# Synthetic Data Generation A process that uses seed data to create artificial data with some of the seed data's statistical characteristics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics DevFeed: [NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics]() Original publisher: [Read original article]() Author: Lukas Zbinden; Javier Gamazo; Mostafa Toloui; Sean Huver Published: 2026-07-27T09:32:20Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Cosmos](), [Robotics](), [foundation-models](), [real-time](), [Nvidia](), [Synthetic Data Generation](), [synthetic-data](), [data](), [datasets](), [GPU](), [Streaming](), [Inference]() Tags: [cosmos](), [data](), [foundation-models](), [generation](), [generative](), [gpu](), [inference](), [nvidia](), [real-time](), [robotics](), [streaming](), [synthetic-data](), [synthetic-data-generation](), [training]() ### AI overview NVIDIA introduces Cosmos-H-Dreams, a real-time, action-conditioned generative simulator for surgical robotics. The model generates future surgical video from an initial RGB frame and live robot kinematics, enabling interactive closed-loop control, offline policy evaluation, and synthetic data generation. ### Source excerpt World foundation models offer a different path. Instead of manually authoring every object and physical interaction, they learn visual dynamics directly from synchronized video and robot kinematics. NVIDIA's Cosmos-H-Surgical-Simulator demonstrated this approach by generating future surgical video from an initial scene and a sequence of robot actions. It enabled faster-than-physical evaluation and synthetic data generation across the Open-H-Embodiment ecosystem. ## Synthetic Data Generation for Financial AI Research with NVIDIA NeMo DevFeed: [Synthetic Data Generation for Financial AI Research with NVIDIA NeMo]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-09T19:40:37Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Synthetic Data Generation](), [synthetic-data](), [Large Language Model](), [Fine-tuning](), [Natural language processing](), [Nemotron](), [vllm](), [datasets]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-ready-data](), [cloud-services](), [compute](), [data](), [data-science](), [featured](), [financial-services](), [fine-tuning](), [generation](), [hardware](), [inference](), [llms](), [models](), [natural-language-processing](), [nemo](), [nemotron](), [nvidia](), [research](), [structured-generation](), [synthetic](), [synthetic-data](), [synthetic-data-generation](), [vllm](), [workflow]() ### AI overview This developer article presents an iterative pipeline for generating a diverse synthetic dataset of more than 500,000 financial news headlines. It combines NeMo Data Designer for structured generation, NeMo Curator for semantic deduplication, Nemotron models for synthesis, and a farthest-from-centroid few-shot strategy to reduce repetition and correct category imbalance. ### Source excerpt Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings... ## Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools DevFeed: [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-06-30T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Omniverse](), [Optimization](), [Tooling](), [GPU](), [Nvidia](), [CUDA](), [PyTorch](), [Physical AI](), [Robotics](), [Synthetic Data Generation](), [real-time](), [Reinforcement learning]() Tags: [3d](), [ai](), [autonomous](), [autonomous-vehicles](), [cuda](), [developer-tools](), [developer-tools-techniques](), [driving](), [engineering](), [featured](), [gpu](), [lidar](), [machine-learning](), [nvidia](), [omniverse](), [optimization](), [performance](), [physical-ai](), [real-time](), [reinforcement-learning](), [simulation-modeling-design](), [synthetic-data-generation]() ### AI overview This article explains how NVIDIA Nsight Developer Tools can optimize the NVIDIA Omniverse NuRec neural reconstruction pipeline. It focuses on reducing GPU-intensive reconstruction and rendering costs to improve engineering iteration and move toward real-time performance. ### Source excerpt NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such... ## Datadog acquires Adaptive ML DevFeed: [Datadog acquires Adaptive ML]() Original publisher: [Read original article]() Author: Alexis Lê-Quôc Published: 2026-06-30T00:00:00Z Content type: news Language: en Sources: [Datadog | The Monitor blog]() Topics: [Artificial Intelligence](), [agent observability](), [Frontier AI](), [Reinforcement learning](), [Synthetic Data Generation](), [A/B Testing](), [Deployment](), [observability](), [Security](), [real-time]() Tags: [a-b-testing](), [acquisition](), [ai](), [ai-agents](), [autonomous](), [data](), [deployment](), [frontier-ai](), [models](), [observability](), [platform](), [production](), [real-time](), [reinforcement-learning](), [security](), [synthetic-data-generation]() ### AI overview