Best Synthetic Data Generation Processes

Aditya Chakurkar | January 15, 2022 | 13 views

Best_Synthetic_Data
Synthetic data is a replacement for the real thing when researchers and scientists can't get their hands on it because of confidentiality or privacy concerns. In simple words, synthetic data is a replacement for real data.

With the growing demand for artificial intelligence, there's no way we can produce enough real-world examples on our own. As a result, the need to create synthetic data has never been more pressing than it is now.


What is Synthetic Data?

Synthetic data is artificial data that is created through algorithms. It simulates the statistical properties of real people but without revealing any identifying information. Businesses across various industries need synthetic data for three major reasons:

  • Training machine learning algorithms
  • Product testing
  • Privacy

Artificial intelligence is becoming more and more advanced, with even the most complicated tasks being handled by these computer systems. For us humans who work in a less sophisticated field than AI development to make sense out of what they're doing sometimes, we need help from "synthetic" data which reflects real-world events mathematically or statistically but does not come directly from any person.

Synthetic data is a bit like diet soda. To be effective, it has to resemble the “real thing” in certain ways. Diet soda should look, taste, and fizz like regular soda. Similarly, a synthetic dataset must have the same mathematical and statistical properties as the real-world dataset it's standing in for. It looks like it, and has formatting like it.”

Kalyan Veeramachaneni, principal investigator of the Data to AI (DAI) Lab and a principal research scientist in MIT’s Laboratory for Information and Decision Systems


What are the Best Synthetic Data Generation Techniques?

Different methods of data synthesis can be used to generate synthetic data. Businesses should choose a method according to the requirements and level desired for specific purposes in generating this type of information, such as decision tree learning versus deep-learning techniques. Let’s learn more about some of the best synthetic data generation techniques.


Variational Autoencoder (VAE) and Generative Adversarial Network (GAN)

Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) are deep generative models. VAE is a method of encoding data so that it can be transmitted more efficiently. The encoder creates an output that represents the original input, and then there's training on how well those two match up to each other through optimization methods.

On the other hand, the GAN model is an artificial intelligence technique that takes data from real-world events and generates synthetic ones. This process happens in two parts: generators create this new information, using something like machine learning or statistical analysis, and discriminants compare these synthetically created datasets with their original versions based on certain conditions set before training begins.


Monte Carlo Method

It has been said that if you have the data, then gambling on its distribution can be an effective means of generating synthetic samples. This is because businesses can determine what their best-fit model should look like by considering both observed information as well as potential outcomes based on different models or theories about how this particular set might behave; once they know these parameters it becomes possible for them to use Monte Carlo synthetic data generation method.


According to Dataset Distribution

For cases where real data does not exist but an analyst has a comprehensive understanding of how dataset distribution would look like, they can generate a random sample from any given distribution such as Exponential, Lognormal, Uniform, Chi-Square, Normal or t-distributions.


Scikit-Learn

Scikit-Learn is a method of synthetic data generation using Python, one of the most popular languages in the field of data science. Scikit-Learn is one of Python's three libraries that can be used to generate synthetic data. This powerful Python tool can be used for machine learning tasks like regression, classification and clustering. It also allows you to generate synthetic data if there's no real-world example of your researched topic.


Best Tools for Synthetic Data Generation

Generating synthetic data includes two steps: Data preparation and data synthesis. Data preparation is necessary before data synthesis. While several vendors can perform these two steps, we have compiled the three, best synthetic data generation tools for all:


Hazy

Hazy offers unique models capable of generating high-quality synthetic data with a differential privacy mechanism. Data can be tabular, sequential (containing time-dependent events), or dispersed across several tables in relational databases.


Edgecase.AI

Edgecase.AI takes the need for large-scale data annotation and generation of training images/videos to a whole new level with its proprietary platform. It helps solve fundamental problems in multiple industries like security, industry 4.0, healthcare, agriculture and retail.


MOSTLY AI

MOSTLY AI offers the leading synthetic data platform, which enables enterprises to unlock, share and fix their data. Thanks to its advanced artificial intelligence system, MOSTLY AI promises unmatched synthetic data accuracy while maintaining granular-level information.


Frequently Asked Questions


Which business functions can consider using synthetic data for improved overall efficiency?

The benefits of synthetic data are many. Data is an incredibly important component in all areas and can help us continue innovating new products, services or solutions when the necessary information isn't readily available. The following business functions can make the most of synthetic data for the best outcomes:

  • Human resources
  • DevOps
  • Agile Development
  • Machine Learning
  • Marketing


What are the key benefits of synthetic data?

Synthetic data has many benefits over real data. For example,

  • It focuses on multivariate relationships
  • It is immune to several, common, statistical problems
  • It overcomes restrictions that may arise while using real data


What are the main limitations of synthetic data?

