What do cows and AI have in common?

Synthetic data is playing an increasingly important role in AI model development - last year Gartner predicted that 60% of the data used to train AI and analytics projects will be synthetic by the end of 2024. This AI-generated data offers compelling benefits: cost savings, enhanced privacy protection, and the potential to mitigate biases inherent in real-world datasets. It allows for the training of models without the limitations of traditional data, while still mimicking the statistical properties of real-world data.

Moreover, Since the training datasets for generative AI models tend to be sourced from the Internet, today’s AI models are unwittingly being trained on increasing amounts of AI-synthesized data but here’s the catch:

Degenerative AI - watering down milk

According to research it only take a couple of iterations before the models start to collapse. This model collapse is a degenerative process affecting next-generation generative models, in which the data they generate end up polluting the training set of the next generation. Being trained on polluted data, they then mis-perceive reality.

Without enough fresh real data in each generation of an autophagous (“self-consuming”) loop, future generative models are doomed to have their quality or diversity progressively decrease - researchers of Rice University dub this condition as Model Autophagy Disorder (MAD) making analogy to mad cow disease. Each iteration dilutes the model’s accuracy, like watering down milk repeatedly until it barely resembles the original.

Training AI models on synthetic data can lead to "model collapse", a degenerative process where models, over generations, lose accuracy and misrepresent the original data distribution. This collapse occurs as models trained on AI-generated data start to amplify errors, especially in low-probability events, and produce unrealistic outputs.

Model collapse is a degenerative and diluting process that can occur in as few as four or five iterations.

Here is how it unfolds:

1. An AI model generates synthetic data.

2. This synthetic data is used to train the next generation of AI models.

3. The new models, trained on artificial data, begin to misperceive reality.

4. The cycle repeats, with each generation becoming further removed from real-world patterns.

The Implications for Your AI Program

The consequences of Degenerative AI can be severe:

• Diminished Accuracy: Models trained on synthetic data may make critical errors, especially in edge cases.

• Reduced Diversity: Your AI's ability to handle the full spectrum of real-world scenarios may be compromised.

• Stifled Innovation: As models drift from reality, their capacity for genuine innovation decreases.

• Amplified Errors: Minor inaccuracies in early generations can become major distortions over time.

• Biased Predictions: Particularly for rare or nuanced cases, your AI may produce increasingly unreliable outputs.

The use of synthetic data in AI is not inherently problematic - it's a powerful tool when used correctly. The key lies in understanding its limitations and implementing proper safeguards.

With the right strategies, you can harness the benefits of synthetic data while avoiding the pitfalls of Degenerative AI.

Are you concerned about the risks in your AI program? Let's connect and discuss how we can ensure your AI remains robust, accurate, and grounded in reality.