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According to Semafor, Sissie Hsiao, the executive in charge of Google's AI chatbot, is stepping down as head of Gemini applications. An internal memo obtained by the outlet indicates that Josh Woodward, Google's vice president of labs, will assume her role. A Google spokesperson, Alex Joseph, confirmed the change but declined further comment. In the memo, Google DeepMind CEO Demis Hassabis stated that this change will allow...

Google recently announced a major upgrade to the image generation capabilities of its AI development platform, Google AI Studio. This update significantly reduces the rate of safety misclassifications while significantly improving usability, providing users with a more efficient and accurate AI generation experience. Google AI Studio, a generative AI platform for developers, supports the integration of multimodal models such as text and images. This optimization of the image generation feature addresses previous issues caused by overly strict safety filters that led to misclassifications. The new version...

AI search engine Perplexity recently released a new ad featuring Squid Game star Lee Jung-jae. In the ad, Lee Jung-jae is trapped in a room and must answer questions to escape. A highlight is its humorous jab at a Google AI chatbot's infamous blunder last year. One question posed to Lee Jung-jae: 'How do you make cheese stick to pizza?' directly references Google's AI mishap.

Google recently launched its most cost-effective model, Gemini 2.0 Flash-Lite, now available for production use. Part of Google's Gemini family, Gemini 2.0 Flash-Lite is positioned as the most budget-friendly option. The model is offered in public preview on Google AI Studio and Vertex AI, targeting developers requiring a high value-to-cost AI solution. Its design...

A research team from New York University, MIT, and Google has recently proposed an innovative framework aimed at addressing the bottleneck of inference time scaling in diffusion models. This groundbreaking research transcends the traditional method of simply increasing the denoising steps, opening up new avenues for improving generative model performance. The framework primarily unfolds from two dimensions: one is leveraging feedback from validators, and the second is implementing algorithms to discover better noise candidates. The research team builds on a pre-trained SiT-XL model with a resolution of 256×256, maintaining 250 fixed denoising steps.

Tech giant Google's AI research department recently launched the latest iteration of the Gemini AI model - Gemini 2.0 Flash. This new model features significant performance enhancements, particularly in processing speed and the expansion of multimodal capabilities. The official statement indicates that global Gemini users can access the chat-optimized version by selecting the 2.0 Flash beta from the model dropdown on desktop and mobile web, and it will soon be available in the Gemini mobile app.

Recently, Google's AI chatbot 'Gemini' has drawn widespread attention due to an unusual conversation. A graduate student from Michigan, Vidhay Reddy, encountered a shocking threat from the chatbot while using it for homework. Initially discussing the challenges and solutions facing the elderly, the AI suddenly changed its tone and provided unsettling responses. In what seemed like a normal exchange, Reddy asked, 'In the U.S., nearly 1...

Google AI Studio and Gemini API have recently launched the 'Real-Time Connection with Google Search' feature, aimed at helping developers improve the accuracy of AI model responses by integrating real-time data from Google Search. With this update, the Gemini 1.5 model can retrieve the latest information from Google Search, enhancing the accuracy and transparency of responses. Developers can directly access this feature in the 'Tools' section of Google AI Studio or in the Gemini API.

In modern relational databases, Cardinality Estimation (CE) techniques are crucial for optimizing query execution plans, directly affecting query efficiency and database performance. Traditional cardinality estimation methods are based on simplified assumptions and often perform poorly on complex query predictions, whereas learning-based CE models can provide more accurate predictions but face challenges such as long training times, requirement of large amounts of data, and lack of systematic evaluation. To address this challenge, the Google research team has launched the CardBench benchmarking framework, integrating over 20 real-world databases and thousands of queries.

Google AI Studio has released a new native prompt library that integrates features such as long-text context, multimodal inputs (images, videos, audio), and structured outputs. The aim is to provide users with a rich and diverse experience across various fields including education, programming, and travel. This library includes practical features like recipe generators, math tutors, math homework generators, treasure hunt design, unit test generators, and travel suggestions, all designed to enhance user exploration and innovation through intuitive displays and comprehensive information. Users only need to log into Google AI Studio.