Betterdata

Betterdata has been awarded a contract by the U.S. Department of Homeland Security (DHS) under their Synthetic Data Generator call! With our #Enterprise #SyntheticData platform, proprietary #LargeSyntheticModel (LSM) and data auditing with #DifferentialPrivacy, DHS will be equipped to generate private synthetic data that is: 🔹 Statistically accurate when real data is unavailable or limited in volume 🔹 Highly representative when real data is available and extensive We are humbled to contribute to mission-critical #NationalSecurity systems and partner with DHS to drive cutting-edge innovations in #DataPrivacy for enterprise #AI, #Analytics and #Augmentation. Press release: https://lnkd.in/e3Q77JJZ #SyntheticData #GenerativeAI #LLMs #DHS #SVIP #CISA #PRIV #Betterdata #LargeSyntheticModel #LSM

Synthetic data is the potential of real data unleashed. The commercialization of AI, for example, is based on abundant amounts of data being collected daily. However, one massive challenge in AI development is the accuracy of its models, which are often biased and inaccurate. Amazon's AI recruiting tool was discontinued for this specific reason—it showed bias against women. *https://lnkd.in/dBC96zat Other examples of bad AI due to bad data are, Carnegie Mellon University found Google’s ad system was gender biased, showing high-paying job ads to men more often than women. *https://lnkd.in/exV_Ddc6 ProPublica’s 2016 investigation showed racial bias in COMPAS, labeling Black defendants as having higher reoffending risks than white ones. *https://lnkd.in/bxSCFDr This happens because models learn from data and so do humans. Incomplete, biased, and partially hidden data leads to inaccurate model training. With humans, it comes down to guesswork where data teams, analysts, etc. are forced to make assumptions from data that only give you the full picture. As enterprises adopt data-driven strategies to drive growth and innovation, synthetic data offers better data for accurate and precise analysis, decision-making, model training, and so on, whatever the use case might be. At Betterdata, we have been pioneering SOTA DGMs with privacy engineering to produce high-quality synthetic data that is also private. Contact us to explore how we can support your data needs. https://lnkd.in/dBiDwz-P #syntheticdata #usecases

We're #hiring a new Senior Machine Learning Engineer in India. Apply today or share this post with your network.

Simply put synthetic data is important for two reasons: ✔ Businesses need real unfiltered data to evolve. ❌ Businesses cannot use real unfiltered data due to increasing data privacy laws. In a scenario where access to high-utility real data is limited, Betterdata'a programmatic synthetic data is a viable alternative to real data where you get access to high-utility and fast-moving synthetic data that looks, feels, and operates like real data. Since the privacy of tabular synthetic data is difficult to evaluate quantitatively, our synthetic data engines provide privacy guarantees through differential privacy, that ensure synthetic data cannot be traced back to real data or individuals. Read the complete article here: https://lnkd.in/eCduMwyt #syntheticdata #differentialprivacy

Betterdata equals better performing and fair models. Our programmatic synthetic data engine is designed to improve data quality by removing bias and imbalances, generating high-quality and privacy-preserving synthetic data. Read our blog on how synthetic data improves machine learning: https://lnkd.in/dxVh8D5D Contact us: https://lnkd.in/dBiDwz-P #syntheticdata #databias #AI #machinelearning

Betterdata is pioneering AI development in Singapore, tackling critical challenges in #DataPrivacy and #EnterpriseML through large-scale synthetic data solutions. With a solid foundation from the National AI Strategy (NAIS) 2.0, Singapore is poised for exponential growth in AI, attracting global talent and encouraging collaboration among startups, academia, and enterprises. In a recent feature on CNA, Betterdata’s Head of R&D, Zilong ZHAO, shared his journey from the EU to Singapore, driven by the nation’s tremendous potential for AI innovation. Meanwhile, CTO and Co-founder Kevin Yee highlighted the value of focusing AI development on specialized domains to create meaningful, high-impact progress. A special thank you to CNA, Amanda Yeap, and Loraine Lee for the insightful feature and for giving us the opportunity to share our vision! Read the full article here: https://lnkd.in/eWB-HYMS #syntheticdata #AI

Privacy teams often face a dilemma between protecting data privacy and maintaining data utility. 🔴 Traditional anonymization methods make data nearly unusable for machine learning, pushing data teams to use real data despite potential legal risks or anonymized data with low utility. 🔵 Betterdata's programmatic synthetic data is identical but non-identifiable alternative data generated through ML models trained on real sensitive data retaining its statistical properties. Making it 100% privacy-compliant which allows organizations safe, unrestricted access to useful data without compromising privacy. ➡ Replicates real-world data with accuracy scores up to 99% ➡ Preserves the structure and statistical properties of production data.  ➡ Maintains and improves data quality through bias mitigation and rebalancing, improving data utility and coverage. ➡ Is not regulated by data protection agencies since it contains no identifiable markers linking back to real individuals. Learn more: https://www.betterdata.ai/ #syntheticdata #dataanonymization #datautility #dataprivacy

We're at #SWITCH until 30 Oct, Wednesday! Visit our booth with SUTD near the L1 entrance at MBS to discover how our synthetic data platform drive privacy-compliant data sharing and enhance model performance.

AI/ML model performance depends on the data you feed it. Bad data results in bad models leading to data drifts, model collapse, and unexpected outcomes like McDonald’s AI drive-thru ordering experiment (now closed) which according to one customer kept adding chicken nuggets as far as 260, or Elon Musk's xAI Grok falsely accusing NBA star Klay Thompson of throwing bricks through windows of multiple houses. BTW if you are wondering both of these are blunders committed in 2024. At the rate at which AI is being developed and adopted, real-world data with all its limitations (specifically data privacy laws and generally data bias, gaps, and unbalanced data), data was and will play a critical role in its success. Synthetic data which closely mirrors real data is one popular solution to the data problem not only for developing AI but also for developing ML models for prediction modeling, data analytics, financial forecasting, and more. 1️⃣ Read our complete blog on generating tabular synthetic data: https://lnkd.in/eiPmkfyA 2️⃣ Learn more about synthetic data vs legacy data anonymization for ML https://lnkd.in/d8AiPiHZ ➡ or contact us for a quick go-through of synthetic data specific to your business requirements. https://lnkd.in/dBiDwz-P #syntheticdata #dataprivacy #machinelearning #ai #modeltraining #LLM #GANs

Major recent blunders by AI such as ChatGPT citing non-existent cases in legal research, or AI chatbots giving wrong information have made it clear that AI is only as good as the data it is trained on. But data has it's own problems. ❌ Strictly protected under data privacy laws ❌ Contains real-world bais ❌ Costly and time-consuming to use and share internally and externally ❌ Extremely hard to keep up with the data quality and quantity required to train ML models Bad and inadequate data results in equally bad and inadequate AI models which creates a series of unfortunate experiences for all those involved. This is why globally companies using AI are now shifting to synthetic data. (Below is an oversimplified version of why organizations are shifting to synthetic data) ✔ Privacy regulations compliant ✔ No risk of re-identification and exposure ✔ Easy to share and use internally and externally ✔ Easy to modify and update based on newer data trends to maintain ML model quality ✔ Remove bias and modify data based on specific use cases. For an in-depth review of specific synthetic data use cases, implementation, and functionality, contact us at: [email protected]