#syntheticdata

[jovanovik]

New Preprint: RDFGraphGen: A Synthetic RDF Graph Generator based on SHACL Constraints

In the past year or so, our research team designed, developed and published RDFGraphGen, a general-purpose, domain-independent generator of synthetic RDF knowledge graphs, based on SHACL constraints. Today, we published a preprint detailing its design and implementation: "RDFGraphGen: A Synthetic RDF Graph Generator based on SHACL Constraints".

So, how does RDFGraphGen work, and why was it needed?

The Shapes Constraint Language (SHACL) is a W3C standard which specifies ways to validate data in RDF graphs, by defining constraining shapes. However, even though the main purpose of SHACL is validation of existing RDF data, in order to solve the problem with the lack of available RDF datasets in multiple RDF-based application development processes, we envisioned and implemented a reverse role for SHACL: we use SHACL shape definitions as a starting point to generate synthetic data for an RDF graph. The generation process involves extracting the constraints from the SHACL shapes, converting the specified constraints into rules, and then generating artificial data for a predefined number of RDF entities, based on these rules. The purpose of RDFGraphGen is the generation of small, medium or large RDF knowledge graphs for the purpose of benchmarking, testing, quality control, training and other similar purposes for applications from the RDF, Linked Data and Semantic Web domain.

RDFGraphGen is open-source and is available as a ready-to-use Python package.

Preprint: https://arxiv.org/abs/2407.17941 Authors: Marija Vecovska and Milos Jovanovik RDFGraphGen on GitHub: https://github.com/mveco/RDFGraphGen RDFGraphGen on PyPi: https://pypi.org/project/rdf-graph-gen/

[unstructured-data-management]

As AI advances, synthetic data emerges as a key player, ensuring privacy without compromising data quality. Essential practices like data observability and preparedness are critical for generating reliable synthetic data.

[luxlaff]

Do you trust artificial intelligence with the safety of your personal data? 🤖

Traditional anonymization methods are no longer cutting it. Your names, addresses, and even medical records could become fodder for AI's ravenous algorithms.

But there's a solution – synthetic data, homomorphic encryption, and blockchain. These technologies promise true privacy for machine learning. 🔐

Communities like @anon_tg and https://t.me/anon_club are already stirring, sensing this acute data privacy crisis. Want to learn more?

Tap the link in bio and join the vanguard of the fight for digital anonymity. The race is on! 🏁

[mikyit]

💥 Explore practical insights 👓 on utilizing #generative #AI 🧠🤖, including when and how to apply it, creation #methods, and key market #vendors! -> It’s estimated that by 2024, 60% of the #data used to develop #AI and analytics projects will be #syntheticallyz generated. 💡 Why? -_- Some types of data are costly to collect, or they are rare. -_- Many business problems that #AI & #ML models require access to #sensitive customer data such as Personally Identifiable Information (#PII) or Personal Health Information (#PHI). ----> As a result, #businesses are turning to data-centric approaches to #ArtificialInteligence #MachineLearning development, including #synthetic #data to solve these problems. -------> Click here for more: https://lnkd.in/g5252ajp Vendors: ✔️ BizDataX ✔️ CA Technologies Datamaker ✔️ CVEDIA ✔️ Deep Vision Data by Kinetic Vision ✔️ Delphix Test Data Management ✔️ Genrocket ✔️ Hazy ✔️ Informatica Test Data Management Tool ✔️ Mostly AI ✔️ Neuromation ✔️ Solix EDMS ✔️ Supervisely ✔️ TwentyBN

[govindhtech]

Data Alchemy: Trustworthy Synthetic Data Generation

Many firms use structured and unstructured synthetic data to tackle their largest data challenges thanks to breakthroughs in machine learning models and artificial intelligence including generative AI, generative adversarial networks, computer vision, and transformers. Qualitative data comprises text, images, and video, while quantitative synthetic data is tabular. Business leaders and data scientists across industries prioritize creative data synthesis to address data gaps, protect sensitive data, and speed to market. Finding and exploring synthetic data use cases like:

Edge cases and sample size increase with synthetic tabular data. When paired with real datasets, this data improves AI model training and prediction.

Synthetic test data speeds application and feature testing, optimization, and validation.

Synthetic data from agent-based simulations for “what-if” or new business events.

Protecting sensitive machine learning data with created data.

Sharing and selling a high-quality, privacy-protected synthetic copy to internal stakeholders or partners.

Synthesising data provides improved data value and guards against data privacy and anonymization strategies like masking. Business leaders lack trust. To build confidence and adoption, synthetic data creation tool manufacturers must answer two questions corporate leaders ask: Does synthetic data enhance my company’s data privacy risks? How well does synthetic data match mine?

Best practices help organizations answer these challenges and build trust in synthetic data to compete in today’s shifting marketplaces. Check it out.

