synthetic data

Extracted Attributes

• Types

  Multimedia (video, images), tabular, text.

• Definition

  Artificially generated data not produced by real-world events, designed to mimic real-world data in structure and statistical properties.

• Key Benefit

  Protects privacy and confidentiality of sensitive information, allowing use without consent or compromising personal data.

• Applications

  Computer simulations (e.g., music synthesizers, flight simulators), natural language processing (NLP), computer vision (image classification, object detection), software testing, data quality assurance, analytics, demoing, personalized product development, scientific research (clinical trials), finance, healthcare, insurance, telecommunications.

• Primary Purpose

  Validate mathematical models and train machine learning models.

• Generation Methods

  Algorithms, statistical methods, deep learning, generative AI.

• Additional Benefits

  Cost-effective, customizable, can augment data (e.g., upsampling minority groups), eliminates bias, adds fresh domain knowledge, enhances model performance, adheres to privacy regulations (HIPAA, GDPR, CCPA).

• Relationship to Real Data

  Can supplement or replace real datasets, retains underlying statistical properties of original data.

Timeline

• Increasingly adopted for training advanced AI models, such as Large Language Models (LLMs) and autonomous driving systems (e.g., Tesla FSD), driven by the principle of 'The Bitter Lesson' which emphasizes scalable computation over human-labeled data. (Source: Related Documents, Web Search Results)

  2024-01-01

Synthetic data

Synthetic data are artificially-generated data not produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. In many sensitive applications, datasets theoretically exist but cannot be released to the general public; synthetic data sidesteps the privacy issues that arise from using real consumer information without permission or compensation.

Web Search Results

• Synthetic data - Wikipedia

  Synthetic data are artificially-generated data not produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated. [...] and solve unexpected issues such as information processing limitations. Synthetic data are often generated to represent the authentic data and allows a baseline to be set. Another benefit of synthetic data is to protect the privacy and confidentiality of authentic data, while still allowing for use in testing systems. [...] ### Scientific research [edit] Researchers doing clinical trials or any other research may generate synthetic data to aid in creating a baseline for future studies and testing. Real data can contain information that researchers may not want released, so synthetic data is sometimes used to protect the privacy and confidentiality of a dataset. Using synthetic data reduces confidentiality and privacy issues since it holds no personal information and cannot be traced back to any individual.

• What Is Synthetic Data? - IBM

  Synthetic data is artificial data designed to mimic real-word data. It’s generated through statistical methods or by using artificial intelligence (AI) techniques like deep learning and generative AI. Despite being artificially generated, synthetic data retains the underlying statistical properties of the original data that it is based on. As such, synthetic datasets can supplement or even replace real datasets. [...] Types of synthetic data ----------------------- Synthetic data can come in multimedia, tabular or text form. Synthetic text data can be used for natural language processing (NLP), while synthetic tabular data can be used to create relational database tables. Synthetic multimedia, such as video, images or other unstructured data, can be applied for computer vision tasks like image classification, image recognition and object detection. [...] Synthetic data can act as a placeholder for test data and is primarily used to train machine learning models, serving as a potential solution to the ever-growing need for—yet short supply of—high-quality real-world training data for AI models. However, synthetic data is also gaining traction in sectors like finance and healthcare where data is in limited supply, time-consuming to obtain or difficult to access due to data privacy concerns and security requirements. In fact, research firm Gartner

• What is synthetic data? - MOSTLY AI

  Good quality synthetic data is an accurate representation of the original data. As a result, it can be used as a drop-in placement for sensitive production data in non-production environments. Typical use cases include: AI training, analytics, software testing, demoing, and building personalized products. [...] For example, synthetic data versions of customer databases, patient journeys, medical records or transaction data are used by companies to make data driven decisions while respecting the privacy of their customers. Synthetic data is an industry-agnostic solution, used across various fields from finance and healthcare to insurance and telecommunications. [...] Synthetic data is the perfect fuel that AI and machine learning development projects need. Synthetic data is created by generative AI algorithms, which can be instructed to create bigger, smaller, fairer or richer versions of the original data. Due to how the synthesization process takes place, the data can be augmented to fit certain characteristics. In a way, synthetic data is like modelling clay for data scientists and data managers. For example, upsampling minority groups in a dataset can

• Synthetic Data Generation: A Hands-On Guide in Python - DataCamp

  Synthetic data is artificially generated information that mimics real-world data in structure and statistical properties but doesn't correspond to actual entities. It's created algorithmically and is used as a stand-in for real data in various applications. [...] Discover how to begin responsibly leveraging generative AI. Learn how generative AI models are developed and how they will impact society moving forward. See DetailsStart Course See More Related Image 19 blog ### What is Synthetic Data? Synthetic data is artificially generated data that mimics the characteristics of real-world data without containing any actual information. Image 20: Abid Ali Awan's photo Abid Ali Awan 6 min [...] Image 11: Timeline mobile.png FAQs ---- ### How does synthetic data differ from real data? Synthetic data is artificially generated to mimic the statistical properties of real data but does not represent actual entities. While real data is collected from real-world sources, synthetic data is produced through algorithms, making it ideal for overcoming privacy and data scarcity challenges. ### Is synthetic data as reliable as real data for training AI models?

• What is Synthetic Data? Examples, Use Cases and Benefits

  Customizable data. An organization can customize synthetic data to its needs, tailoring the data to conditions that can't be obtained with authentic data. They can also generate data sets for software testing and data quality assurance (QA) purposes for DevOps teams. Cost-effective data. Synthetic data is an inexpensive alternative to real-world data. For example, real vehicle crash data can cost an automaker more to collect than simulated data. [...] AI and ML model training. Synthetic data is increasingly used to train AI models. It often outperforms real-world data and is essential for developing superior AI models. Synthetic training data enhances model performance, eliminating bias and adding fresh domain knowledge and explainability. Besides being completely privacy-compliant, it also enhances the original data thanks to the nature of the AI-powered synthetization process. For example, in artificial training data, uncommon patterns [...] Privacy regulations. Synthetic data helps data analysts adhere to data privacy laws, such as the Health Insurance Portability and Accountability Act, General Data Protection Regulation and California Consumer Privacy Act. It's also the best option when using sensitive data sets for testing or training. Synthetic data provides insight without jeopardizing privacy compliance.

• Instance Of

  Q190087