Artificial Intelligence

Posted by Sara Ahmadian and Matthew Fahrbach, Research Scientists, Google Research, Large-Scale Optimization Team Economics, combinatorics, physics, and signal processing conspire to make it difficult to design, build, and operate high-quality, cost-effective wireless networks. The radio transceivers that communicate with our mobile phones, the equipment that supports them (such as power and wired networking), and…

Algorithms recommend products while we shop online or suggest songs we might like as we listen to music on streaming apps. These algorithms work by using personal information like our past purchases and browsing history to generate tailored recommendations. The sensitive nature of such data makes preserving privacy extremely important, but existing methods for solving…

Posted by Jason Wei and Denny Zhou, Research Scientists, Google Research, Brain team In recent years, scaling up the size of language models has been shown to be a reliable way to improve performance on a range of natural language processing (NLP) tasks. Today’s language models at the scale of 100B or more parameters achieve…

Posted by Isaac Caswell and Ankur Bapna, Research Scientists, Google Translate Machine translation (MT) technology has made significant advances in recent years, as deep learning has been integrated with natural language processing (NLP). Performance on research benchmarks like WMT have soared, and translation services have improved in quality and expanded to include new languages. Nevertheless,…

Ubiquitous computing has triggered an avalanche of data that is beyond human processing capabilities. AI technologies have emerged as the only viable way to turn this data into information. As more computing produces more data, more computing power is needed to power AI. Next generation AI will soon look to planetary-scale computing systems to further…

There are 3 common challenges that organizations face while transforming AI aspirations into scalable and intelligent solutions. Get an insider’s view of use case scenarios that illustrate real business and functional value through a proven framework and process toward sustainable digital transformation. About the speaker Vishal Kapoor, Vice President, Data and AI, Kyndryl Vishal Kapoor…

Some people pass the time with hobbies like crossword puzzles or Sudoku. When Chris Rackauckas has a spare moment, he often uses it to answer questions about numerical differential equations that people have posed online. Rackauckas — previously an MIT applied mathematics instructor, now an MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) research affiliate…

Posted by Jimmy (Tsung-Yen) Yang, Student Researcher, Robotics at Google The promise of deep reinforcement learning (RL) in solving complex, high-dimensional problems autonomously has attracted much interest in areas such as robotics, game playing, and self-driving cars. However, effectively training an RL policy requires exploring a large set of robot states and actions, including many…

Modern machine-learning models, such as neural networks, are often referred to as “black boxes” because they are so complex that even the researchers who design them can’t fully understand how they make predictions. To provide some insights, researchers use explanation methods that seek to describe individual model decisions. For example, they may highlight words in…

John Palowitch and Anton Tsitsulin, Research Scientists, Google Research, Graph Mining team Graphs are very common representations of natural systems that have connected relational components, such as social networks, traffic infrastructure, molecules, and the internet. Graph neural networks (GNNs) are powerful machine learning (ML) models for graphs that leverage their inherent connections to incorporate context…