白皮书：数据瓶颈正在拖慢视觉与物理 AI
Inside the Data Bottleneck Slowing Visual and Physical AI
IEEE Spectrum 与 Wiley 发布、Voxel51 赞助的白皮书基于 2026 年对 700 多名从业者的调研，指出多数模型失败源于数据问题，策展数据比追求更大架构更重要。78% 的团队已从视觉与物理 AI 获得可衡量的价值，但 74% 认为该领域投入相对机会仍显不足。成功交付的团队在数据工作上投入的时间接近挣扎团队的 3 倍，92% 的从业者看好该领域下一阶段走向。
Download this complimentary White Paper today!
This white paper reports how more than 700 practitioners build visual and physical AI, why models fail, and where data work decides production success.
What you will learn about:
- Why 78% of teams already see measurable value from visual & physical AI, while 74% still consider the field underinvested relative to its opportunity.
- Why the teams that ship successfully invest nearly 3x more time in data work than teams that struggle
- Where 92% of practitioners believe the field is heading next
Click ‘LOOK INSIDE’ to Download Now.
IEEE Spectrum and Wiley are proud to bring you this White Paper, sponsored by Voxel51
More Information
The last decade of AI progress was built on text, but the frontier has shifted toward data from the physical world. Video, LiDAR point clouds, sensor streams, and other high-dimensional data now drive systems that perceive, reason, and act in physical space. This report, based on a 2026 survey of more than 700 professionals, documents how teams actually build physical AI today. It finds that data problems cause the majority of model failures, and that curating data matters more than chasing larger architectures. Annotation remains costly and wasteful, because teams often label everything and then discard much of it before production. The findings show why data work, not data collection, separates teams that ship from teams that stall.
来源：IEEE Spectrum · AI · content.knowledgehub.wiley.com