In the world of artificial intelligence (AI), one of the biggest challenges is the need for large amounts of data to train models, whether language or computer vision systems.
Traditionally, this data comes from real sources: articles, images, videos, transcripts, and other types of human-generated content.
However, getting real data is not always easy or ethical, especially when it comes to copyrighted content, sensitive data, or situations where data is not available in sufficient quantity.
Faced with this challenge, an emerging solution is the use of synthetic data. Let’s look at what they are, how they are generated and why synthetic data might not be the panacea that many initially expected.
What is synthetic data?
Synthetic data is generated by AI algorithms rather than being extracted from real-world sources. This data mimics patterns, structures, and characteristics of real data, but does not come directly from it.
Images, texts, sounds or even user profiles can be generated based on certain parameters that imitate behaviors and characteristics of authentic data.
For example, in the area of computer vision, a company developing systems for autonomous cars could generate synthetic images of roads and traffic conditions through computer simulations.
These images are used to train AI models that will then identify obstacles or interpret traffic signs without the need to gather thousands of real photographs of different scenarios.
How is synthetic data generated?
Generating synthetic data can be carried out in a variety of ways, but all depend on the use of advanced AI models that mimic the characteristics of real data. Some of the most common methods include:
Generation through generative networks
Generative adversarial networks (GANs) are popular tools for creating synthetic data.
These networks consist of two components: a generator that creates the synthetic data and a discriminator that evaluates whether the generated data is similar enough to the real data.
As they interact, the generator improves its ability to produce increasingly realistic data.
Simulations
Instead of using real data, researchers can create simulations of real-world situations.
For autonomous cars, for example, virtual traffic scenarios are generated that imitate human behavior on the streets, allowing systems to be trained without collecting real data.
Specialized AI models
Some AI systems, such as natural language processing (NLP) models, can generate text that mimics the style and structure of real text.
These models can be trained with a small sample of real data and then generate large amounts of synthetic content similar to the source data.
Transformed data
Sometimes synthetic data is not generated from scratch, but is created by modifying real data. This approach allows variants of existing data to be created without compromising privacy or intellectual property.
For example, images can be altered to represent different angles, colors or lighting conditions, helping to train AI systems without the need to collect new data.
Why is synthetic data needed?
There are several reasons why synthetic data is a valuable tool for the development of artificial intelligence. Let’s explore some of them
Shortage of real data
As more companies and governments impose restrictions on data access, traditional data sources are becoming depleted.
This is particularly relevant in areas such as language models, where web content is being restricted to prevent unauthorized use of copyrighted materials.
Costs and efficiency
Obtaining large volumes of real data can be expensive and logistically complicated. Additionally, collecting high-quality data involves navigating legal and ethical issues, such as user consent or privacy protection.
Synthetic data can generate large amounts of information much more quickly and cheaply.
Protection of intellectual property and privacy
One of the main advantages of synthetic data is that it helps avoid legal disputes over the use of copyrighted content.
By artificially generating data, companies can create AI models without the risk of infringing intellectual property rights.
Diversity and customization
Synthetic data allows developers to create highly specific data sets that reflect situations that are unusual or underrepresented in real data.
This can improve the performance of AI models in a variety of scenarios that would not otherwise be adequately represented.
The risks of synthetic data
Although there are several advantages to using synthetic data, there are also associated risks that should not be overlooked.
Data quality
Although synthetic data may be accurate, there is a risk that it is of lower quality than real data. An AI model trained only on artificially generated data may lack the complexities and nuances that characterize human data, which could lead to less reliable results.
Perpetuation of biases
If synthetic data is generated using biased data, these biases can be amplified in the AI model.
For example, if facial recognition systems are trained with images from a predominantly male and white dataset, the model could perform poorly with faces of other ethnicities or genders.
Risk of model collapse
When an AI model is trained solely on synthetic data, a phenomenon known as model collapse can occur.
This problem occurs when the model loses the ability to apply to real-world situations, resulting in inaccurate or incorrect results.
Lack of real context
Synthetic data cannot always capture the complex and changing context of reality. This is especially true in sectors like healthcare, where the variability and contextual details of data can be crucial.
Models trained only with synthetic data may be unable to handle situations outside the parameters established in their generation.
A solution with the potential to become a problem
The use of synthetic data represents a promising solution to data scarcity problems and legal challenges in training AI models. However, it is important to note that this practice is not without risks.
While synthetic data can accelerate the development of artificial intelligence and offer innovative solutions, it can also introduce quality, bias and context issues that, if not properly managed, could affect the reliability and ethics of the models created.
Synthetic data may not completely replace real data in training AI models at this time, but its complementary use could be a valuable tool if used with caution and responsibility.
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