When I hear the word ‘generative AI tools’ I am taken back to Builtwith.
This website allowed me as a marketer to spy on competitor website’s, more than a decade ago. That was my first fling with an AI tool. Although I write quite a lot of content during a week, I stay away from using generative AI to write my content. I have 2 reasons for that. One is of course the SEO aspect and the other, is my love for writing stories instead of articles…as illustrated in me starting this post with a Builtwith throwback. But let’s face it,
The increase of data that you examine for insights has led directly to the AI revolution we are currently experiencing. But gathering information from the actual world can be difficult. While handling and storing personal data poses privacy and security risks. Handling other kinds of data can be costly or even hazardous. So here’s my key question for this article.
Why can’t I create synthetic data that is sufficiently similar to real-world data to be utilized for many of the same objectives at a far lower risk, expense, and time investment?
Boom! That's what I believe synthetic data offers, and it's another area where generative AI is rapidly emerging as a useful tool. To support this argument of mine, I have created this generative AI tools list for synthetic data creation.
This is my (humble) compilation of some of the best free and premium generative AI tools for creating synthetic data that are also intriguing, helpful, or one-of-a-kind.
Wait wait!
I need some more SEO garnish.
So, let’s delay the list a bit to educate folks on what synthetic data I’m rambling about.
What is Synthetic Data?
Quite simply, it’s the information that has been artificially created as opposed to coming from actual occurrences.
Synthetic data is generated algorithmically. It's used to train machine learning (ML) models and validate mathematical models. They also serve as a stand-in for test data sets of operational or production data.
I come from the old school of digital marketing where data scrapping and data cleaning were part of my working hours each day. It was challenging, costly (to my employers), and time-consuming (to me) when collecting high-quality data from the actual world. Synthetic data technology makes it possible for me to quickly, easily, and digitally generate the data in any quantity they need, tailored to their unique requirements. Magic! Because synthetic data has various advantages over real-world data, its use is becoming more and more common. It’s so beneficial that according to Gartner's prediction, 60% of the data used to build AI and analytics would be artificially generated by 2024. Who uses the most synthetic data you ask?
The training of neural networks and machine learning models. They require well labeled data sets that may contain anything from a few thousand to tens of millions of items. This represents the largest use of synthetic data.
Companies can generate a big amount of training data. This data is diverse and non-prejudicial without having to invest a lot of time or money in artificially manufactured synthetic data that mimics real data sets.
Paul Walborsky, co-founder of AI.Reverie, one of the first companies offering artificial intelligence services, claims that for six cents, an image that would normally cost $6 from a labeling service can be created artificially.
How is synthetic data generated?
Three popular methods for producing synthetic data are as follows:
Distribution numbers
Synthetic data is sometimes created by randomly choosing numbers from a distribution. This approach can yield a data distribution that roughly mimics real-world data. Sometimes it doesn't capture the insights of real-world data.
Agent-based modeling
This simulation technique entails the creation of distinct agents capable of intercommunication. These techniques are particularly useful for analyzing the interactions between several agents in a complex system, such as humans, computers, or mobile phones.
Python programs like Mesa make it easy to quickly construct agent-based models and observe them through a browser-based interface by using pre-built core components.
Generative Model
The statistical characteristics or aspects of real-world data can be replicated in synthetic data created by these methods. This is what this article stresses upon.
In order to create fresh synthetic data that is comparable to the original data, generative models first employ a set of training data to identify statistical patterns and relationships in the data. Variational autoencoders and generative adversarial networks are two types of generative models.
What are the advantages of synthetic data?
Synthetic data offers the following advantages:
Customizable data
Synthetic data can be tailored to specific conditions that cannot be attained with actual data, allowing your business to modify it to meet its needs. Additionally, they can produce data sets for DevOps teams' use in software testing and quality assurance (QA).
Cost-effective
A less expensive option to genuine data is synthetic data. For example, gathering actual car crash data may be more expensive for an automaker than doing so with simulated data.
Data labeling
Synthetic data is not always labeled, even when it is available. Manually identifying a large number of examples for supervised learning activities can be laborious and prone to mistakes.
The process of developing a model can be accelerated by producing synthetically labeled data. It also ensures accuracy of labeling.
Faster production
With the correct tools and technology, it is feasible to build a data set more quickly because synthetic data isn't collected from real events. This enables the creation of artificial data in large quantities more quickly.
Complete annotation
Manual data gathering is not necessary with perfect annotation. An assortment of annotations can be automatically generated by each object in a scene.
