For organizations that train AI models, access to sufficient volumes of high quality data is quickly becoming a serious challenge. Privacy and regulatory compliance are among the biggest problems, with increasingly strict rules that hinder access to the necessary information to train difficult robust models.
Even when the data is available, the quality is not always guaranteed. Real world data sets can easily reflect existing inequalities or historical decisions that, without addressing, can lead to defective results that can manifest in customer -oriented applications. In addition, in highly specialized industries or where rare events are involved, the volume of usable data can be too small to obtain meaningful ideas.
So, there is the cost. The preparation of real world data for AI training is an intensive labor process, which often implies large -scale compilation, labeling and validation. This can take a long time and prone to setbacks, particularly when the equipment is under pressure to offer rapid results. Put all this together, and it is not surprising that some companies are fighting to take off their AI projects.
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Various cases of use
To close the gap, many are resorting to the generated or “synthetic” data “as an alternative to real world sources. This comes in several formats, ranging from structured tables and records to unstructured content, such as text, images and videos. It is even possible to create synthetic users or behaviors for more sophisticated training and test scenarios.
Designed to reflect the properties of real data without including any personal identification information (PII), it can provide a flexible solution that exceeds many of the challenges associated with live data sets.
For regulated industries, synthetic data is already demonstrating to be valuable. In medical care, for example, the recreation of realistic data sets without referring to patient data avoids many of the legal and ethical problems typically associated with these cases of use. In practical terms, this means that hospitals and research institutions can use AI platforms that replicate the characteristics of medical records without including personal details.
In other places, researchers can explore complex questions, such as predicting the progression of the disease or optimizing treatment plans through the use of synthetic data sets that behave as populations of real patients. This means that they can train AI models without putting privacy, and because synthetic data maintains the central properties of the original, the output is still valid for modeling and analysis, but with a zero risk of reidentification.
The correct data sets at the right time
In other environments, real -world data sets often reflect the limitations or inequalities present in the systems from which they were extracted, whether surrender to certain demographic data or biased results caused by historical decision making. Leaded without correcting, these problems can lead to AI models that are being trained, resulting in defective or unfair results.
Synthetic data offers a way to correct that imbalance. Because it is artificially generated, data sets can be adjusted to better reflect a more diverse or representative sample, such as different age groups, ethnic groups or behavior patterns. It also allows organizations to create realistic simulations of rare scenarios that would otherwise be too rare in real world data to train effectively.
The lack of data is also manifested in other situations, such as those found by autonomous driving systems. In some countries, weather events such as hail storms are rare, but when they occur, they can present a real danger to vehicles and their occupants.
Instead of waiting for such conditions to occur naturally, IA developers can create synthetic simulations of low visibility conditions and other unusual scenarios, which are then used to train vehicle systems to respond properly in living situations.
Similarly, images of people or objects that appear suddenly on the car route can be generated by computer and tested from all angles to ensure that all possibilities are addressed. Without this level of training, the model may not recognize a potentially dangerous situation and not respond properly.
Cost and efficiency
Compared to the time, effort and budget necessary to obtain and prepare real -scale data sets on a large scale, the use of synthetic data can offer a faster and more predictable alternative. In financial services, for example, the use of real client transactions data generally requires extensive anonymization and compliance controls. On the contrary, the synthetic data sets that mimic transaction patterns without referring to the real data of the client allow a development models of less risk.
In the real world, financial institutions have used synthetic data to improve the development of the fraud detection model without depending on confidential records of customer transactions. Access and use of real financial data generally requires expensive anonymity, compliance verifications and legal reviews, a set of processes that inevitably increase costs. By generating synthetic data sets that replicate real transaction patterns, companies reduce the need for a expensive data preparation and minimize regulatory obstacles, which makes their projects more profitable.
Looking towards the future, this type of work represents the tip of the iceberg, and we can expect many more organizations to resort to synthetic data to feed their AI projects. In fact, if Gartner's predictions are precise, by 2030, “synthetic data will completely eclipse the real data in AI models.”
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