News from the AI & ML world
DeeperML - #bigquery
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@cloud.google.com
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Google is significantly expanding the AI and ML capabilities within its BigQuery and Vertex AI platforms. BigQuery is receiving a boost with the integration of the TimesFM forecasting model, a state-of-the-art, pre-trained model from Google Research designed to simplify forecasting problems. This managed and scalable engine enables users to generate forecasts for both single and millions of time series within a single query. Additionally, BigQuery now supports structured data extraction and generation using large language models (LLMs) through the AI.GENERATE_TABLE function, alongside new row-wise inference functions, expanded model choice with Gemini and OSS models, and the general availability of the Contribution Analysis feature.
NotebookLM is also seeing expansion with the "Audio Overviews" feature now available in approximately 75 languages. This feature, powered by Gemini, allows users to listen to AI-generated summaries of documents, slides, web pages, and YouTube transcripts in multiple languages. This feature distills any mix of documents into a scripted back-and-forth between two synthetic hosts. Users can direct tone and depth through a prompt and then download an MP3 or keep playback inside the notebook. Early testers report that multilingual voices make long reading lists easier to digest on commutes and provide an alternative channel for blind or low-vision audiences. Furthermore, Google is experimenting with AI-powered language learning formats through its “Little Language Lessons,” integrated directly into NotebookLM and running on Gemini. These tools support situational learning, generating content dynamically based on user-described scenarios, rather than relying on fixed vocabulary lists. Google is also preparing new Gemini AI subscription tiers, potentially including a "Gemini Ultra" plan, evidenced by code discoveries in the Gemini web interface referencing distinct tiers with varying capabilities and usage limits.
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@www.bigdatawire.com
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Google Cloud Next 2025 featured a series of announcements focused on enhancing data analytics capabilities within Google Cloud, particularly through advancements to BigQuery. These enhancements center around a vision for AI-native data analytics, aiming to make data work more conversational, contextual, and intelligent. Key innovations include the introduction of unified governance in BigQuery, AI-powered agents for data engineering and data science tasks, and the integration of Gemini, Google's flagship foundation model, to drive these intelligent capabilities. These developments are designed to simplify data management, improve data quality, and accelerate the generation of AI-driven insights for businesses.
The new intelligent unified governance in BigQuery is designed to help organizations discover, understand, and leverage their data assets more effectively. This includes a universal, AI-powered data catalog that natively integrates Dataplex, BigQuery sharing, security, and metastore capabilities. The unified governance brings together business, technical, and runtime metadata, providing end-to-end data-to-AI lineage, data profiling, insights, and secure sharing. A universal semantic search allows users to find the right data by asking questions in natural language. These advancements promise to transform governance from a burden into a powerful tool for data activation, simplifying data and AI management. A significant aspect of these enhancements is the introduction of specialized AI agents within BigQuery and Looker. These agents are tailored to different user roles, such as data engineers and business analysts, assisting with tasks like building data pipelines, model development, and querying data in plain English. Powered by Gemini, these agents provide suggestions based on information collected through the new BigQuery Knowledge Engine, which understands schema relationships, business terms, and query history. These agents are designed to make more data available to more people without them doing more work, ultimately transforming how data professionals interact with data.
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