images: some helpful, and some antithetical to the effort of producing a trustworthy and complete representation of the research.

Developed to boost productivity and operational readiness, the AI is now being used to “review” diversity, equity, inclusion, and accessibility polices to align them with President Trump’s orders.

The release of OpenAI’s biggest model ever exposes the tension between building artificial general intelligence and making ChatGPT into a truly useful utility.

This post was co-authored with Johann Wildgruber, Dr. Jens Kohl, Thilo Bindel, and Luisa-Sophie Gloger from BMW Group. The BMW Group—headquartered in Munich, Germany—is a vehicle manufacturer with more than 154,000 employees, and 30 production and assembly facilities worldwide as well as research and development locations across 17 countries. Today, the BMW Group (BMW) is the

Time series forecasting is critical for decision-making across industries. From predicting traffic flow to sales forecasting, accurate predictions enable organizations to make informed decisions, mitigate risks, and allocate resources efficiently. However, traditional machine learning approaches often require extensive data-specific tuning and model customization, resulting in lengthy and resource-heavy development. Enter Chronos, a cutting-edge family of

Generative AI question-answering applications are pushing the boundaries of enterprise productivity. These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. However, building and deploying trustworthy AI assistants requires a robust ground truth and evaluation framework. Ground truth