Until recently, Artificial Intelligence (AI) learned from reality, meaning data from users, images captured by cameras, human conversations, and natural patterns. But that is changing.

Today, AI no longer needs real data to train itself. Instead, it uses synthetic information generated artificially by other AI models.

This means that, in the future, many decisions made by artificial intelligence could be based on a world that does not actually exist, but rather on a digitally created version.

Is this a revolutionary advance or a loss of control over how AI understands the world?

What are synthetic data and why does AI need them?

Synthetic data is information generated artificially rather than collected from the real world. It is created using AI models to train other systems without needing to access personal or sensitive data.

Practical example:

If a company needs to train a facial recognition system, instead of collecting millions of photos of real people (which could create privacy issues), it can generate synthetic images of human faces to train its model.

Advantages of synthetic data:

• Protect privacy by not using real data.
• Allow AI to be trained in scenarios where there is insufficient data available.
• Help reduce biases present in real data sets.

Potential risks:

• If an AI only learns from synthetic data, it may lose touch with reality.
• It may amplify biases without developers noticing.
• In critical sectors like health or justice, making decisions based on unreal data could lead to serious errors.

Where are synthetic data being used?

The AI trained with synthetic data is already operating in various industries:

• Smart cities and mobility: Traffic simulations to optimize transportation without affecting the real population.
• AI models that predict traffic congestion before it occurs.
• Security and fraud detection: Banks create synthetic transactions to train AI against fraud without compromising real customer data.
• Cybersecurity companies generate fictitious attacks to strengthen digital defenses.
• Medicine and health: Synthetic medical images are generated to train AI without using information from real patients.
• Helps in early disease detection without exposing sensitive data.
• Digital content and entertainment: In movies and video games, AI creates hyper-realistic faces, voices, and movements without real actors.
• Synthetic dialogues are generated to train chatbots and virtual assistants.

The risks of a world based on synthetic data:

Although synthetic data offer advantages, they also present critical challenges:

• Disconnection from reality: If a model is trained only with information generated by other AIs, will it still understand the real world?
• Accuracy and reliability: If AI makes decisions in medicine, justice, or security based on data that never existed, can we trust its results?
• Manipulation and misinformation: The ability to generate synthetic information could facilitate the creation of fake news, deepfakes, and alter the perception of reality.

We are entering an era where AI no longer learns from us, but from artificial versions of the world.

Conclusion: Are We Losing Control?

The advancement of synthetic data is a powerful tool, but it also poses significant risks. While it allows AI to develop without compromising privacy, it also confronts us with a future where artificial intelligence could design its own version of the world, disconnected from human reality.

The key questions are:

• Is this the path to a more advanced AI or are we creating a digital bubble without a connection to reality?
• How do we ensure that AI reflects the real world?

Regulation, transparency, and human control will be essential to prevent AI from operating in a fictional world without supervision.

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