Latest from MIT Tech Review – AI Integration Across Industries

To create sustainable business impact, AI capabilities need to be tailored and optimized to an industry or organization’s specific requirements and infrastructure model. Hear how customers’ challenges across industries can be addressed in any compute environment from the cloud to the edge with end-to-end hardware and software optimization. About the speakers Kavitha Prasad, VP &…

Latest from Google AI – Large-Scale Matrix Factorization on TPUs

Posted by Harsh Mehta, Software Engineer, Google Research Matrix factorization is one of the oldest, yet still widely used, techniques for learning how to recommend items such as songs or movies from user ratings. In its basic form, it approximates a large, sparse (i.e., mostly empty) matrix of user-item interactions with a product of two…

Latest from MIT Tech Review – Business-Ready Data Holds the Key to AI Democratization

Good data is the bedrock of a self-service data consumption model, which in turn unlocks insights, analytics, personalization at scale through AI. Yet many organizations face immense challenges setting up a robust data foundation. Dive into a pragmatic perspective on abstracting the complexity and untangling the conflicts in data management for better AI. About the…

Latest from Google AI – VDTTS: Visually-Driven Text-To-Speech

Posted by Tal Remez, Software Engineer, Google Research and Micheal Hassid, Software Engineer Intern, Google Research Recent years have seen a tremendous increase in the creation and serving of video content to users across the world in a variety of languages and over numerous platforms. The process of creating high quality content can include several…

Latest from Google AI – Efficiently Initializing Reinforcement Learning With Prior Policies

Posted by Ikechukwu Uchendu, AI Resident and Ted Xiao, Software Engineer, Robotics at Google Reinforcement learning (RL) can be used to train a policy to perform a task via trial and error, but a major challenge in RL is learning policies from scratch in environments with hard exploration challenges. For example, consider the setting depicted…

Latest from MIT : An optimized solution for face recognition

The human brain seems to care a lot about faces. It’s dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency — and neuroscientists at MIT’s…

Latest from MIT Tech Review – This horse-riding astronaut is a milestone in AI’s ability to make sense of the world

When OpenAI revealed its picture-making neural network DALL-E in early 2021, the program’s human-like ability to combine different concepts in new ways was striking. The string of images that DALL-E produced on demand were surreal and cartoonish, but they showed that the AI had learned key lessons about how the world fits together. DALL-E’s avocado…

Latest from MIT : Does this artificial intelligence think like a human?

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo. While tools exist to help experts…

Latest from Google AI – Reproducibility in Deep Learning and Smooth Activations

Posted by Gil Shamir and Dong Lin, Research Software Engineers, Google Research Ever queried a recommender system and found that the same search only a few moments later or on a different device yields very different results? This is not uncommon and can be frustrating if a person is looking for something specific. As a…