Explore the rapidly evolving world of artificial intelligence, machine learning, and automation. From AI ethics to real-world applications, this category delivers insights that matter for today and tomorrow.
Generative AI’s ability to write software code has quickly created one of the technology’s first real use cases for business. Professional software engineers and novices alike are using AI coding assistants to produce, test, edit, and debug code, reducing the…
In sprawling stretches of farmland and industrial parks, supersized buildings packed with racks of computers are springing up to fuel the AI race. These engineering marvels are a new species of infrastructure: supercomputers designed to train and run large language…
How large is a large language model? Think about it this way. In the center of San Francisco there’s a hill called Twin Peaks from which you can view nearly the entire city. Picture all of it—every block and intersection,…
MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. I am writing this because one of my editors woke up…
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Sometimes AI feels like a niche topic to write about, but then the holidays happen, and I…
MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. In an industry in constant flux, sticking your neck out to…
The agentic AI field is moving from experimental prototypes to production-ready autonomous systems.
When the creator of the world’s most advanced coding agent speaks, Silicon Valley doesn’t just listen — it takes notes. For the past week, the engineering community has been dissecting a thread on X from Boris Cherny, the creator and…
Editor’s note: This article is a part of our series on visualizing the foundations of machine learning.
This article is divided into five parts; they are: • An Example of Tensor Parallelism • Setting Up Tensor Parallelism • Preparing Model for Tensor Parallelism • Train a Model with Tensor Parallelism • Combining Tensor Parallelism with FSDP Tensor…