Author: AI Generated Robotic Content

I recently started playing with DALL-E 2, which will attempt to generate an image to go with whatever text prompt you give it. Like its predecessor DALL-E, it uses CLIP, which OpenAI trained on a huge collection of internet images and nearby text. I’ve experimented with a few methods based on CLIP, but DALL-E generates …

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If you’re going to open a late-night donut shop, you’re going to need a unique set of over-the-top donuts to set the proper festive atmosphere. But how to keep the ideas coming? I decided to see what donut ideas I could get using OpenAI’s GPT-3 text-generating models. I collected seven of the weirdest donuts and …

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Posted by Tingbo Hou and Juhyun Lee, Software Engineers, Google In recent years video conferencing has played an increasingly important role in both work and personal communication for many users. Over the past two years, we have enhanced this experience in Google Meet by introducing privacy-preserving machine learning (ML) powered background features, also known as …

Read more “High-Definition Segmentation in Google Meet”

Posted by Aparna Taneja, Software Engineer, and Milind Tambe, Principal Scientist, Google Research, India Research Lab The widespread availability of mobile phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. While advanced applications on smartphones allow for richer multimedia content and two-way communication between beneficiaries and health coaches, …

Read more “Using ML to Boost Engagement with a Maternal and Child Health Program in India”

Posted by Yilin Wang, Staff Software Engineer, YouTube and Feng Yang, Senior Staff Software Engineer, Google Research Online video sharing platforms, like YouTube, need to understand perceptual video quality (i.e., a user’s subjective perception of video quality) in order to better optimize and improve user experience. Video quality assessment (VQA) attempts to build a bridge …

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Posted by Yutian Chen, Staff Research Scientist, DeepMind, and Xingyou (Richard) Song, Research Scientist, Google Research, Brain Team One of the most important aspects in machine learning is hyperparameter optimization, as finding the right hyperparameters for a machine learning task can make or break a model’s performance. Internally, we regularly use Google Vizier as the …

Read more “OptFormer: Towards Universal Hyperparameter Optimization with Transformers”

Posted by Brian Ichter and Karol Hausman, Research Scientists, Google Research, Brain Team Over the last several years, we have seen significant progress in applying machine learning to robotics. However, robotic systems today are capable of executing only very short, hard-coded commands, such as “Pick up an apple,” because they tend to perform best with …

Read more “Towards Helpful Robots: Grounding Language in Robotic Affordances”

Posted by Rolf Jagerman and Honglei Zhuang, Software Engineers, Google Research Ranking is a core problem across a variety of domains, such as search engines, recommendation systems, or question answering. As such, researchers often utilize learning-to-rank (LTR), a set of supervised machine learning techniques that optimize for the utility of an entire list of items …

Read more “Rax: Composable Learning-to-Rank Using JAX”