Posted by Zalán Borsos, Research Software Engineer, and Marco Tagliasacchi, Senior Staff Research Scientist, Google Research The recent progress in generative AI unlocked the possibility of creating new content in several different domains, including text, vision and audio. These models often rely on the fact that raw data is first converted to a compressed format…
A significant proportion of people paid to train AI models may be themselves outsourcing that work to AI, a new study has found. It takes an incredible amount of data to train AI systems to perform specific tasks accurately and reliably. Many companies pay gig workers on platforms like Mechanical Turk to complete tasks that…
Posted by Jimmy Tobin and Katrin Tomanek, Software Engineers, Google Research, AI for Social Good Google’s AI for Social Good team consists of researchers, engineers, volunteers, and others with a shared focus on positive social impact. Our mission is to demonstrate AI’s societal benefit by enabling real-world value, with projects spanning work in public health,…
Posted by Trond Andersen and Yuri Lensky, Research Scientists, Google Quantum AI Team Imagine you’re shown two identical objects and then asked to close your eyes. When you open your eyes, you see the same two objects in the same position. How can you determine if they have been swapped back and forth? Intuition and…
Multi-tenant systems are invaluable for modern, fast-paced businesses. These systems allow multiple users and teams to access and use them at the same time. Machine learning operations (MLOps) teams, in particular, benefit greatly from using multi-tenant systems. MLOps teams that don’t leverage multi-tenant systems can fall victim to inefficiency, inconsistency, duplicative work, and bumpy onboarding—adding…
Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. Knowing what makes up the supply chain is critical to enforcing the security of the AI system, establishing trust with the consumer of the AI’s output, and protecting…
Who’s afraid of the big bad bots? A lot of people, it seems. The number of high-profile names that have now made public pronouncements or signed open letters warning of the catastrophic dangers of artificial intelligence is striking. Hundreds of scientists, business leaders, and policymakers have spoken up, from deep learning pioneers Geoffrey Hinton and…
Posted by Shaina Mehta, Program Manager, Google This week marks the beginning of the premier annual Computer Vision and Pattern Recognition conference (CVPR 2023), held in-person in Vancouver, BC (with additional virtual content). As a leader in computer vision research and a Platinum Sponsor, Google Research will have a strong presence across CVPR 2023 with…
How will advances in computing transform human society? MIT students contemplated this impending question as part of the Envisioning the Future of Computing Prize — an essay contest in which they were challenged to imagine ways that computing technologies could improve our lives, as well as the pitfalls and dangers associated with them. Offered for…
Posted by Juhyun Lee and Raman Sarokin, Software Engineers, Core Systems & Experiences The proliferation of large diffusion models for image generation has led to a significant increase in model size and inference workloads. On-device ML inference in mobile environments requires meticulous performance optimization and consideration of trade-offs due to resource constraints. Running inference of…