This is also the primary cause of synthetic data's low cost in comparison to actual data.
Data privacy
Even though synthetic data may have certain characteristics of actual data, it shouldn't have any information that would allow the real data to be recognized.
This feature, which can be quite advantageous for the pharmaceutical and healthcare sectors, renders the synthesized data anonymous and suitable for distribution.
Full user control
You can have total control over every detail with a synthetic data simulation. Event frequency, item distribution, and many other variables are at the control of the individual managing the data set.
When employing synthetic data, machine learning practitioners also have complete control over the data set.
Controlling the degree of class separations, sampling size, and amount of noise in the data set are a few examples. However, there are certain disadvantages to synthetic data as well.
These include inconsistencies when attempting to duplicate the complexity of the original data set and the incapacity to completely replace authentic data because accurate, authentic data is still needed to generate useful synthetic examples of the information.
What are the use cases for synthetic data?
The synthetic data need to fairly represent the original data that it aims to enhance. The following are some use cases for synthetic data:
Testing
Synthetic test data is more flexible, scalable, realistic, and easier to generate than rules-based test data. Synthetic data is essential for data-driven testing and software development.
AI/ML model training
Since synthetic data frequently outperforms real-world data and is necessary for creating better AI models, it is being utilized to train AI models more and more. Synthetic training data improves the performance of the model while removing bias and adding new domain knowledge and explainability.
Because of the nature of the AI-powered synthetization process, it is not only fully compatible with privacy laws but actually improves the original data.
For instance, unusual patterns and occurrences can be sampled in simulated training data.
Privacy regulations
Data scientists can comply with data privacy requirements including the California Consumer Privacy Act, General Data Protection Regulation, and Health Insurance Portability and Accountability Act, thanks to synthetic data.
Additionally, it's the greatest choice for training or testing with sensitive data sets. Organizations can obtain insights from synthetic data without compromising privacy compliance.
Health and privacy
Since privacy laws impose stringent limitations on these domains, health and privacy data are especially suitable for a synthetic approach.
Researchers can get the information they need without violating people's privacy by employing fake data.
It is quite improbable that synthetic data will lead to the reidentification of a genuine patient or their personal data record because it does not represent the data of real patients. Additionally, synthetic data offers many advantages over data masking methods, which carry more privacy-related issues.
Generative AI tools for synthetic data creation
Finally! We reach the part which you have patiently scrolled for. Thanks for making till here. Synthetic data is created with artificial intelligence algorithms to replicate real data's features while maintaining confidentiality and anonymity. Let’s look at the best generative AI tools used for synthetic data creation.
It's a reliable synthetic data platform for producing data that closely mimics reality, for the most part. It is used by a number of industries, including banking, retail, telecommunication, and healthcare. It makes a difference by making it easier to generate datasets that guarantee privacy and compliance with data protection rules like the CCPA and GDPR, which is why Gartner named it a Cool Vendor. Its natural language UI allows you to query the data it creates in a way that's akin to having a conversation with a ChatGPT bot. It also includes protections to ensure that bias is not introduced into the synthetic data it generates.
Gretel makes it easy for practically anyone to create tabular, unstructured, and time-series data for use in any type of analytics or machine-learning workflow.
Even people with little experience with coding can create artificial data because to its user-friendly architecture.
Thanks to a large number of connectors and API connections, it is compatible with most cloud and data warehousing infrastructures, and there is a thriving user community providing help and support.
Synthea is a free and open-source application designed specifically to build virtual patients for use in healthcare analytics.
It can create complete medical records for people whose records might not exist but contain information that can aid with complex medical problems.
Because of this, medical researchers don't have to worry about patient confidentiality or the ethical implications of using real patient data.
Tonic is a comprehensive platform that is primarily intended for software and AI development. It enables the production of safe, compliant, and realistic synthetic data.
In addition to producing synthetic data, it offers de-identification for the anonymization of real-world data.
It can be accessible in a cloud environment or installed on-premises, and it is designed to interact with all commonly used databases.
Faker needs some expertise with Python and JavaScript, among other languages, as it is a library rather than a standalone application.
However, it's a helpful tool for those who want to fake information about everything from financial activities to online shopping habits.
Then, this data may be used to train algorithms for anything from fraud detection to recommendation engines, without running the risk of breaching privacy that comes with using real data.