Datadog announces the acquisition of Adaptive ML, whose Adaptive Engine helps enterprises build, own, and deploy specialized AI agents and models. The platform supports fine-tuning open models with reinforcement learning and synthetic data, evaluating them with AI judges and A/B testing, and using production signals to improve subsequent training. ### Source excerpt Datadog has acquired Adaptive ML, a platform for building, owning, and deploying specialized AI agents and models. ## Designing synthetic datasets for the real world: Mechanism design and reasoning from first principles DevFeed: [Designing synthetic datasets for the real world: Mechanism design and reasoning from first principles]() Original publisher: [Read original article]() Published: 2026-04-16T14:41:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [datasets](), [Synthetic Data Generation](), [synthetic-data](), [Machine learning](), [machine learning research](), [Test coverage](), [Scalability]() Tags: [ai](), [datasets](), [evaluation](), [generation](), [generative-ai](), [machine-intelligence](), [machine-learning](), [machine-learning-research](), [natural-language-processing](), [privacy](), [reasoning](), [research](), [scalability](), [synthetic](), [synthetic-data-generation]() ### AI overview Google Research introduces Simula, a framework that treats synthetic data generation as dataset-level mechanism design. It uses reasoning from first principles to control coverage, diversity, complexity, and quality for scalable generation in data-scarce or privacy-sensitive domains. ### Source excerpt Generative AI ## Build a Domain-Specific Embedding Model in Under a Day DevFeed: [Build a Domain-Specific Embedding Model in Under a Day]() Original publisher: [Read original article]() Author: Steve Han; Rucha Apte; Sean Sodha; Oliver Holworthy Published: 2026-03-20T19:38:16Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [synthetic-data](), [Synthetic Data Generation](), [Model Development](), [Inference](), [model-deployment](), [NeMo](), [Nemotron](), [NVIDIA NIM](), [TensorRT](), [Claude](), [API keys]() Tags: [api](), [fine-tuning](), [gpu](), [inference](), [nemo](), [nemotron](), [nim](), [nvidia](), [nvidia-nim](), [synthetic](), [synthetic-data](), [synthetic-data-generation]() ### AI overview A tutorial showing how to fine-tune a general-purpose embedding model for a specific domain in less than a day using synthetic question-answer pairs generated from domain documents. It covers data generation, contrastive training, retrieval evaluation, and deployment, using NVIDIA NeMo components and a Llama-Nemotron embedding model. ### Source excerpt With a single GPU and less than a day of training time, you can transform a general-purpose embedding model into one that truly understands your domain, no manual labeling required. To help you hit the ground running, we are also releasing a ready-to-use synthetic training dataset generated from NVIDIA's public documentation using this exact pipeline. ## Mistral AI partners with NVIDIA to accelerate open frontier models DevFeed: [Mistral AI partners with NVIDIA to accelerate open frontier models]() Original publisher: [Read original article]() Published: 2026-03-16T20:00:00Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Nvidia](), [foundation-models](), [Open Source](), [Nemotron](), [Fine-tuning](), [Model Development](), [NVIDIA DGX](), [DGX Cloud](), [Synthetic Data Generation](), [model architecture](), [NeMo]() Tags: [ai](), [dgx-cloud](), [fine-tuning](), [foundation-models](), [models](), [multimodal](), [nemo](), [nemotron](), [nvidia](), [open](), [optimization](), [post-training](), [synthetic](), [synthetic-data-generation]() ### AI overview Mistral AI announces a partnership with NVIDIA and its founding membership in the NVIDIA Nemotron Coalition. The collaboration will develop open frontier AI models using Mistral AI's model expertise and NVIDIA's compute, development tools, and synthetic-data pipelines. The coalition's first initiative will support the NVIDIA Nemotron 4 family, while Mistral AI also releases Mistral Small 4 for developers, researchers, and organizations. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Teaching AI to read a map DevFeed: [Teaching AI to read a map]() Original publisher: [Read original article]() Published: 2026-02-17T21:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Machine Perception](), [Synthetic Data Generation](), [multimodal](), [datasets](), [AI Models](), [Open Source](), [Language models](), [large-language-models](), [Google]() Tags: [ai](), [ai-models](), [datasets](), [gemini](), [generation](), [google](), [images](), [language-models](), [large-language-models](), [machine-perception](), [multimodal](), [open-source](), [open-source-models-datasets](), [synthetic-data](), [synthetic-data-generation]() ### AI overview Google Research presents MapTrace, a