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Posted by Nicholas Rubin, Senior Research Scientist, and Ryan Babbush, Head of Quantum Algorithms, Quantum AI Team If you’ve paid attention to the quantum computing space, you’ve heard the claim that in the future, quantum computers will solve certain problems exponentially more efficiently than classical computers can. They have the potential to transform many industries,…

After a procession of overhyped technologies like Web3, the metaverse, and blockchain, executives are bracing for the tidal wave of generative AI, a shift some consider to be on par with the advent of the internet or the desktop computer. But with power comes responsibility, and generative AI offers as much risk as reward. The…

Ethan and Lilach Mollick’s paper Assigning AI: Seven Approaches for Students with Prompts explores seven ways to use AI in teaching. (While this paper is eminently readable, there is a non-academic version in Ethan Mollick’s Substack.) The article describes seven roles that an AI bot like ChatGPT might play in the education process: Mentor, Tutor,…

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. I hate going to the gym. Last year I hired a personal trainer for six months in the hope she would brainwash me into adopting healthy exercise habits longer-term. It was…

Posted by Sagar M. Waghmare, Senior Software Engineer, and Kimberly Wilber, Software Engineer, Google Research, Perception Team As most people navigate their everyday world, they process visual input from the environment using an eye-level perspective. Unlike robots and self-driving cars, people don’t have any “out-of-body” sensors to help guide them. Instead, a person’s sensory input…

When searching for flights on Google, you may have noticed that each flight’s carbon-emission estimate is now presented next to its cost. It’s a way to inform customers about their environmental impact, and to let them factor this information into their decision-making. A similar kind of transparency doesn’t yet exist for the computing industry, despite…

Autonomous shopping carts that follow grocery store customers and robots that pick ripe cucumbers faster than humans may grab headlines, but the most compelling applications of AI and ML technology are behind the scenes. Increasingly, organizations are finding substantial efficiency gains by applying AI- and ML-powered tools to back-office procedures such as document processing, data…

Enterprise adoption of AI is ready to shift into higher gear. The capabilities of generative AI have captured management attention across the organization, and technology executives are moving quickly to deploy or experiment with it. Many organizations intend to increase their spending on the wider family of AI capabilities and the data infrastructure that supports…

Posted by Carlos Esteves and Ameesh Makadia, Research Scientists, Google Research, Athena Team Typical deep learning models for computer vision, like convolutional neural networks (CNNs) and vision transformers (ViT), process signals assuming planar (flat) spaces. For example, digital images are represented as a grid of pixels on a plane. However, this type of data makes…

Imagine you’re in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they’re always looking out for different things. If they’re both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes…