The Reason Many AI and Analytics Projects Fail—and How to Make Sure Yours Doesn’t
Topping the list of executive priorities for 2023—a year heralded by escalating economic woes and climate risks—is the need for data driven insights to propel efficiency, resiliency, and other key initiatives. Many companies have been experimenting with advanced analytics and artificial intelligence (AI) to fill this need. Now, they must turn their proof of concept…
Read MoreThe Delicate Dance Between AI and Human Agents
Artificial intelligence will soon take center stage in your contact center — if it hasn’t already. Artificial intelligence (AI) uptake increased dramatically over the last few years. A 2022 PwC report revealed that more than 70% of companies were already using or planning to deploy AI in some form within their business operations. Business leaders…
Read MoreEHR-Safe: Generating High-Fidelity and Privacy-Preserving Synthetic Electronic Health Records
Posted by Jinsung Yoon and Sercan O. Arik, Research Scientists, Google Research, Cloud AI Team Analysis of Electronic Health Records (EHR) has a tremendous potential for enhancing patient care, quantitatively measuring performance of clinical practices, and facilitating clinical research. Statistical estimation and machine learning (ML) models trained on EHR data can be used to predict…
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Differential Privacy Accounting by Connecting the Dots
Posted by Pritish Kamath and Pasin Manurangsi, Research Scientists, Google Research Differential privacy (DP) is an approach that enables data analytics and machine learning (ML) with a mathematical guarantee on the privacy of user data. DP quantifies the “privacy cost” of an algorithm, i.e., the level of guarantee that the algorithm’s output distribution for a…
Read MoreAccelerating Text Generation with Confident Adaptive Language Modeling (CALM)
Posted by Tal Schuster, Research Scientist, Google Research Language models (LMs) are the driving force behind many recent breakthroughs in natural language processing. Models like T5, LaMDA, GPT-3, and PaLM have demonstrated impressive performance on various language tasks. While multiple factors can contribute to improving the performance of LMs, some recent studies suggest that scaling…
Read MoreWho Said What? Recorder’s On-device Solution for Labeling Speakers
Posted by Quan Wang, Senior Staff Software Engineer, and Fan Zhang, Staff Software Engineer, Google In 2019 we launched Recorder, an audio recording app for Pixel phones that helps users create, manage, and edit audio recordings. It leverages recent developments in on-device machine learning to transcribe speech, recognize audio events, suggest tags for titles, and…
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Private Ads Prediction with DP-SGD
Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. With increasing focus on online privacy, there’s an opportunity to identify ML algorithms…
Read MoreVoice Instructions – Multiple Robots in Real Time
Posted by Corey Lynch, Research Scientist, and Ayzaan Wahid, Research Engineer, Robotics at Google A grand vision in robot learning, going back to the SHRDLU experiments in the late 1960s, is that of helpful robots that inhabit human spaces and follow a wide variety of natural language commands. Over the last few years, there have…
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Robots That Write Their Own Code
Posted by Jacky Liang, Research Intern, and Andy Zeng, Research Scientist, Robotics at Google <!––><!––> A common approach used to control robots is to program them with code to detect objects, sequencing commands to move actuators, and feedback loops to specify how the robot should perform a task. While these programs can be expressive, re-programming…
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Open Images V7 — Now Featuring Point Labels
Posted by Rodrigo Benenson, Research Scientist, Google Research Open Images is a computer vision dataset covering ~9 million images with labels spanning thousands of object categories. Researchers around the world use Open Images to train and evaluate computer vision models. Since the initial release of Open Images in 2016, which included image-level labels covering 6k…
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