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The pitfalls of training AI with invented data

5 min read

AI is evolving and finding its way into our everyday lives and workplaces. The prospect of always having a highly intelligent system in your pocket is gaining ground.

Whether it’s writing an essay, creating complex artwork, reviewing policies, developing custom code, or crafting a speech, the technology is already here, the way we work and live, to change.

However, artificial intelligence (AI) relies solely on data to perform its tasks.

Let’s take an example of the request: “Create me a picture of a rose”. The AI ​​must first learn about the various data offered before it can get to work.

It has to capture all the information like the typical rose shape, the colors, the design and the arrangement of the petals – all the characteristics that make a rose a rose.

What is the source of the data that the system learns from? It is AI-generated or synthetic data.

Train artificial intelligence

While today’s focus is on training an AI system with AI-generated data, in general an AI system is trained with a mix of AI-generated and real-world data.

The process was developed taking into account legal, ethical and confidentiality considerations when acquiring real data.

However, data is crucial if one wants to develop realistic AI systems (e.g. synthetic news readers) and given the lack of real data, the generation of synthetic data that mimics real data is essential.

For example, an AI system can create a detailed image of an airplane’s cockpit, but it won’t quite match reality.

1st step: Generation of synthetic data

The original AI system produces synthetic data that is used to train the actual AI model.

This can be a neural network or another machine learning algorithm.

The synthetic data is as close to the real world as possible and allows the target system to learn about the object to which the data relates. It knows things like shapes, colors, and configuration details.

2nd step: Preparation of the training data

The synthetically generated data is mixed with the corresponding real data.

For example, the AI-generated image of a cockpit dashboard is combined with the original of such a board.

This is an opportunity for the AI ​​learning model to learn from the data. Not only can it identify the components of the data, e.g. B. the fuel gauge and the altimeter, but also distinguish between synthetic and real data.

Step 3: Training the AI ​​model

The desired AI model learns from the mixed data set.

For example, the goal is to enable the AI ​​model to recognize different types of dog images. The acceptable answer is that it can identify the dogs’ names and categorize them as sheepdogs, hounds, etc.

The AI ​​model provides a limited collection of real dog images and a larger range of synthetic data.

The learning model examines and understands the various characteristics and parameters and can form conclusions and patterns from them.

For example, dogs with short tails can be identified as Dobermans, or those with pronounced and pointed ears as German Shepherds.

In addition, the model learns not to generalize based on the parameters. For example, Doberman dogs have short tails, but not all short-tailed dogs are Dobermans.

Using data in the real world

One of the most interesting practical examples of AI being trained using AI-generated data is PilotNetthe self-driving car project by NVIDIA.

PilotNet is a deep learning system. It learns in real time from synthetic data and the observation of human drivers. They drive a special car that collects data on driving behavior, road conditions, traffic signs, lane markings, vehicles and pedestrians.

Driving is a complex task. It requires both skill and decision-making in an extremely short period of time. While the human driver drives the car, collects PilotNet Data. The relevant information is marked as highlighted pixels.

The deep learning system behind the self-driving car must control driving based on the highlighted pixels that identify various objects on the road, such as pedestrians, traffic lights, and vehicles.

Advantages of synthetic data

The Main Benefits of training AI with synthetic data are:

  • As mentioned, real data is difficult to obtain due to various limitations, which is why synthetic data is the best choice. High-quality synthetic data that is as close as possible to real data is the best learning source for AI learning models.
  • With synthetic data there is no risk of breaching confidentiality or secrecy as there is with real data. Real data, when collected legally and with consent, comes with strings attached.
  • Synthetic data enable the exploration of different scenarios. In a self-driving car, for example, synthetic data can help explore driving on a congested road or a highway—without actually having to step onto the road.

limitations and problems

Synthetic data is both an advantage and a limitation because it is not real data, regardless of its quality.

An AI model takes longer to learn about real-world objects using synthetic data.

Synthetic data can contain errors and biases that can lead to unintended training results because they don’t match real-world use cases.

For example, synthetic creditworthiness and loan application data may include incorrect and biased data about certain communities or may be inaccurate because they do not comply with recent changes in data laws.

The result could be not only unwanted, but also dangerous.

However, despite its limitations, synthetic data is still the best available data source for AI models to learn from.

However, companies could be extremely cautious when using AI in sensitive use cases such as medical treatment, social issues and loan applications.


Obtaining data from the real world seems to be a major obstacle to learning AI models. Obtaining data comes up against many hurdles in different forms.

Recognizing that AI can achieve remarkable feats, key institutions such as governments, corporations, and research institutes need to figure out how AI systems can analyze real-time data and filter out parts that processing could cause problems in the real world.

In the meantime, however, synthetic data – used judiciously – is better than nothing.

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All content in this article is for informational purposes only and in no way serves as investment advice. Investing in cryptocurrencies, commodities and stocks is very risky and can lead to capital losses.