All news with #gemini tag

🔬 Google Cloud outlines an agentic AI framework to accelerate life sciences R&D by orchestrating specialized, fine-tunable models into modular workflows. It describes four agents—MedGemma for deep literature and data synthesis, TxGemma for in-silico preclinical prediction, Gemini 2.5 Pro as the cognitive orchestrator, and AlphaFold-2 plus docking tools for molecular design. The architecture maps data flows, tooling, and cloud services (Vertex AI, HPC, search) to move from target discovery through iterative Design→Dock→Predict→Refine cycles toward lab-ready lead nomination while preserving version control and compliance.

🤖Google has begun showing ads in its AI mode, the company's answer-engine experience rather than a traditional search engine. AI mode has been available for about a year and is free to all, with Google One subscribers able to toggle advanced models such as Gemini 3 Pro. Until now Google avoided ads to keep the conversational experience compelling; the new placements are labeled “sponsored” and typically appear at the bottom of AI-generated answers rather than in the right-side citation area. This looks like an experiment or optimization to improve click-through rates while complying with ad disclosure rules.

⚠️A representative YouGov survey commissioned by recruitment firm SThree found that 77% of STEM professionals in Germany use AI tools at work without approval from IT or management. Commonly used services include ChatGPT, Google Gemini and Perplexity. Experts warn this shadow IT practice can lead to GDPR breaches, inadvertent disclosure of sensitive customer or internal data and the risk that providers will retain and reuse submitted content for training. In Germany, 23% report daily use, 29% weekly and 12% monthly; respondents cite efficiency gains and technical curiosity as primary drivers.

🎨 Google is unveiling Nano Banana Pro (Gemini 3 Pro Image), a high-fidelity image generation and editing model available today in Vertex AI and Google Workspace, with a rollout to Gemini Enterprise coming soon. The model supports multi-language text rendering and on-image translation, connects to Google Search for context-aware outputs, and accepts up to 14 reference images and 4K inputs for production-grade assets. Built-in SynthID watermarking and planned copyright indemnification address commercial use and responsible deployment.

🎮 Google has released Gemini 3 Pro, a multimodal model that posts strong benchmark results and produces notable real-world demos. Early tests show top-tier scores (LMArena 1501 Elo, high marks on MMMU-Pro and Video-MMMU) and PhD-level reasoning in targeted exams. Designers reported one-shot generation of a 3D LEGO editor and a full recreation of Ridiculous Fishing. Adherence remains imperfect, so the author suggests Claude Sonnet 4.5 for routine tasks and Gemini 3 Pro for more complex queries.

🔎 Google is testing AI-powered, interactive UI answers within AI Mode, integrating Gemini 3 to generate on-the-fly interfaces tailored to queries. Instead of relying solely on text and a couple of links, Search can produce dynamic tools—such as an RNA polymerase simulator—to demonstrate concepts in action. This change could improve comprehension but may also reduce traffic to original sites and reshape the web economy.

🛍️ Black Friday shopping is increasingly fraught with staged discounts and manipulated prices, but large language models (LLMs) can help shoppers cut through the noise. Use AI like ChatGPT, Claude, or Gemini to build a wish list, track historical prices, compare alternatives, and vet sellers quickly. The article provides step-by-step prompts for price analysis, seller verification, local-market queries, and model-specific requests, and recommends security measures such as using a separate card and installing Kaspersky Premium to reduce fraud risk.

🧬 This guide demonstrates step-by-step fine-tuning of MedGemma (a Gemma 3 variant) to classify breast histopathology images using the public BreakHis dataset and a notebook-based workflow. It highlights practical choices—using an NVIDIA A100 40 GB, switching from FP16 to BF16 to avoid numerical overflows, and employing LoRA adapters for efficient training. The tutorial reports dramatic accuracy gains after merging LoRA adapters and points readers to runnable notebooks for reproducibility.

🤖 Google has made Gemini 3 available to enterprises and developers via Gemini Enterprise and Vertex AI, bringing advanced multimodal reasoning and agentic capabilities to production teams. The model can analyze text, images, video, audio, and code together, supports a 1M-token context window, and improves frontend generation, legacy code migration, and long-running tool orchestration. Early partners report faster diagnostics, richer UI prototypes, and more reliable automation across business workflows.

🤖 Google’s Gemini 3 has been spotted in AI Studio, suggesting an imminent rollout that could begin within hours or days. The AI Studio entry references how temperature influences reasoning — noting "For Gemini 3, best results at default 1.0. Lower values may impact reasoning" — and highlights controls such as context size and temperature. Earlier sightings on Vertex AI show a preview build named gemini-3-pro-preview-11-2025, while Google is also testing an image model codenamed GEMPIX2 (Nano Banana 2).

