Three Questions about AI
The Scientific Question
The "Scientific Question" series at the World Artificial Intelligence Conference (WAIC) is set to explore the integration of artificial intelligence with scientific research. WAIC 2025 will take place in Shanghai from July 26th to 28th, featuring the largest lineup in the conference's history under the theme, "In the Age of Intelligence, All Nations on Earth Pull Together."
The conference includes:
- Forum: July 26 - 28, 2025
- Exhibition: July 26 - 29, 2025
- Venue: Expo Center, Expo Exhibition Hall, Xuhui West Bund, among others
This year, WAIC highlights the "Three Questions of AI," focusing on mathematics, science, and models. Mathematics provides a cognitive framework through axiomatic formulas; science builds understanding on empirical evidence; models bridge the two to transform theory into practice. Together, these perspectives quantify, explain, and apply knowledge—demonstrating AI's role in advancing innovation across disciplines.
The "Questions of Science" activities at WAIC foster high-level academic exchanges, tackling key challenges in AI-science integration. This forum is valuable for both AI researchers and industry professionals seeking fresh insights and practical inspiration.
AI and Human Scientists: The Synergy of Rational Analysis and Innovative Inspiration
Human scientists bring intuition and creativity to research frontiers, but large-scale data analysis challenges their capacity. AI excels in processing complex data and logical reasoning. Combining AI's analytical power with human insight offers a complementary advantage that can accelerate scientific breakthroughs.
For example, in drug development, AI efficiently screens biological data to identify drug targets, while human scientists use their expertise to validate these findings experimentally. This collaboration shortens development cycles and enhances the precision of new therapies.
Data and Models: Systematic Integration to Overcome Non-Deterministic Barriers
Scientific research spans diverse data types—biological, physical, clinical—that often use incompatible representations. Achieving alignment across these modalities is essential for cross-disciplinary collaboration and faster knowledge creation.
In healthcare, aligning medical imaging with clinical diagnosis data supports more accurate diagnoses and personalized treatments. Developing comprehensive models for non-deterministic causal reasoning and bridging physical-digital semantic gaps is foundational for technologies like digital twins and intelligent manufacturing.
Such models enable seamless interaction between virtual simulations and real-world systems, improving prediction accuracy and resource allocation in areas including smart factories, transportation, and urban management.
Computational Boundaries: Collaborative Exploration of Quantum and Classical Computing
Balancing quantum and classical computing capabilities is key to advancing scientific simulations. Quantum computing offers exponential speed-ups in specific tasks, whereas classical computing provides stability and broader applicability.
Defining how these two approaches can collaborate will pave the way for hybrid systems that leverage the strengths of generative language models alongside quantum processors, even with limited quantum bits. This synergy holds promise for discoveries in materials science, cryptography, and beyond.
Life Sciences: Frontier Breakthroughs Driven by Holographic Data
In life sciences, AI-driven hypotheses generation, virtual cell and organ modeling, and high-throughput system automation are essential for tackling major diseases and improving health outcomes. AI-assisted optimization in breeding also offers potential solutions for global food security challenges.
Physical Sciences: Technological Innovation through Multi-Dimensional Analysis
The physical sciences are undergoing transformation through AI integration. Processing high-dimensional data enables new insights into complex material properties, atmospheric dynamics, and astrophysical phenomena.
For instance, AI helps simulate atmospheric circulation by analyzing multiple meteorological variables, improving extreme weather forecasts. In astrophysics, AI processes stellar and galactic data to detect unique celestial events and test cosmological theories.
High-energy physics benefits from AI's ability to identify signals in particle collision data, accelerating particle discovery. In condensed matter physics, AI predicts material behaviors under extreme conditions, such as superconductivity, while materials science uses graph neural networks to screen candidate materials and optimize production processes.
The "Questions of Science" series invites young researchers to contribute fresh ideas and explore solutions to pressing scientific challenges. These discussions offer valuable perspectives on how AI can reshape scientific inquiry.
If you want to grasp how AI is transforming research and join in on these forward-thinking conversations, the "Questions of Science" activities at WAIC provide an excellent opportunity to engage with peers and experts alike.
Discover More with WAIC UP!
The World Artificial Intelligence Conference has introduced its first publication, "WAIC UP!", a guide to AI evolution that aims to inspire and connect thinkers across disciplines. This resource encourages exploration of technology, human potential, and future civilizations centered on intelligence.
Explore WAIC UP! and related AI learning resources to deepen your understanding and prepare for the evolving landscape of artificial intelligence in science and research.
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