Strict verification instead of guesswork, how Kazakhstan conference reconnects AI to mathematical foundations

When ninety researchers from seven countries gather in a single hall, the goal is not to linger on the sparkle of generative interfaces but to descend to the solid mathematical rules that govern every layer in those models. In Kazakhstan's capital, Nazarbayev University hosted the opening session of the International Conference on Mathematics and Artificial Intelligence (ICMAI 2026), aiming to bridge the gap between abstract academic theory and practical computational applications and to build research-collaboration channels that go beyond the usual geographic boundaries.

The conference, organized by the Faculty of Science and Artificial Intelligence in cooperation with the Institute of Intelligent Systems and Artificial Intelligence at the university, was chaired by Professor Dorvodkhan Soragan and featured an intensive three-day program that included seven plenary lectures, thirty-two specialized sessions, and four training workshops. The aim was not merely to deliver traditional lectures but to train participants and students in computer science and mathematics programs on modern computational tools that give them direct exposure to cutting-edge research and make theories practically applicable.

Rigorous mathematical verification replaces probabilistic approaches in critical environments.

The scientific discussions focused on applying graph theory, linear algebra and multi-dimensional data analysis to the engineering of artificial-intelligence systems, alongside examining the reciprocal role AI can play in serving mathematics by detecting patterns and testing hypotheses. In this context, researchers highlighted recent experiments conducted by OpenAI on Navier-Stokes equations and the formalisation of mathematical proofs using the Lean language, strongly raising the issue of systematic, rigorous verification of machine-generated outputs before they are adopted in scientific and engineering communities.

This discussion has a directly practical dimension for those leading technological development in the region, especially with researchers from Saudi Arabia and the United Arab Emirates participating alongside delegations from Kazakhstan, Russia, Hungary, Azerbaijan and China. The message to engineering teams and research centres in the Gulf and Egypt is clear: move from a reliance on black-box models to building solutions grounded in mathematical proof and deterministic verification. If sectors such as energy management, transport networks or healthcare require error-intolerant reliability, merely training models on massive data sets is no longer sufficient; the skill now needed shifts toward mathematical verification and mastery of automated proof tools to ensure computational decision safety and reduce the cost of catastrophic errors.

Automated proof is establishing itself as a quality standard for systems before deployment.

Professor Soragan's remarks after the conference concluded affirm that the ambition is to make this gathering a regular platform for scientific exchange and joint research. The lesson for developers and investors goes beyond merely following an academic conference: watch how industry returns to its rigorous theoretical roots, because the next phase will be won not only by those who have the most processors but by those who possess the mathematical standards that prevent algorithms from hallucinating when decisions become costly.