Keeping bogus data private

Artificial data, which is computer-generated rather than real occurrences like customer transactions, internet logins, or patient diagnoses, can reveal PII when used as AI model training data. If a company prioritizes precision in synthetic data, the output may include too many personally identifiable traits, accidentally increasing privacy risk. Companies and vendors must work hard to minimize inadvertent linkages that could reveal a person’s identity and expose them to third-party assaults as data science modeling tools like deep learning and predictive and generative models evolve.

Companies interested in synthetic data reduce privacy risk:

Data should stay

Many companies are moving their software programs to the cloud for cost savings, performance, and scalability, but privacy and security require on-premises deployments. This includes fake data. Low-risk public cloud deployment of synthetic data without private or PII or model training data. When synthetic data requires sensitive data, organizations should implement on-premises. Your privacy team may prohibit sending and storing sensitive PII client data in third-party cloud providers, notwithstanding their outstanding security and privacy measures.

Be in charge and protected

Some synthetic data uses require privacy. Executives in security, compliance, and risk should govern their intended privacy risk during synthetic data generation. “Differential privacy” enables data scientists and risk teams choose their privacy level (1–10, with 1 being the most private). This method hides each person’s participation, making it impossible to tell if their information was used.

It automatically finds sensitive data and hides it with “noise”. The “cost” of differential privacy is reduced output accuracy, although introducing noise does not reduce usefulness or data quality compared to data masking. So, a differentially private synthetic dataset resembles your real dataset statistically. Data transparency, effective data security against privacy threats, and verifiable privacy guarantees regarding cumulative risk from subsequent data releases are also provided by differential privacy strategies.

Understand privacy metrics

If differentiated privacy isn’t achievable, business users should monitor privacy metrics to determine their privacy exposure. Although incomplete, these two metrics give a good foundation:

Leakage score: Percentage of false dataset rows that match original. A synthetic dataset may be accurate, but too much original data may compromise privacy. When original data contains target information but is inaccessible to the AI model for prediction or analysis, data leaks.

Distance between original and generated data generates closeness score. Short distances make synthetic tabular data rows easier to extract, increasing privacy risk.

Synthetic data quality assessment

Data scientists and business leaders must trust synthetic data output to use it enterprise-wide. In particular, they must immediately assess how well synthetic data matches their data model’s statistical properties. Lower-fidelity synthetic data is needed for realistic commercial demos, internal training assets, and AI model training situations than healthcare patient data. A healthcare company may use synthetic output to identify new patient insights that inform downstream decision-making, thus business leaders must ensure that the data matches their business realities.

Considering fidelity and other quality metrics:

Fidelity

A critical metric is “fidelity”. It assesses synthetic data based on its data model and real data similarity. Companies should know column distributions and univariate and multivariate column linkages. Understanding complex and huge data tables is crucial (most are). The latest neural networks and generative AI models capture these intricate relationships in database tables and time-series data. Bar graphs and correlation tables show lengthy but informative fidelity measurements. Open-source Python modules like SD metrics can help you learn fidelity analytics.

Utility

AI model training requires real datasets, which takes time. Machine learning model training is faster with synthetic data. Understanding synthetic data’s application in AI model training is essential before sharing it with the proper teams. The expected accuracy of a machine learning model trained on real data is compared to synthetic data.

Fairness

Enterprise datasets may be biased, making “fairness” important. A biased dataset will skew synthetic data. Understanding prejudice’s scope helps businesses address it. Identification of bias can help businesses make informed judgments, but it’s less common in synthetic data solutions and less significant than privacy, fidelity, and utility.

Watsonx.ai synthetic data usage

IBM watsonx.ai allows AI builders and data scientists input data from a database, upload a file, or construct a custom data schema to create synthetic tabular data. This statistics-based method creates edge situations and larger sample sets to improve AI training model forecasts. With this data, client demos and employee training may be more realistic.

Foundation models power Watsonx.ai, an enterprise-ready machine learning and generative AI studio. Watsonx.ai lets data scientists, application developers, and business analysts train, validate, adapt, and deploy classical and generative AI. Watsonx.ai aids hybrid cloud AI application development collaboration and scaling.

Read more on Govindhtech.com

[contentcollectivestudio]

Adobe has announced their new AI called Gingerbread, which is set to revolutionize the industry! With its ability to perform a variety of functions such as image recognition and language translation, it's incredibly intuitive and easy to use. Plus, with its ability to learn and adapt over time, it will only become more effective at performing its functions. But that's not all - in combination with Infinigen, a procedural generator of photo-realistic 3D scenes developed by researchers from Princeton University, these developments represent a major leap forward for the AI industry and suggest a future where AI more accurately interprets and interacts with the world around us. Stay tuned for more exciting updates in the world of AI!

