Latest from Google AI – GraphWorld: Advances in Graph Benchmarking

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…

Latest from MIT Tech Review – The Download: Meta’s AI giveaway, and abortion clinic data tracking

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Meta has built a massive new language AI—and it’s giving it away for free Open to ideas: Meta’s AI lab has created a massive new language model, and in an unprecedented move for…

Latest from MIT : Artificial intelligence system learns concepts shared across video, audio, and text

Humans observe the world through a combination of different modalities, like vision, hearing, and our understanding of language. Machines, on the other hand, interpret the world through data that algorithms can process. So, when a machine “sees” a photo, it must encode that photo into data it can use to perform a task like image…

UC Berkeley – Rethinking Human-in-the-Loop for Artificial Augmented Intelligence

Figure 1: In real-world applications, we think there exist a human-machine loop where humans and machines are mutually augmenting each other. We call it Artificial Augmented Intelligence. How do we build and evaluate an AI system for real-world applications? In most AI research, the evaluation of AI methods involves a training-validation-testing process. The experiments usually…

Latest from Google AI – Alpa: Automated Model-Parallel Deep Learning

Posted by Zhuohan Li, Student Researcher, Google Research, and Yu Emma Wang, Senior Software Engineer, Google Core Over the last several years, the rapidly growing size of deep learning models has quickly exceeded the memory capacity of single accelerators. Earlier models like BERT (with a parameter size of < 1GB) can efficiently scale across accelerators…

Latest from MIT Tech Review – Meta has built a massive new language AI—and it’s giving it away for free

Meta’s AI lab has created a massive new language model that shares both the remarkable abilities and the harmful flaws of OpenAI’s pioneering neural network GPT-3. And in an unprecedented move for Big Tech, it is giving it away to researchers—together with details about how it was built and trained. “We strongly believe that the…

UC Berkeley – Designing Societally Beneficial Reinforcement Learning Systems

Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind’s work on controlling a nuclear reactor or on improving Youtube video compression, or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning. But the exciting potential…

Latest from MIT : A one-up on motion capture

From “Star Wars” to “Happy Feet,” many beloved films contain scenes that were made possible by motion capture technology, which records movement of objects or people through video. Further, applications for this tracking, which involve complicated interactions between physics, geometry, and perception, extend beyond Hollywood to the military, sports training, medical fields, and computer vision…

Latest from Google AI – Extracting Skill-Centric State Abstractions from Value Functions

Posted by Dhruv Shah, Intern, and Brian Ichter, Research Scientist, Robotics at Google Advances in reinforcement learning (RL) for robotics have enabled robotic agents to perform increasingly complex tasks in challenging environments. Recent results show that robots can learn to fold clothes, dexterously manipulate a rubik’s cube, sort objects by color, navigate complex environments and…

Latest from MIT : Engineers use artificial intelligence to capture the complexity of breaking waves

Waves break once they swell to a critical height, before cresting and crashing into a spray of droplets and bubbles. These waves can be as large as a surfer’s point break and as small as a gentle ripple rolling to shore. For decades, the dynamics of how and when a wave breaks have been too…