task, dataset, and synthetic data generation pipeline for teaching multimodal large language models to trace valid routes on maps. The work addresses models' difficulty with spatial, geometric, and topological reasoning and releases 2 million generated question-answer pairs using Gemini 2.5 Pro and Imagen-4 Models. ### Source excerpt Machine Perception ## Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam DevFeed: [Booking.com 2026 GenAI and ML PhD Research Internship in Amsterdam]() Original publisher: [Read original article]() Author: Yang Yang Published: 2026-02-05T10:39:26Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Machine Learning & Artificial Intelligence](), [genai](), [AI Research](), [Python](), [Large Language Model](), [Apache Spark](), [SQL](), [Hadoop](), [Reinforcement learning](), [Synthetic Data Generation](), [Transformer]() Tags: [2026](), [ai](), [big-data](), [blog-posts](), [featured](), [genai](), [internship](), [machine-learning](), [ml](), [python](), [research]() ### AI overview Booking.com is recruiting current PhD students in quantitative fields for a three-month GenAI and machine learning research internship in Amsterdam in 2026. Projects include LLM alignment, transformer explainability, embeddings, context engineering, and synthetic data generation. ### Source excerpt At Booking.com, we don't just use Machine Learning -- we use it to solve some of the most complex travel challenges in the world. We're looking for the next generation of researchers to join our Machine Learning community in Amsterdam for a 3-month deep dive into cutting-edge AI. The Program As a Research Intern, you'll be embedded in our teams, working alongside world-class mentors. Your mission? To tackle real-world problems and push the boundaries of the state-of-the-art. Are You the One? We're looking for current PhD students in quantitative fields (CS, Math, AI, Physics) who can conduct independent research and have a solid grip on Python and Big Data tech (SQL, Spark, Hadoop). What's in it for you? You won't just be "an intern". You'll be a contributor to our Machine Learning community. You'll have the opportunity to contribute to the existing efforts of the Machine Learning teams, participate in internal knowledge-sharing sessions, and enjoy the collaborative, high-energy environment of our Amsterdam HQ. Projects Regularized Target Encoding for large real-world datasets Multi-Agent Collaboration Aligning LLMs with user feedback via reinforcement learning Multi-level treatments Interpretable Foundations: Explainability Methods for Transformer Models on Sequential Event Data Scalable and generalisable ID embedding learning Improving property embeddings with better handling of rich and long-context data Utility-aware retrieval for context engineering in travel planning Synthetic Data Generation in Images Requirements We are looking for independent researchers with strong understanding of Machine Learning topics (see requirements for each project in the Linkedin ad), have a track record of peer-reviewed publications and a passion for solving complex problems. Why Booking.com? You'll join a vibrant, diverse community of data scientists and researchers who love to experiment. Beyond the code, you'll experience the unique culture of our Amsterdam headquarters -- a hub ## A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums DevFeed: [A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums]() Original publisher: [Read original article]() Published: 2025-10-20T21:54:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Generative AI](), [Synthetic Data Generation](), [synthetic-data](), [Image](), [Google](), [datasets](), [Machine learning]() Tags: [2025](), [ai](), [ai-models](), [data](), [datasets](), [fine-tuning](), [generation](), [generative](), [generative-ai](), [google](), [image](), [images](), [machine-intelligence](), [machine-learning](), [privacy](), [security-privacy-and-abuse-prevention](), [synthetic](), [synthetic-data](), [synthetic-data-generation](), [training]() ### AI overview Google Research introduces a method for generating differentially private synthetic photo albums. The approach translates image data into an intermediate text representation and generates albums hierarchically to preserve thematic coherence and character consistency across multiple photos. It uses differentially private fine-tuning, such as DP-SGD, to produce representative synthetic data without unique details from individual users. ### Source excerpt Generative AI ## Nemotron-Personas-India: Synthesized Data for Sovereign AI DevFeed: [Nemotron-Personas-India: Synthesized Data for Sovereign AI]() Original publisher: [Read original article]() Author: Kiran Praveen; Utkarsh Vaidya; Evan A; Lipika Ramaswamy; Dhruv Nathawani; Dane Corneil; Yev Meyer Published: 2025-10-13T23:00:42Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Synthetic