Latest from MIT : On the road to cleaner, greener, and faster driving

No one likes sitting at a red light. But signalized intersections aren’t just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change. What if motorists could time their trips so they arrive at the intersection when the light is green? While that might be just…

Latest from Google AI – Challenges in Multi-objective Optimization for Automatic Wireless Network Planning

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…

Latest from MIT : Technique protects privacy when making online recommendations

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…

Latest from Google AI – Language Models Perform Reasoning via Chain of Thought

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…

Latest from Google AI – Unlocking Zero-Resource Machine Translation to Support New Languages in Google Translate

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,…

Latest from MIT Tech Review – Powering the next generation of AI

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…

Latest from MIT Tech Review – From data and AI aspirations to sustainable business outcomes

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…

Latest from MIT : Q&A: Chris Rackauckas on the equations at the heart of practically everything

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…

Latest from Google AI – Learning Locomotion Skills Safely in the Real World

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…

Latest from MIT : Unpacking black-box models

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…