Why Real-World Data Will Fall Short in Your Computer Vision Project in 2026
Explore 2026 computer vision trends and the limits of relying solely on real-world data.
Synthetic Data vs Real Data – How to Choose?
Learn when synthetic or real data works best, how each affects AI performance, and why a hybrid approach gives the most reliable and scalable machine learning results.
Synthetic Data in 2030 – Technologies, Shifts and Challenges Ahead
See how synthetic data will evolve by 2030, which technologies will drive growth, and what challenges and standards will shape the next decade of AI development.
7 pillars of trust: how to build AI you can truly trust?
Check out the major criteria every trustworthy AI system should meet, and why they matter for building reliable and responsible models.
How SMEs Can Adopt Synthetic Data on a Small Budget?
A practical roadmap for SMEs to start using synthetic data, validate results, and scale workflows without enterprise-level costs or complex infrastructure.
Transparent ROI (Return on Investment) of Synthetic Data
Estimating the financial costs and benefits of implementing a Synthetic Data Cloud
What data does AI need?
Your computer vision project needs data that’s reliable, accurate, and diverse. But can real-world data alone meet those standards? In this post, we explore why it often falls short and how synthetic data fills the gap.
Synthetic Data and GDPR
Learn how artificial data can help protect privacy in the age of AI.
Unlocking the Future of Computer Vision: Our Journey at CVPR 2025
CVPR consistently delivers cutting-edge advancements that shape the future of our industry, providing a forum for discoveries that propel the field forward.
Driving the Future: Our Takeaways From InCabin USA 2025 in Detroit
The InCabin Detroit conference recently concluded, offering a focused and insightful exploration into the evolving landscape of automotive sensing, safety, and in-cabin technologies.