Even though synthetic data has many advantages, there are some challenges around it as well. For example,

  • It requires output control
  • It is time-consuming
  • It is yet to gain significant user acceptance
  • Data quality depends heavily on data source

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SoftWorks AI Rebrands as TRUE and Answers Need for Lending Intelligence

TRUE | June 23, 2022

SoftWorks AI, the leader in lending intelligence, today launched an extensive rebrand, including changing its name to TRUE. The new name reflects the company’s unique ability to give lenders a true picture of a borrower by using AI to inspect and audit every data point when verifying a loan and its underlying documentation. By providing deeper, more comprehensive, and more accurate insights on borrowers, TRUE allows lenders to gain market share by enabling faster turn times and reducing risk. TRUE’s new name and logo demonstrate how the company harnesses the power of AI to turn lending – especially in the mortgage space – into a near real-time experience. While other vendors need humans in the loop to get data right, TRUE’s platform accurately analyzes every data point in a loan package automatically. TRUE’s technology can reduce manual reviews by up to 85% and has increased underwriter productivity by up to 300%. The lending industry has long been focused on automation, with an end-state goal of touchless automation. This can only be achieved with lending intelligence, which uses AI to ensure all data going into the automation process is 100% correct and verify that every extrapolated insight is accurate and provably true. The TRUE brand is focused on using cutting-edge technology to find what is true, a process the company calls Lending Intelligence. “TRUE uses AI to help lenders address customers’ most important expectations – speed, accuracy and reduced risk. “We are an AI lab focused executively on the lending sector and our AI platform is fully trained to address the challenges and nuances of lending. TRUE optimizes every step of the lending cycle, efficiently delivering critical information so our clients can get a full picture of their risk exposure. By providing a fast, thorough, and explainable analysis of every last data point, we reduce errors and improve decisioning, powering trustworthy lending.” Ari Gross, CEO of TRUE TRUE is transforming both the primary and secondary lending markets with technology that reads documents chronologically and holistically, going far beyond generic data classification that misses important details and nuances – and results in costly bad decisions. By ensuring that loan documents tell a consistent and coherent story and that all documents are cross-validated, TRUE enables lenders to get a holistic view of lending risk while delivering greater certainty to customers. The company currently serves major institutions from across the lending industry including Rocket Mortgage, Carrington, and 50% of the top mortgage insurers. TRUE processes millions of loans and billions of documents through its AI powered lending technology annually. About TRUE TRUE is a software solutions company and AI lab that helps lenders harness the power of artificial intelligence to make accurately informed lending decisions – increasing revenue while reducing operating costs and risk. Since 2017, it has set the industry standard for intelligent lending technology, delivering results that provide a true competitive advantage.

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GENERAL AI

AI Quality Leader TruEra Receives Investment from Hewlett Packard Enterprise

TruEra | June 23, 2022

Hewlett Packard Enterprise announced today that it has invested in TruEra through its venture capital program, Hewlett Packard Pathfinder. TruEra offers the first suite of AI Quality management solutions for managing model performance, explainability, and societal effect. The $25M Series B round TruEra announced in March 2022 is now extended. Hewlett Packard Pathfinder invests in market-leading start-ups, develops solutions fusing the technology of portfolio companies with Hewlett Packard Enterprise goods, and designs collaborative go-to-market strategies. Hewlett Packard Pathfinder also keeps a careful eye on longer-term disruptive innovation, supporting the development of cutting-edge technology. "We're excited to become an investor and to partner with TruEra in developing comprehensive solutions for our enterprise customers in conjunction with our High-Performance Computing offering," said Paul Glaser, Vice President and Head of Hewlett Packard Pathfinder. The quality challenge, the following significant AI challenge, is addressed by TruEra. In order to quickly correct problems and maintain peak performance, ML teams can use TruEra's solutions to explain, analyze, and test the performance, risk, and responsible AI characteristics of models early in the development process. As a result, numerous Fortune 1000 businesses have chosen TruEra as their preferred provider because of its distinctive, whole lifecycle approach to model quality. "AI model quality and ML Ops have emerged as considerable challenges for enterprises deploying and scaling machine learning models. Solving these challenges is imperative for AI success, and TruEra stands out for its deep expertise, differentiated technology and practical experience helping companies deliver and monitor AI applications." Ali Wasti, Managing Director, Hewlett Packard Pathfinder Top-tier investors such as Menlo Ventures, Greylock Partners, and Wing Venture Capital have contributed approximately $45 MM to TruEra. Anupam Datta and Shayak Sen, co-founders of TruEra, conducted academic research at Carnegie Mellon University that served as the basis for the company's technology. "Hewlett Packard Enterprise is a leading, trusted provider to the enterprise, and is known for its ability to ensure that cutting-edge innovation delivers proven results. We're looking forward to working closely with the HPE team as partners on customer engagements," said Will Uppington, CEO and co-founder, TruEra.

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