Three Questions about AI ② The Scientific Question | The "Scientific Question" series of activities at WAIC is about to kick off to explore the new frontiers of the integration of artificial intelligence and scientific academia.

WAIC 2025

World Artificial Intelligence Conference

Forum: July 26 - 28, 2025

Exhibition: July 26 - 29, 2025

Venue: Expo Center, Expo Exhibition Hall, Xuhui West Bund, etc.

To delve deeper into the core propositions of AI, this year's WAIC has put forward the "Three Questions of AI" - directly hitting the cutting - edge topics that urgently need to be discussed and speculated in the directions of mathematics, science, and models. The question of mathematics deduces laws through axiomatic formulas and builds a cognitive framework; the question of science is rooted in empirical evidence and explores the essence of nature; the question of models integrates the two and transforms the abstract into the practical.

The three questions coexist. Mathematics quantifies science, science gives meaning to mathematics, and models promote the implementation of wisdom. The three work together to demonstrate the profound value of innovation in diverse fields.

The upcoming "Questions of Science" series of activities of the World Artificial Intelligence Conference focus on the key propositions in the process of integrating artificial intelligence and science and aim to build a high - quality academic exchange platform. Whether for artificial intelligence scholars or industry insiders, this intellectual feast will bring profound inspiration and cutting - edge insights.

AI and Human Scientists: The Synergy of Rational Analysis and Innovative Inspiration

In the field of scientific exploration, human scientists, relying on intuition and inspiration, can often open up new research frontiers. However, when facing massive data and complex analyses, their abilities have objective limitations. Artificial intelligence systems show significant advantages in data processing and logical reasoning. How can the analysis and reasoning abilities of AI scientists be integrated with the intuition and inspiration of human scientists to achieve complementary advantages? This is not only the key to improving scientific research efficiency but also a new core proposition for catalyzing major scientific breakthroughs in the era of artificial intelligence. In the field of new drug research and development, AI can efficiently analyze massive biological data and accurately screen potential drug targets; human scientists, relying on academic intuition and experimental experience, conduct in - depth verification of the feasibility of the targets. The synergy of the two will significantly shorten the drug research and development cycle.

Data and Models: Systematic Integration to Break Through the Non - Deterministic Barriers

Scientific research involves a multi - modal data system. The data representation methods in fields such as biology and physics are significantly different, just as there are natural barriers in cross - language communication. How to achieve the representation alignment of scientific data modalities is a fundamental question for promoting cross - field scientific research collaboration and accelerating knowledge innovation. Take the field of medical health as an example. The representation alignment of medical imaging data and clinical diagnosis data will provide strong support for accurate diagnosis and the formulation of personalized treatment plans.

The systematic construction of a world model for non - deterministic scientific causal reasoning and the bridging of the semantic gap between the physical and digital world models, although seemingly abstract, are actually closely related to the future social development. The former provides a theoretical framework for the accurate prediction of complex systems such as the climate system and financial markets; the latter is a prerequisite for the real implementation of cutting - edge technologies such as digital twins and intelligent manufacturing, which can promote the seamless collaboration between the virtual and real worlds and achieve accurate prediction and optimal allocation in fields such as intelligent unmanned factories, intelligent transportation, and urban governance.

Computational Boundaries: Collaborative Exploration of Quantum and Classical Computing

The game boundary between quantum and classical computing in full - scale scientific intelligent simulation is the core issue for unleashing computational potential. Quantum computing has exponential acceleration capabilities in specific tasks, while classical computing has advantages in stability and universality. Defining the collaborative boundary between the two can provide a new path for building a collaborative architecture between generative language models and quantum computing under the current condition of scarce quantum bits, and enable the accidental important discoveries in the scientific field empowered by microscopic quantum effects. This will provide efficient computing tools for fields such as materials science and cryptography and promote double breakthroughs in basic research and applied technologies.

Life Sciences: Frontier Breakthroughs Driven by Holographic Data

In the field of life sciences, propositions such as automatically generating original scientific hypotheses, constructing artificial intelligence virtual cells and organs for virtual experiments, and promoting the computerization and experimental intervention of high - throughput systems are directly related to the conquest of major diseases and the improvement of human health levels. The exploration of optimizing breeding through artificial intelligence technology provides an innovative path for solving the global food security problem.

Physical Sciences: Technological Innovation through Multi - Dimensional Analysis

The field of physical sciences is in a critical period of transformation. The intervention of artificial intelligence technology brings dawn to the conquest of many difficult problems. The identification and analysis of dynamic high - dimensional scientific representation patterns, as well as the research on the efficient patterns of high - dimensional evolution equations and cross - scale material systems, are crucial for in - depth understanding of the complex relationships between the material characteristics in various fields.

In the field of physics, artificial intelligence expands the scientific research paradigm by innovating the way of high - dimensional data processing: In the research of the atmospheric system, it processes multi - dimensional meteorological data such as temperature and humidity. Through the analysis of dynamic high - dimensional scientific representation patterns, it breaks through the limitations of traditional models on non - linear atmospheric motion and realizes accurate simulation of atmospheric circulation and efficient prediction of extreme weather; in the field of astrophysics, relying on artificial intelligence systems to process high - dimensional evolution data of stars and galaxies, simulate galaxy formation, and analyze galaxy spectra, it can quickly identify special celestial phenomena to discover new celestial bodies or verify cosmological theories; in high - energy physics, artificial intelligence is used to identify dynamic scientific representation patterns of high - dimensional data containing information such as energy and momentum generated by particle collision experiments, efficiently screen physical signals, and accelerate the discovery of new elementary particles and the revelation of interaction laws; in condensed matter physics, artificial intelligence captures the efficient patterns of high - dimensional evolution equations, processes multi - dimensional data of matter under extreme conditions, simulates phase transition processes and electron behaviors, such as predicting the critical temperature of superconducting materials, and promotes relevant research to break through traditional cycle limitations.

In the field of materials science, artificial intelligence builds models based on graph neural networks and materials science databases, automatically simulates and screens candidate materials, predicts material characteristics through in - depth analysis of experimental data, shortens the research and development cycle, and at the same time optimizes the preparation process to improve production quality, revolutionizing the traditional research, development, and production paradigms.

In this "Questions of Science" series of activities, young scientific researchers will carry cutting - edge concepts and innovative perspectives to conduct in - depth dialogues. Through the collision of ideas, they will explore new ideas for solving key scientific problems and provide forward - looking insights into the future path of integrating artificial intelligence and science. If you want to understand the internal logic of how artificial intelligence reshapes scientific research and witness the in - depth discussion of cutting - edge propositions, the "Questions of Science" series of activities sincerely invite your participation. Let's witness this knowledge event together and explore the infinite possibilities of integrating artificial intelligence and science.

Hello:

The World Artificial Intelligence Conference (WAIC) has launched its first publication "WAIC UP!", a "Guide to Evolution in the AI Era".

WAIC UP! WAKE UP MORE！

We invite the vanguard forces of global AI and cross - fields to jointly release the power of thinking and the propositions of wisdom, aiming to wake up more people and explore the infinite possibilities related to technological leaps, self - boundaries, and future civilizations.

Get ready! Release your thinking, awaken your actions, and explore the possibilities that have not yet emerged with us, and outline a panoramic view of the future intelligent civilization centered on humans!

"WAIC UP!"

Unlock your "Guide to Evolution in the AI Era" now.