Generative AI is used by Broadcom CTA Test Manager, a potent software testing tool, to produce synthetic data that closely mimics real-world data. To create synthetic data that preserves the statistical characteristics and distribution of real-world data, CTA Test Manager uses sophisticated algorithms.
A state-of-the-art generative artificial intelligence technology called BizData X is meant to produce artificial data that looks a lot like real data.
In order to create synthetic data that preserves the statistical characteristics and distribution of real-world data, BizData X uses cutting-edge deep learning techniques.
This guarantees that the artificial data is representative and capable of simulating real-world events accurately.
In situations when data is scarce or nonexistent, CVEDIA develops commercial-grade algorithms for computer vision applications.
Our models are strong and have been benchmarked; additionally, each model is accompanied by a data scientist-designed performance report and an ongoing maintenance contract.
Working on the most intricate deep learning projects for more than 30 of the world's biggest corporations, CVEDIA significantly cuts down on training needs, data bias, and project durations.
Datawizz produces synthetic data, which is effectively made-up data that looks like actual information. On their website, the company's crew is introduced along with instructions on how to utilize their program.
Notably, Datawizz was formed by people with experience at Apple and other significant tech businesses, and its software is available for free and open-source.
Edgecase offers artificial intelligence (AI) and data labeling solutions, such as on-demand expert data labeling, synthetic data generation, and data labeling as a service.
Their ability to quickly and accurately generate vast amounts of high-quality training data is their unique selling proposition (USP).
They are able to do this because of a proprietary technology that produces millions of photos in a matter of days. Because their data is generated from a combination of real-life blended imagery and 3D models, it is also quite accurate.
GenRocket is dedicated to the generation and management of test data. They recognize the difficulties businesses encounter while managing test data and present GenRocket, a platform that generates test data automatically, as a solution.
For businesses, this automation promises shorter cycle times, more test case coverage, and better data quality.
Hazy is a platform for artificial intelligence that reworks pre-existing data to make it safer, quicker, and easier to utilize for a variety of applications.
Being the first firm to successfully introduce synthetic data as an enterprise product to the market is their unique selling proposition (USP).
This suggests that organizations can easily embrace and use Hazy's technology to solve their data difficulties.
To assist customers in creating data-driven products, K2view provides a platform for data products.
Their capacity to create these data products quickly and provide packaged datasets at scale is their major differentiator (USP).
This guarantees data privacy for authorized users while enabling people to democratize data access.
On its website, MDClone describes a business that uses data insights to enhance patient outcomes. They provide a platform that makes it easier to get quick answers to research queries, which could result in quicker and more efficient therapies.
The website demonstrates how MDClone's data-driven approach to patient care has been successful in lowering hospitalization and death rates for partner organizations.
Using 360° video and LiDAR data, Simerse is an artificial intelligence platform that maps and updates infrastructure records.
Their unique selling proposition is their ability to automate this process, which enables businesses in sectors like transportation and construction to develop, use, and maintain their infrastructure assets more effectively.
Technology and engineering services provider Sogeti assists companies in realizing the benefits of technology.
Artificial intelligence, automation, cloud solutions, DevOps for organizations, DevSecOps and cybersecurity, quality engineering and testing, creating digital experiences, and promoting innovation are just a few of the many services they offer.
Sogeti's commitment to assisting customers in deriving value from technology is their unique selling proposition (USP).
Users can create synthetic data using the Syntho platform. On their website, you may find out what synthetic data is and how to make it using their platform.
It serves a range of functions for which synthetic data could be beneficial. Contact details and a pricing page are also included on the website.
Data quality for data science applications is the main emphasis of YData. According to their website, they are the ones that developed YData Fabric, the "first data-centric platform for data quality."
It appears that this platform provides a complete data asset management solution. YData.ai further emphasizes its dedication to open-source software by providing services like ydata-quality, ydata-synthetic, and ydata-profiling.
Open-source tools for data scientists and user-friendly data administration with a data-centric approach seem to be their unique selling proposition (USP).
Generative AI tools for synthetic data creation is your first step
There you have it folks! Our round up of the best generative AI tools for synthetic data creation. Because generative AI methods can produce vast amounts of high-quality data fast and efficiently, they are useful for creating synthetic data.
This is especially helpful for fields like autonomous driving and medical imaging when gathering real-world data is costly or difficult.
Furthermore, while maintaining the privacy of sensitive data, generative AI technologies can produce synthetic data that is representational of real-world data.
They are therefore perfect for a wide range of uses, such as data analysis, machine learning, and data testing. Follow thegen.ai for everything Generative AI.
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