When OpenAI launched ChatGPT, with zero fanfare, in late November 2022, the San Francisco–based artificial-intelligence company had few expectations. Certainly, nobody inside OpenAI was prepared for a viral mega-hit. The firm has been scrambling to catch up—and capitalize on its success—ever since. It was viewed in-house as a “research preview,” says Sandhini Agarwal, who works…
In a packed room in MIT’s Stata Center, hundreds of digital robots collide across a giant screen projected at the front of the room. A crowd of students in the audience gasps and cheers as the battle’s outcome hangs in the balance. In an upper corner of the screen, the people who have programmed the…
Posted by Florian Hartmann, Software Engineer, and Peter Kairouz, Research Scientist, Google Research Federated learning is a distributed way of training machine learning (ML) models where data is locally processed and only focused model updates and metrics that are intended for immediate aggregation are shared with a server that orchestrates training. This allows the training…
Modern fabrication tools such as 3D printers can make structural materials in shapes that would have been difficult or impossible using conventional tools. Meanwhile, new generative design systems can take great advantage of this flexibility to create innovative designs for parts of a new building, car, or virtually any other device. But such “black box”…
Posted by Minji Yoon, Research Intern, and Bryan Perozzi, Research Scientist, Google Research, Graph Mining Team Industrial applications of machine learning are commonly composed of various items that have differing data modalities or feature distributions. Heterogeneous graphs (HGs) offer a unified view of these multimodal data systems by defining multiple types of nodes (for each…
Posted by Natasha Noy, Research Scientist, and Omar Benjelloun, Software Engineer, Google Research Access to datasets is critical to many of today’s endeavors across verticals and industries, whether scientific research, business analysis, or public policy. In the scientific community and throughout various levels of the public sector, reproducibility and transparency are essential for progress, so…
Posted by Posted by Leslie Yeh, Director, University Relations (This is Part 9 in our series of posts covering different topical areas of research at Google. You can find other posts in the series here.) Sharing knowledge is essential to Google’s research philosophy — it accelerates technological progress and expands capabilities community-wide. Solving complex problems…
In 2020, the School of Engineering and Takeda Pharmaceutical Company launched the MIT-Takeda Program, which aims to leverage the experience of both entities to solve problems at the intersection of health care, medicine, and artificial intelligence. Since the program began, teams have devised mechanisms to reduce manufacturing time for certain pharmaceutical products, submitted a patent…
A group of 10 companies, including OpenAI, TikTok, Adobe, the BBC, and the dating app Bumble, have signed up to a new set of guidelines on how to build, create, and share AI-generated content responsibly. The recommendations call for both the builders of the technology, such as OpenAI, and creators and distributors of digitally created…
Posted by Yang Li, Research Scientist, and Gang Li, Software Engineer, Google Research The computational understanding of user interfaces (UI) is a key step towards achieving intelligent UI behaviors. Previously, we investigated various UI modeling tasks, including widget captioning, screen summarization, and command grounding, that address diverse interaction scenarios such as automation and accessibility. We…