[iemlabs]

Synthetic Data shows Authentic Execution in Machine Learning

Researchers train in machine learning models using vast databases of video clips of real-world behaviour. However, not only is it expensive and challenging to gather and tag tens of millions or billions of videos, but the clips frequently contain sensitive information, such as people's faces or car plate numbers.

Now share your Thoughts with us in the 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 section

Read the full blog: https://bit.ly/3NQP3yt

[only-ai-girls00]

#aimodels #aigirl #technology #cggirl #cgmodel #gorgeousgirl #dataset #nsfwartist #unstablediffusion #datascience #aisexy #aibeauty #aiartists #aiwomen #processdata #dataanalytics #innovation #algorithms #datamodeling #realdata #datamining #deepdata #deeplearning #syntheticdatageneration #computervision #stablediffusionwaifu #syntheticdata #thispersondoesnotexist #lingeriemodel #bikinigirl

[segmed]

Medical Imaging Publication  We are thrilled to announce the publication of our latest review article, "Generating Synthetic Data for Medical Imaging," in Radiology. This project was led by the Segmed team in collaboration with our friends from University of Washington Seattle, Harvard University, Stanford University, Microsoft, and University of California, San Francisco! In this comprehensive review, we explore the potential of hashtag#SyntheticData in enhancing hashtag#AI model development for hashtag#Medicalimaging. As healthcare data availability remains a challenge, synthetic data offers innovative solutions for data augmentation, privacy preservation, and diverse training scenarios.

[kajol1991]

Imagine this: you're on the cusp of a groundbreaking AI discovery, but ethical concerns around data privacy hold you back. That's where synthetic data swoops in, a hero promising anonymized training for your AI models. But here's the plot twist: the quality of your synthetic data hinges on the quality of the real data used to create it. Flawed data means a flawed AI, a villain in the story of responsible innovation.

So, how do you build a foundation of trust for your synthetic hero?

Enter Data Fabric, your secret weapon.

It breaks down data silos, those isolated pockets of information hindering your vision. With Data Fabric, you gain superpowers – data observability lets you see your data clearly, ensuring its accuracy, completeness, and relevance.

Think of it like this: Data Fabric equips you with a high-powered microscope to analyze your data's health. You can identify hidden biases, understand data distribution patterns, and ensure all the pieces are in place. This empowers you to create high-fidelity synthetic data, the purest form of anonymized training fuel for your AI.

But Data Fabric's story goes beyond synthetic data. It unlocks the full potential of your data, fostering better decision-making and ensuring compliance with data privacy regulations. It's the ultimate plot twist, transforming you from a hero struggling with limitations into a data-driven leader, ready to conquer the future of AI.

Are you ready to rewrite your AI story?

Embrace Data Fabric and unleash the power of trustworthy synthetic data. Read our blog post to discover how: https://cutt.ly/qesBvuXd

#AIethics #ResponsibleAI #SyntheticData #DataPrivacy #DataFabric #DataManagement

#DataGovernance #AIInnovation #MachineLearning #DataScience #FutureofAI

[technology7]

AI will be used to diagnose insurance coverage, location, cancellation risk factors to optimize healthcare scheduling workflows and availability. This is why the demand for AI is becoming high. Do you want to include AI in your next project? TechGropse Pvt. Ltd. is a leading name in providing the best AI solutions to our valuable clients within your budget.

#ai#healthcare#insurance#project#ai#blockchain#syntheticdata#technology#techblog#aiarchitecture#techgropse !

[sql-unicorn]

New blog post: How to use SyntheticData in Azure Synapse Database Templates

[simianamber]

Fictional art...exists only in the mind of the reader. All work © simianAmber

[alphatree]

synthetic frequency bands for remote-sensing applications

SFBs highlight hidden radiometric signatures in multispectral imagery through sophisticated transforms that make targeted objects clearly distinguishable with respect to their surroundings. SFBs are computed using a near-linear-complexity algorithm with minimal hardware requirements. The demo shows a pansharp RGB image of a residential neighborhood in Ankara, Turkey (first), the SFB targeting buildings with red roofs (second), and the CSL semantic triplet developed at the European Commission's Joint Research Centre (jrc) for the detection & segmentation of building footprints (third). For more contact the author.

[mikyit]

💡 __ What is #synthetic#data 🤖 and how can it help you competitively? 💡 --- Companies 🏙️ committed to #DataBased 💻 decision making share common concerns about #privacy 🔏, #DataIntegrity 🔩, and a lack of sufficient #data. --- #SyntheticData 🤖 aims to solve those problems by giving #software#developers and #researchers something that resembles #RealData but isn’t. It can be used to test machine learning #models or build and test software applications without compromising real, #PersonalData 🚶.