The article Synthetic Data: Why does AI not do without artificial reality first appeared at the online magazine Basic Thinking. You can start the day well every morning via our newsletter update.
Real data are rare, expensive and often associated with legal restrictions – but artificial intelligence needs ever larger amounts of data. Synthetic data can close this gap in the AI. Because they provide scenarios that are hardly comprehensible in reality, and at the same time protect confidential information.
Data is the basic building block for artificial intelligence. Because without them, AI models cannot learn or make reliable decisions.
With data, AI models receive examples from which their algorithms in turn derive patterns, relationships and rules. The more diverse, more extensive and high -quality the data are, the more precise and more robust the AI becomes.
AI systems can be developed with purely real data, for example. However, synthetic data are also becoming increasingly important in many AI areas. Because you can solve problems that are difficult or not to be handled with real data.
Synthetic data are becoming increasingly important for AI
AI systems are not based on synthetic data. However, they are now indispensable in many fields. Because you can fill gaps to make models more robust and also comply with data protection.
Especially data from the areas of health and finance as well as personal data must not simply be used for AI training. Synthetic data offer a large degree of data protection because they do not affect real people and can thus handle data protection problems.
Synthetic data can also help to solve problems that are difficult or not to be mastered with real data. This is the case, especially for rare events, for which there are simply not enough real data available. This is the case in medicine, for example, with rare diseases, but also in rare traffic situations, such as autonomous vehicles.
The use of synthetic data can also offer a cost advantage for AI systems. Because synthetic data can be generated quickly and quickly in large quantities. However, real data records can be very expensive, for example through clinical studies.
What risks does the use of synthetic data do?
However, the use of synthetic data for the training of AI systems is not only affected. Because this can also cause models to be trained in an artificial comfort zone and thus far from reality.
In this way, known problems such as distortions and bias can occur through synthetic data. If the underlying data or simulations are incorrect, synthetic data can be reflected prejudices or incorrect assumptions.
“Since the synthetic data is created from a small amount of real data, the same bias that is available in the real data can be transferred to the synthetic data,” explains Kalyan Veeramachaneni, senior scientist at the Laboratory for Information and Decision Systems on, opposite With news.
Just like with real data, you must ensure that the bias is eliminated by various sample processes in order to create balanced data records.
According to Veeramachaneni, careful planning is necessary to prevent the occurrence of bias. In order to prevent the spread of bias, a calibration of data generation could be used.
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The article Synthetic Data: Why does AI not do without artificial reality first appeared on basic thinking. Follow us too Google News and Flipboard Or subscribe to our update newsletter.
Synthetic data is becoming increasingly important in the field of AI as it allows for the generation of vast amounts of realistic data to train and test machine learning models. AI cannot do without artificial reality because it provides a crucial tool for creating diverse and complex datasets that accurately represent the real world. Without synthetic data, AI algorithms may struggle to generalize well and make accurate predictions in new and unseen scenarios.
Artificial reality plays a key role in generating synthetic data by simulating various scenarios, environments, and interactions. This allows AI models to be trained on a wider range of data points and improve their performance in real-world applications. Additionally, synthetic data helps address the challenge of data scarcity in certain industries or domains where collecting large amounts of labeled data is difficult or costly.
Overall, the combination of AI and artificial reality opens up new possibilities for advancing technology and solving complex problems. As the tech industry continues to evolve, the importance of synthetic data and artificial reality in AI applications will only continue to grow.
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