🚀 Google Cloud has launched the Production-Ready AI Learning Path, a free curriculum designed to guide developers from prototype to production. Drawing on an internal playbook, the series pairs Gemini models with production-grade tools like Vertex AI, Google Kubernetes Engine, and Cloud Run. Modules cover LLM app development, open model deployment, agent building, security, RAG, evaluation, and fine-tuning. New modules will be added weekly through mid-December.

🚀 Google Cloud introduces hands-on labs for Gemma 3, a family of lightweight open models offering multimodal (text and image) capabilities and efficient performance on smaller hardware footprints. The labs present two deployment paths: a serverless approach using Cloud Run with GPU support, and a platform approach using GKE for scalable production environments. Choose Cloud Run for simplicity and cost-efficiency or GKE Autopilot for control and robust orchestration to move models from local testing to production.

🚀 Google Cloud reports a new state-of-the-art Single Trained Model Track score on the BIRD benchmark, achieving 76.13 with a fine-tuned Gemini 2.5-pro. The team credits rigorous data filtering, multitask supervised fine-tuning, and test-time self-consistency selection for the gains. These improvements bolster NL2SQL features in AlloyDB AI and BigQuery, and enhance developer tooling such as Gemini Code Assist for reliable SQL generation.

🤖 This recap of The Agent Factory episode summarizes a conversation between Amit Maraj and Ravin Kumar (DeepMind) about building open-source agentic models. It highlights how agent training differs from standard ML, emphasizing trajectory-based data, a two-stage approach of supervised fine-tuning followed by reinforcement learning, and the paramount role of evaluation. Practical guidance includes defining a 50-example final exam up front and considering hybrid setups that use a powerful API like Gemini as a router alongside specialized open models.

💬 Google Cloud has made Looker Conversational Analytics generally available, bringing natural-language data queries to all Looker users. Built on the Looker semantic layer and powered by Gemini and Google’s agentic frameworks, the feature provides instant, explainable answers and supports multi-turn exploration across up to five connected Explores. Analysts can build and share agents, use LookML for fine tuning, and rely on a governed foundation that surfaces “How was this calculated?” explanations. Admins can enable the capability now to accelerate data discovery and improve self-service across teams.

🚀 Google outlines its plan to embed Generative AI across enterprise platforms and endpoints, integrating Gemini into Chrome Enterprise, Android, Pixel phones and Chromebook Plus devices. The post highlights the general availability of Cameyo by Google to virtualize legacy and modern apps in the cloud and the launch of Gemini in Chrome with enterprise-grade controls. It also previews Android XR and Pixel features powered by Gemini Nano, while expanding data loss prevention and a one-click SecOps integration to help IT secure AI-driven workflows.

🔒 Google on Tuesday introduced Private AI Compute, a cloud privacy capability that aims to deliver on-device-level assurances while harnessing the scale of Gemini models. The service uses Trillium TPUs and Titanium Intelligence Enclaves (TIE) and relies on an AMD-based Trusted Execution Environment to encrypt and isolate memory on trusted nodes. Workloads are mutually attested, cryptographically validated, and ephemeral so inputs and inferences are discarded after each session, with Google stating data remains private to the user — 'not even Google.' An external assessment by NCC Group flagged a low-risk timing side channel in the IP-blinding relay and three attestation implementation issues that Google is mitigating.

🤝 Google Cloud is increasing local AI compute in India with its AI Hypercomputer powered by Trillium TPUs, enabling training and serving of advanced Gemini models with data residency and sovereignty controls. New local offerings include batch support for Gemini 2.5 Flash, a preview of Document AI, and real‑time grounding using Google Maps for location‑aware responses. Google is also supporting Indic Arena at IIT Madras with cloud credits to benchmark Indian multilingual models and to help grow the local AI ecosystem.

🧠 Gemini Code Assist on GitHub now supports persistent memory that learns from merged pull request interactions to capture a team's coding standards, style, and best practices. The memory is stored securely in a Google-managed project specific to each installation and is applied selectively to relevant reviews. It infers reusable rules from review threads and uses them both to shape initial analysis and to filter draft suggestions so the agent adapts over time and reduces repetitive feedback.

🚀 Deploy the official n8n Docker image to Cloud Run in minutes to run scalable, serverless AI workflows. Cloud Run scales from zero and persists data in Cloud SQL while you only pay for active usage. The post shows how to call Gemini as the agent LLM and optionally connect workflows to Google Workspace via OAuth for Gmail, Calendar, and Drive. For production, follow the n8n docs to add Secrets Manager, Cloud SQL, and Terraform-based deployment.