Generative AI’s biggest immediate impact for most businesses will be resolving the data scarcity crisis. Many industry-specific or domain-specific AI models need large amounts of data, but scarcity makes training and deployment difficult to almost impossible.
This is true of many industries, for example, healthcare, finance, and consumer-packed goods. And for my core audience, this is true for applications within the sales and marketing domain, particularly for B2B enterprises with limited numbers of target customers. Generative AI solutions empower data science teams to create synthetic data and produce successful algorithm models.
Generative AI can be used to create a variety of synthetic data including image and video synthesis, text generation, and natural language processing. This means it can serve data science teams addressing a wide variety of use cases.
Before diving into how to create synthetic data, let’s look at why a company should explore this approach.
The Benefits of Synthetic Data
There are several reasons to make and use synthetic data. Let’s look at the benefits. By using clean data to source synthetic data, companies can reduce a data science team’s traditional workload, which was dominated by cleaning raw information, by significant fractions, perhaps as much as 50% or greater. Given the shortage of data scientists this is a major reason to adapt synthetic AI development.
Let’s examine five additional benefits more in-depth:
1) Cost-effectiveness: In many cases, collecting and annotating large amounts of real-world data can be expensive and time-consuming. Synthetic data can be generated at a lower cost. For some domain-specific AI models, it may be the only way to obtain the amount of data needed.
2) Data augmentation: In some use cases, synthetic data augments existing datasets, which can train and improve the performance of machine learning models. By increasing the size and diversity of the dataset, the model can learn to generalize better and make more accurate predictions. Synthetic data generates more data.
3) Privacy protection: Individuals’ privacy in sensitive datasets, such as medical or financial records, may be protected by synthetic data usage. Generating synthetic data to preserve the statistical properties of the original data, while omitting any actual personal information, makes it possible to share data with third parties without compromising privacy.
4) Scenario generation: Synthetic data can simulate scenarios that are difficult or impossible to observe in the real world. For example, synthetic data can be used to test the safety of autonomous vehicles in a wide range of driving scenarios without the need for expensive and time-consuming real-world testing.
5) Enhanced representativeness and diversity: In many cases, real-world data is limited in quantity or may not cover all the variations and scenarios that could occur. This can lead to biases and inaccuracies in the AI models developed using that data. Synthetic data can help address these limitations by creating new diverse examples that capture a broader range of scenarios and variations. For example, synthetic data can help develop more sophisticated and accurate models for object detection (CV) as we would have access to a wider range of data objects which are realistic and diverse.
Overall, synthetic data has the potential to be a valuable tool for data scientists and machine learning practitioners, as it can help to address many of the challenges associated with collecting and using real-world data. However, it is important to carefully evaluate the quality and validity of synthetic data before using it in any application.
Using Existing Data to Create New Data
Synthetic data is created when generative AI samples existing data and produces similar data to supplement training and improve the accuracy and reliability of machine learning models. The process involves training a model, typically a deep learning model like a Generative Adversarial Network (GAN) or Variational Autoencoder (VAE), on a large dataset of examples that are similar to the type of data needed in a domain-specific AI scenario.
Once the model is trained, it generates new data samples with similar statistical properties to the original data, but the new sets are not exact copies of the original data. The specific process for creating synthetic data with generative AI models can vary depending on the type of data being generated, but generally involves the following steps:
1) Collect and preprocess the original data: This involves gathering a large dataset of examples that are representative of the type of data you want to generate. The data may need to be cleaned, normalized, or transformed to make it suitable for training a generative model.
2) Train the generative model: A generative model is trained on the original data to learn its underlying distribution. For example, a GAN is trained with two networks — a generator and a discriminator — that work together to create new samples. Hyperparameters are adjusted to ensure the sample data output is of high quality representing the characteristics of the original data.
3) Generate new samples: Once the generative model is trained, it generates new samples that are similar to the original data. The number of generated samples is controlled by the data science team.
4) Evaluate the generated data: Generated data quality should be evaluated to ensure that it is similar to the original data and suitable for the intended use. This may involve visual inspection, statistical analysis, or other methods of evaluation.
5) Use the generated data: Now it’s time to use generated data. This can be done for a variety of use cases, such as training machine learning models, testing software, or generating synthetic scenarios for research purposes.
Ensuring Ethical and Responsible Synthetic Data Use
While generative AI offers a promising solution for data scarcity, there is still much to be done to ensure its ethical and responsible use. For example, bias can be passed along in new synthetic data.
Ensuring ethical and responsible use of synthetic data is crucial for building trust in artificial intelligence models. Here are some key considerations for ensuring the ethical and responsible use of synthetic data:
1) Bias: Synthetic data can perpetuate biases or discriminate against certain groups. This means considering how the synthetic data is generated, and whether it reflects the diversity or the bias of the real-world data.
2) Privacy: Synthetic data should be created in a way that protects the privacy of individuals and their sensitive information. This means ensuring synthetic data is not linked to real-world individuals or entities, and that it cannot be used to re-identify individuals.
3) Transparency: It is important to be transparent about the use of synthetic data and how it was generated. This means providing clear documentation and explanations of the data generation process, as well as the intended use of the data.
4) Validation: Synthetic data should be validated to ensure that it accurately represents real-world data and is suitable for the intended use. This means testing synthetic data in various scenarios to ensure it does not lead to unintended consequences or harm.
5) Governance: Establish clear governance policies and procedures for the creation and use of synthetic data to ensure ethical and responsible practices are followed. This can include establishing a code of conduct for the creation and use of synthetic data, as well as regular auditing and monitoring of its use.
Overall, ensuring the ethical and responsible use of synthetic data requires careful consideration of the data generation process, as well as the intended use of the data.
Conclusion
By following best practices and established ethical frameworks, it is possible to resolve the data scarcity crisis while freeing up your data science teams to attend to more strategic work. Consider all the domain-specific applications that could have been imagined, but were not possible because there was not enough data resources. Much more is possible now thanks to generative AI.
This blog post was also published on Medium.
Disclaimer: ChatGPT was used to draft this article.
All Midjourney images created by Geoff Livingston.
