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Google AI - Build Build with Google AI, take advantage of our AI stack, or customize and tune our models
Google AI – Our AI Journey Google developed Med-PaLM, a medically fine-tuned LLM, which was the first model to attain a passing score on a medical licensing exam-style question benchmark, demonstrating its ability to accurately answer medical questions
Google AI - Health AI Explore how teams at Google are catalyzing the adoption of human-centered AI in healthcare
Google AI - Why we focus on AI Consumers with assistive tools like Google Translate, Google Lens, Google Assistant, Project Starline, speech-to-text, Pixel Call Assist and Recorder, real-time text suggestions and summarization, and generative human-assistive capabilities across many creativeand productivity endeavors
Google AI - AI Principles Our AI Principles guide the development and deployment of our AI systems These Principles inform our frameworks and policies, such as the Secure AI Framework for security and privacy, and the Frontier Safety Framework for evolving model capabilities Our governance process covers model development, application deployment, and post-launch monitoring We identify and assess AI risks through
Google AI - Sustainability AI At Google, we have a unique opportunity to help lead the transition to a more sustainable future by investing in AI solutions that help individuals, cities, and other partners in their efforts to collectively reduce one gigaton of their carbon equivalent emissions annually by 2030
Generative AI FAQs - Google AI Now, Google’s newest AI technologies — like LaMDA, PaLM, Imagen and MusicLM — are creating entirely new ways to engage with information, from language and images to video and audio
Machine Learning - Google AI Decode YouTube influencer opinions on any company or product with Google Gen AI Learn how PaLM2 and Langchain can unlock insights from videos and revolutionize influencer analytics
Pourquoi nous nous intéressons à l’IA (et dans quel but) - Google AI Créer une infrastructure d’IA de pointe, sûre et facile à utiliser, y compris des solutions informatiques (Tensor Processing Units, Google Tensor et Colab, par exemple) et des frameworks logiciels courants (TensorFlow, Jax, Android ML et Private Compute, par exemple)