Why synthetic data will be pivotal in building next-generation AI and automated technology (www.techradar.com)

A TechRadar Pro expert argues that synthetic data is shifting from a niche workaround to a foundational ingredient for next‑generation AI, solving limits of real-world data caused by scarcity, cost and privacy. Synthetic datasets—produced by simulations and generative models such as GANs and VAEs—let teams engineer rare edge cases (e.g., fraud patterns, autonomous‑vehicle maneuvers), expand demographic coverage, and share privacy-preserving benchmarks that accelerate iteration. The piece cites real outcomes and industry signals: MIT research showing synthetic data can sometimes outperform real data, and major players like Meta and Google using model‑generated data and distillation to train and slim models, while highlighting failures (e.g., Google Gemini’s fine‑tuning pitfalls) as reminders that diversity and contextual accuracy matter. Technically, synthetic data shines for reproducible, controllable scenario generation and for downstream workflows like RLHF and model distillation, but it’s not a panacea. Effective use requires strong simulation fidelity, rigorous validation, compute and domain expertise, and careful blending with real data to avoid “model collapse” or detached behavior. For AI/ML practitioners, the takeaway is practical: treat synthetic data as a powerful, complementary tool for robustness, fairness and compliance (EU AI Act–style constraints), while maintaining rigorous evaluation pipelines and a balance between artificial and observational data.