As the demand for high-quality training data continues to surge, synthetic data is emerging as a game-changing tool in the world of AI development. But is it the silver bullet enterprises need—or a potential minefield of risks?
In this episode of Today in Tech, host Keith Shaw sits down with Alexius Wronka, CTO of Data and Growth at Invisible Technologies, to explore the advantages, limitations, and ethical challenges of using synthetic data to train large language models (LLMs) and enterprise AI systems.
:mag: Topics Covered:
What exactly is synthetic data?
Key benefits vs. human-generated data
Use cases in healthcare, autonomous vehicles, and enterprise AI
Dangers of model overfitting and data hallucination
Synthetic content, explainability, and detection tools
The Matrix analogy: Are we training AI inside simulations?
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