Gretel

Gretel Navigator's synthetic data generation outperformed OpenAI's GPT-4 by 25.6%, surpassed Llama3-70b by 48.1%, and exceeded human expert-curated data by 73.6%. 🤩 Here's how to use Navigator to create high-quality synthetic data for fine-tuning LLMs. https://lnkd.in/eg2tSFes

Some exciting new AI research on synthetic data for language model training. In this video, Alexander Watson, cofounder and CPO of Gretel, shares insights and demos a Streamlit app for generating synthetics. He covers: ▪️ Intro to agentic systems and evo algorithms 🤖 ▪️ Recent #SynthAI papers 📄 ▪️ Tips for fine-tuning LLMs with synthetic data 🔧 ▪️ A step-by-step guide to creating high-quality expert data ⚛️ Check out the demo and share what you're building in the comments. https://lnkd.in/eMm_nyJj

The pioneering work with ImageNet revolutionized AI training by leveraging internet images, sparking an era of data-intensive deep learning. However, the looming "data wall" and the need for high-quality, diverse data sources present significant challenges for future AI advancements. Enter differentially private synthetic data – a promising solution to this impasse. By generating high-fidelity synthetic datasets, we can ensure robust training without compromising privacy or relying on diminishing real-world data. This approach not only preserves user confidentiality but also offers limitless, high-quality data tailored for specific needs. This has been our focus Gretel since day one.

"Synthetic data is also likely to grow in popularity due to its ability to train AI models at a much faster pace by generating large, clean, relevant datasets." https://lnkd.in/gzpJQZ8W #SyntheticData #OpenDataQuality #AI

Join us for a livestream on the latest in applied science and privacy research, along with demos on creating AI-ready synthetic data. Date: July 30th at 9 AM PT / 12 PM ET Topics: ✨ Open synthetic datasets ✨ Fine-tuning secure LLMs ✨ Live Demos + Q&A Register: https://t.co/IZENg4QIP1

Gretel Synthetics for the Public Sector: Accelerate government AI innovation while safeguarding data privacy and security. Gretel's synthetic data solutions help: 🤝 Boost collaboration across agencies 💡 Unlock data-driven insights 🛡️ Ensure data protection and national security SA Health's CCIO on Gretel Synthetics: "Our collaboration with Gretel [...] symbolizes a fundamental shift in how we manage and use health data, improving care with a focus on inclusivity and privacy protection." — Rhys Parker, CCIO, Government of Southern Australia Health Download the brief: https://lnkd.in/ehJJHmj7 #SyntheticData #GovTech #DataPrivacy

As synthetic data becomes increasingly crucial in AI development, the United Nations University has outlined key guidelines to ensure its responsible and effective use. These recommendations address both technical and policy aspects, aiming to foster equitable AI growth worldwide while supporting the Sustainable Development Goals. Here are 13 essential recommendations for the governance and implementation of synthetic data in AI: T͟e͟c͟h͟n͟i͟c͟a͟l͟ r͟e͟c͟o͟m͟m͟e͟n͟d͟a͟t͟i͟o͟n͟s͟: 𝟭. 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗲 𝗯𝗶𝗮𝘀: - Use diverse data sources - Apply statistical analysis to minimize quantitative bias - Address qualitative bias using social science techniques - Regularly assess and update for emerging biases 𝟮. 𝗨𝘀𝗲 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 𝗳𝗼𝗿 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮: - Explore various algorithms and methods - Compare effectiveness of different approaches 𝟯. 𝗘𝗻𝘀𝘂𝗿𝗲 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆: - Document methods and parameters used - Provide detailed information about synthetic datasets 𝟰. 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗮𝗻𝗱 𝗱𝗶𝘀𝗰𝗹𝗼𝘀𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Define clear benchmarks for synthetic data effectiveness 𝗪𝗮𝘁𝗲𝗿𝗺𝗮𝗿𝗸 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮: 𝟱. Make synthetic data recognizable by both humans and machines 𝟲. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀: - Protect synthetic data from unauthorized access and manipulation 𝟳. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗼𝗻 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮: - Ensure performance and robustness - Regularly update synthetic datasets P͟o͟l͟i͟c͟y͟ ͟R͟e͟c͟o͟m͟m͟e͟n͟d͟a͟t͟i͟o͟n͟s͟: 𝟴. 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗴𝗹𝗼𝗯𝗮𝗹 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝗮𝗻𝗱 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀: - Ensure trust and interoperability - Prevent misuse of AI models trained on synthetic data 𝟵. 𝗘𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝗹𝗼𝗰𝗮𝗹𝗹𝘆: - Regulate and implement global standards at local levels 𝟭𝟬. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗴𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀 𝗳𝗼𝗿 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮: - Address transparency, safe use, diversity, and bias avoidance 𝟭𝟭. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀: - Clarify ownership and promote open collaboration 𝟭𝟮. 𝗣𝗿𝗼𝗺𝗼𝘁𝗲 𝗴𝗹𝗼𝗯𝗮𝗹 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀: - Foster interdisciplinary studies on safe and ethical use of synthetic data 𝟭𝟯. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝘁𝗼 𝗿𝗲𝗱𝘂𝗰𝗲 𝗚𝗹𝗼𝗯𝗮𝗹 𝗦𝗼𝘂𝘁𝗵-𝗡𝗼𝗿𝘁𝗵 𝗱𝗶𝘃𝗶𝗱𝗲: - Ensure synthetic data benefits all regions equitably These guidelines serve as a roadmap for practitioners, lawmakers, and policymakers alike, promoting the ethical and efficient use of synthetic data in our rapidly evolving AI landscape. https://lnkd.in/gTaHmMxg

We built Gretel Navigator, our compound AI system, to automate data prep and accelerate safe AI development. Watch how quickly it generates realistic, high-quality synthetic healthcare data for testing, training, and fine-tuning models. 👇 https://lnkd.in/enyrARzW #SyntheticData #PHI #Healthcare

In this walkthrough of the Gretel.ai platform we provide step-by-step instructions on how create high quality synthetic data for fine-tuning #LLMs, and demonstrated how you can do this to achieve the same results we do with our existing customers-- outperforming OpenAI's #GPT4 by 25.6%, surpassed #Llama3-70b by 48.1%, and exceeded human expert-curated data by 73.6%.

Our research shows differential privacy (DP) can fine-tune models on sensitive data like customer call logs and patient interactions with minimal data quality impact. 📊 Learn more: https://lnkd.in/e3XAXhP7 #SyntheticData #Privacy #AI