Hao Su
All AI mindsHao Su, full AI read
Hao Su, Associate Professor of Computer Science, UC San Diego (United States), ranks #411/520 on the AI Advancement Index (64.1). Known for PointNet co-author; ShapeNet and SAPIEN/ManiSkill embodied-AI simulation; 3D deep learning and sim-to-real manipulation benchmarks.
Dimension read
| Dimension | Value | Standing | What a high vs low value means, and where Hao Su sits |
|---|---|---|---|
| AAI AI Advancement (AAI) | 64.1 | Developing · #409/520 | Low here, lower relative influence within this elite set. ▲ high: among the very top minds advancing AI · ▼ low: lower relative influence within this elite set |
| Research influence Research influence | 70.0 | Moderate · #314/520 | Mid-pack. High would mean field-defining research contributions; low would mean limited direct research influence. ▲ high: field-defining research contributions · ▼ low: limited direct research influence |
| Frontier role Frontier role | 58.0 | Developing · #366/520 | Low here, removed from frontier development. ▲ high: central to building today's frontier AI · ▼ low: removed from frontier development |
| Thought leadership Thought leadership | 56.0 | Developing · #423/520 | Low here, limited public/field influence. ▲ high: shapes how the field and public think about AI · ▼ low: limited public/field influence |
| Field-building Field-building | 66.0 | Moderate · #296/520 | Mid-pack. High would mean builds the field, mentorship, institutions, tools, community; low would mean limited field-building footprint. ▲ high: builds the field, mentorship, institutions, tools, community · ▼ low: limited field-building footprint |
| Momentum Momentum | 70.0 | Developing · #366/520 | Low here, less active at the current frontier. ▲ high: driving AI's advancement right now · ▼ low: less active at the current frontier |
Strengths
- No standout dimension.
Risk factors
- A significant, well-rounded contributor to AI's advancement.
AI worldview
Ideas & positions
Hao Su is a leading researcher in 3D deep learning and robotics, with significant contributions to PointNet, ShapeNet, and the SAPIEN/ManiSkill simulation environments. His work emphasizes the development of robust, data-driven methods for understanding and manipulating 3D environments, which are crucial for advancing embodied AI. While he has not made extensive public statements on broader AI policy issues, his research suggests a focus on practical, incremental advancements in AI capabilities, particularly in the realm of robotics and automation. He has not publicly taken a stance on existential risk, open vs closed models, or regulation.
What shapes the view
Hao Su's views are shaped by his academic background in computer science and his practical experience in developing 3D deep learning and robotics systems. His work often involves collaboration with industry and other academic institutions, indicating a pragmatic approach to advancing technology through interdisciplinary efforts. His focus on sim-to-real manipulation benchmarks and embodied AI suggests a belief in the importance of real-world applicability and the integration of AI into physical systems.
The AI-powered future they see
Hao Su's research implies a future where AI, particularly in the form of advanced robotics, plays a significant role in automating tasks that require understanding and interacting with complex 3D environments. This could lead to improvements in manufacturing, healthcare, and service industries. However, his public statements do not explicitly address the broader societal implications of such advancements, focusing instead on technical achievements and practical applications.