Spot The Ball: A Benchmark for Visual Social Inference

Google has upgraded its Trends Explore page, integrating Gemini capabilities to enhance the analysis of search interest and allow users to identify and compare relevant trends more effectively. This significant update aims to improve user engagement and data insights.

Google has introduced a new feature called 'Personal Intelligence' for its Gemini AI, which integrates data from Gmail, Google Photos, and YouTube to enhance user interactions. This feature aims to make the AI assistant more responsive and personalized by leveraging Google's extensive data resources.

Google has introduced a new feature for its AI model, Gemini, allowing it to scan users' photos, emails, and other data to provide more accurate responses. This feature is currently available only to paid users and is disabled by default.

Google has announced that its AI model, Gemini, can now pull context from other Google applications, enhancing its functionality and user experience. This capability allows Gemini to provide more personalized and relevant responses by integrating data from services like Gmail and Calendar, contingent on user consent.

Alphabet Inc.'s Google has announced that its Gemini artificial intelligence assistant can now proactively analyze users' data across various platforms, including Gmail, Search, Photos, and YouTube, enhancing personalization for its consumer-facing AI product.

Google has introduced a new feature for its AI model, Gemini, called 'Personal Intelligence,' which allows it to scan users' emails, photos, and other data to provide more personalized responses, contingent on user consent. This feature aims to enhance user interaction by leveraging data from various Google services, including Gmail and YouTube.

Google has launched a new beta feature for its AI model, Gemini, called 'Personal Intelligence,' which allows the AI to proactively respond to users by analyzing their emails, photos, and other data, contingent on user consent. This feature is currently off by default, giving users control over their data integration with Gemini.

The introduction of E^2-LLM (EEG-to-Emotion Large Language Model) marks a significant advancement in emotion recognition from electroencephalography (EEG) signals, addressing challenges such as inter-subject variability and the need for interpretable reasoning in existing models. This framework integrates a pretrained EEG encoder with Qwen-based large language models through a multi-stage training pipeline.