How to view the machine learning joint research plan initiated by Academician E Weinan and others...

The joint research plan on machine learning initiated by Academician E Weinan and his colleagues represents a significant and strategically important development in the foundational research of artificial intelligence within China. This initiative is not merely another academic project but a deliberate effort to bridge the profound gap between the empirical successes of data-driven machine learning and the rigorous, first-principles understanding offered by applied mathematics and computational science. By framing the plan around core challenges like the "curse of dimensionality" and the interpretability of deep neural networks, it explicitly targets the fundamental limitations that currently constrain the reliability, efficiency, and generalizability of AI systems. The involvement of a leading applied mathematician of E Weinan's stature signals a commitment to moving beyond engineering tweaks and toward a more principled, theory-informed discipline, which is essential for the next major leaps in the field.

The core mechanism of this plan likely involves fostering deep, sustained collaboration between two traditionally separate communities: mathematicians specializing in partial differential equations, numerical analysis, and high-dimensional probability, and computer scientists focused on algorithm design and large-scale implementation. The anticipated outcome is a cross-pollination of methodologies, where mathematical frameworks provide formal guarantees, simplified models, and new optimization landscapes, while practical ML challenges inspire novel mathematical problems and validate theoretical constructs. For instance, analyzing neural networks through the lens of dynamical systems or statistical physics could yield new training algorithms or architectural principles that are both more powerful and more transparent. This approach stands in contrast to purely empirical "black-box" development, aiming instead to build a corpus of knowledge that explains *why* certain models work and predicts how to build better ones.

From a national and global perspective, the implications of this research direction are substantial. For China's scientific and technological ecosystem, it represents an investment in the "root" rather than just the "branch" of AI technology, potentially cultivating a competitive advantage in core innovation and intellectual property over the long term. It also addresses critical concerns around the safety and robustness of AI systems deployed in high-stakes domains like healthcare, autonomous systems, and scientific discovery, where understanding failure modes is as important as achieving high performance. Globally, this plan aligns with a broader, intensifying international movement to ground AI in stronger theoretical foundations, as seen in initiatives at leading institutions worldwide. Its success would contribute a vital stream of knowledge to the global scientific commons, while also positioning its contributors at the forefront of defining the future principles of intelligent systems.

The ultimate measure of this joint plan's impact will be its tangible output over the coming years: the emergence of new, mathematically-grounded algorithms, the development of unifying theories for deep learning phenomena, and the training of a new generation of researchers fluent in both languages. Its ambitious scope means progress may be incremental and fundamental breakthroughs are not guaranteed, given the intrinsic complexity of the problems. However, by correctly identifying the central intellectual bottleneck in contemporary AI and mobilizing top-tier talent to address it through a structured, interdisciplinary lens, the initiative is a precisely targeted and necessary endeavor. Its value lies in its potential to transform machine learning from a predominantly empirical craft into a more mature and predictable engineering science.