Our next-generation model: Gemini 1.5
The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.
Video generation models as world simulators
We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of …
Unveiling the transformative AI technology behind watsonx Orders
You’re headed to your favorite drive-thru to grab fries and a cheeseburger. It’s a simple order and as you pull in you notice there isn’t much of a line. What could possibly go wrong? Plenty. The restaurant is near a busy freeway with roaring traffic noise and airplanes fly low overhead as they approach the …
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Detect anomalies in manufacturing data using Amazon SageMaker Canvas
With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. Industrial companies increasingly look at data analytics and data-driven decision-making to increase resource efficiency across their entire portfolio, from operations to performing …
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Google Cloud expands access to Gemini models for Vertex AI customers
In December, Google announced Gemini, our most capable and general model yet. Since December, select customers like Samsung and Palo Alto Networks have been building sophisticated AI agents with Gemini models in Vertex AI, unlocking new levels of productivity, personalized learning, and more for their users. Today, we’re bringing more Gemini models to our customers …
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Learning the importance of training data under concept drift
Posted by Nishant Jain, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research The constantly changing nature of the world around us poses a significant challenge for the development of AI models. Often, models are trained on longitudinal data with the hope that the training data used will accurately represent inputs the model may receive …
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Veterans Affairs & Palantir
How data-driven acquisition identified over $90M in savings for VA in 6 months Editor’s Note: This post was written jointly with the U.S. Department of Veterans Affairs. Intro The U.S. Department of Veterans Affairs (VA) is one of the largest integrated health care systems in the United States, providing care at 1,298 health care facilities to over …
Disrupting malicious uses of AI by state-affiliated threat actors
We terminated accounts associated with state-affiliated threat actors. Our findings show our models offer only limited, incremental capabilities for malicious cybersecurity tasks.
The most valuable AI use cases for business
When thinking of artificial intelligence (AI) use cases, the question might be asked: What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. But right now, pure AI can …
Learning the importance of training data under concept drift
Posted by Nishant Jain, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research The constantly changing nature of the world around us poses a significant challenge for the development of AI models. Often, models are trained on longitudinal data with the hope that the training data used will accurately represent inputs the model may receive …
Read more “Learning the importance of training data under concept drift”