Understanding Alignment in Multimodal LLMs: A Comprehensive Study

Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like hallucination. In MLLMs, hallucination can occur not only by stating incorrect facts but also by …

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From weeks to minutes: How Formula 1® uses agentic AI on AWS to accelerate data operations

Formula 1® (F1) engages an audience of over 800 million fans globally across digital platforms, F1 TV, social media, ticketing, and merchandise year-round. Races happen every two weeks. Fan engagement windows are measured in minutes and commercial decisions need to move at the speed of the grid. Behind the scenes, F1’s marketing technology (MarTech) platform, Customer …

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Real-world mainframe modernization with AI: A safe, scalable path from mainframe to cloud

For too long, enterprises with legacy mainframe estates have been faced with a high-stakes dilemma: continue maintaining their mainframes, essentially kicking the modernization can down the road (they know they will need to deal with it eventually), or perform a dangerous “big bang” migration with many unknowns and risks.  At Google Cloud, we propose an …

Noninvasive AI-based system translates brain signals into written text

Some physical injuries and neurological conditions can temporarily or permanently impair movement, leaving some people unable to speak, type on keyboards or use electronic devices. Brain-computer interfaces (BCIs), systems that can decode brain activity patterns and convert them into computer commands or written text, could be of great value for paralyzed patients.

AI reduces sensory hallucinations, even at night or in smoke

Multimodal large language models (MLLMs), which process multiple types of sensory information, such as text, images and audio, at the same time, are rapidly expanding the range of applications for artificial intelligence (AI). However, in real-world environments, these models can misinterpret the physical characteristics of sensors, mistakenly identify objects or claim to hear sounds that …

Modeling Device Capabilities for Analytics

by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh Selveraj Netflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem of devices. However, not all devices are created equal. Hardware limitations such as available RAM, CPU cores, …

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Announcing the Agentic Catalog Experience in Amazon Quick

As organizations embrace AI-powered analytics, the value of a natural language (Text2SQL) answer is only as good as the business context behind it. We’re entering a phase where semantic richness (table and column descriptions, and relationships) must flow directly from where it’s authored in upstream data catalogs and semantic tools into the AI products that …

What’s new in AI infrastructure and orchestration this month

At Google, AI is a soup-to-nuts endeavor. Obviously, we make leading AI models like Gemini and Nano Banana. We incorporate AI into the tools you use every day (think Gmail, BigQuery, AlloyDB, Google Cloud Code and Google Cloud Assist). We make software frameworks to help you build with AI, like Gemini Enterprise Agent Platform, JAX, …