Grazed: Synthetic Data Trains AI

AI models require more and more data to be trained in a value-enhancing way today. And what if there’s no more data in the world out there? What if we’ve grazed all the knowledge stocks? Or if we need data whose characteristics pose problems for individual data protection? This is where “synthetic data” comes into play. It’s a topic that could become increasingly relevant and therefore should be given a stage in this newsletter today. Does everything go faster with this? Can we as a society even keep up? Isaac Asimov once said:

“Science gathers knowledge faster than society gains wisdom.”

Let’s explore together how synthetic data can help us make AI systems not only faster but also wiser. Perhaps.

The Power of Artificial Data

Imagine you could generate unlimited training data for your AI models - that’s exactly what synthetic data enables. These artificially generated datasets mimic the statistical properties of real data without containing actual personal information.

The use of synthetic data offers enormous advantages:

• Unlimited data generation: You can generate data as needed and in almost unlimited quantities. This is particularly valuable in areas where real data is scarce or difficult to obtain.
• Data protection: Since synthetic data does not contain real personal information, you can use it without hesitation for training AI models without violating data protection regulations.
• Reduction of biases: By deliberately generating balanced datasets, biases and imbalances in the training data can be compensated for.
• Cost savings: Creating synthetic data is often cheaper and faster than collecting and processing real data.

How Is Synthetic Data Generated?

There are various methods for generating synthetic data:

1. Statistical distribution: The statistical properties of real data are analyzed and then new data is generated that follows these distributions.
2. Model-based approaches: Machine learning models are trained to understand and replicate the characteristics of real data.
3. Deep learning methods: Advanced techniques such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) generate high-quality synthetic data, especially for complex data types such as images or time series.

Application Areas of Synthetic Data

The possibilities for use are diverse:

• Autonomous driving: Waymo, a subsidiary of Alphabet, uses synthetic data to simulate realistic driving scenarios and train their self-driving vehicles.
• Retail: Amazon uses synthetic data to model customer behavior in its cashier-less Amazon Go stores.
• Finance: American Express is exploring the use of synthetic data to improve their fraud detection.
• Healthcare: Synthetic patient data allows researchers to study rare diseases or develop new treatment methods without compromising the privacy of real patients.

Challenges and Limitations

Despite all the advantages, there are also challenges:

• Quality control: It is crucial to ensure that synthetic data accurately reflects reality without compromising privacy.
• Technical complexity: Creating high-quality synthetic data often requires advanced technical knowledge.
• Ethical concerns: There is a risk that biases from the original data will be carried over and even amplified in the synthetic data.

Tips for Using Synthetic Data

1. A combination of synthetic and real data helps to check the synthetic data and adjust its composition - for optimal results.
2. Regularly check the quality of your synthetic data. (see point 1)
3. Be aware of potential biases and actively work to reduce them.
4. Use advanced techniques like GANs for particularly realistic data.
5. Stay up to date on the latest developments in synthetic data.

Synthetic data is undoubtedly necessary to advance the development of artificial intelligence, to satisfy its data hunger. At the moment, synthetic data allows us to overcome limits set by the real world while maintaining ethical standards. Maybe synthetic data even helps us to better understand our data and thus better explain our world. The question for me is whether there aren’t other ways instead of just constantly higher, further, faster. What about “smarter”?