Data Generation](), [synthetic-data](), [datasets](), [Sovereign AI](), [NeMo](), [Nemotron](), [LLMs]() Tags: [ai](), [ai-adoption](), [data](), [datasets](), [evaluation](), [generation](), [india](), [language](), [llms](), [model](), [models](), [nemo](), [nemotron](), [nvidia](), [open-source](), [synthetic](), [synthetic-data](), [synthetic-data-generation]() ### AI overview NVIDIA releases Nemotron-Personas-India, an open synthetic dataset of Indic personas designed to address the lack of multilingual and culturally representative data for Indian AI systems. Built with NeMo Data Designer and licensed under CC BY 4.0, it contains 21 million personas across English and Hindi in Devanagari and Latin scripts, with demographic, geographic, occupational, and cultural attributes. ### Source excerpt India represents one of the world's largest AI opportunities -- with over 700 million internet users, a multitude of languages, and a rapidly growing developer ecosystem. Yet, most open datasets reflect Western norms and English-only contexts, creating a data gap that limits AI adoption in India's multilingual, multi-script environment. ## NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets DevFeed: [NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets]() Original publisher: [Read original article]() Author: Ming-Yu Liu; Hanzi Mao; Jinwei Gu; Pranjali Joshi; Asawaree Published: 2025-03-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Cosmos](), [Physical AI](), [Artificial Intelligence](), [Robotics](), [Synthetic Data Generation](), [datasets](), [autonomous vehicles](), [foundation-models](), [Isaac](), [Omniverse](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [autonomous-vehicles](), [community](), [cosmos](), [datasets](), [foundation-models](), [isaac](), [nvidia](), [omniverse](), [open-source](), [physical-ai](), [research](), [robotics](), [synthetic-data-generation]() ### AI overview NVIDIA announces Cosmos Transfer, an open world foundation model that generates controllable, photorealistic video scenes from structured inputs such as depth maps, trajectories, LiDAR scans, and 3D bounding boxes. Together with NVIDIA Omniverse, it supports synthetic data generation for robotics and autonomous vehicle development. NVIDIA also introduces an open Physical AI Dataset on Hugging Face and the Isaac GR00T N1 foundation model for humanoid robot reasoning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Open Preference Dataset for Text-to-Image Generation by the 🤗 Community DevFeed: [Open Preference Dataset for Text-to-Image Generation by the 🤗 Community]() Original publisher: [Read original article]() Author: David Berenstein; ben burtenshaw; Daniel Vila; Daniel van Strien; Sayak Paul; Ame Vi; Linoy Tsaban Published: 2024-12-09T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Open Source Models & Datasets](), [datasets](), [Synthetic Data Generation](), [flux](), [stable-diffusion](), [argilla](), [distilabel](), [data observability](), [GitHub](), [hugging face]() Tags: [argilla](), [artificial-intelligence](), [data](), [data-is-better-together](), [data-quality](), [datasets](), [distilabel](), [flux](), [generation](), [github](), [hugging-face](), [image](), [open](), [open-source](), [stable-diffusion](), [synthetic-data](), [synthetic-data-generation](), [text-to-image]() ### AI overview The article describes an open community effort to create an image-preference dataset for text-to-image generation. It covers prompt preparation with distilabel, synthetic data generation, image generation with Flux and Stable Diffusion, and filtering with text- and image-based classifiers plus manual review. The resulting dataset and related code are available through the Hugging Face Hub and GitHub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Temporal use case roundup: Generative AI DevFeed: [Temporal use case roundup: Generative AI]() Original publisher: [Read original article]() Author: Clair Byrd Published: 2024-11-19T08:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Generative AI](), [AI Development](), [agentic workflows](), [Orchestration](), [Programming](), [Conversational AI](), [Synthetic Data Generation](), [data-processing]() Tags: [agentic](), [agentic-workflows](), [ai](), [applications](), [conversational-ai](), [datasets](), [generative-ai](), [processing](), [temporal-voices](), [video](), [workflows]() ### AI overview This use-case roundup describes how Temporal orchestrates reliable, long-running workflows for generative AI applications. It covers video processing and translation, conversational AI and call transcription, synthetic data generation, predictive analytics, and cryptocurrency data enrichment. ### Source excerpt See how Temporal boosts generative AI applications across industries, from video processing to conversational AI, by orchestrating complex, reliable workflows.