— Ch. 1 · Born Among Teachers —
Alexey Ivakhnenko.
~6 min read · Ch. 1 of 6
Aleksey Ivakhnenko entered the world on the 30th of March 1913 in Kobelyaky, Poltava Governorate. His family background was rooted in education as both parents worked as teachers. His mother Maria Operman carried German descent within her lineage. The young boy grew up surrounded by books and academic expectations from his earliest days. He attended Electrotechnical college in Kyiv where he completed his studies in 1932. Two years followed as an engineer working on the construction of a large power plant in Berezniki. This early industrial experience shaped his practical approach to complex engineering problems before he ever touched artificial intelligence. In 1938 he graduated from the Leningrad Electrotechnical Institute after those formative field years. Wartime conditions brought him to Moscow where he joined the All-Union Electrotechnical Institute. There he investigated automatic control problems under the leadership of Sergey Lebedev inside a busy laboratory setting. Returning to Kyiv in 1944 marked a turning point for his personal and professional trajectory. That same year he received his Ph.D. degree while continuing research across various Ukrainian institutions. By 1954 he had earned his D.Sc. degree establishing himself as a serious scholar in the field. The path from a teacher's son to a leading cyberneticist unfolded through decades of steady academic progression.
The Year Inductive Modeling Began
A new stage in scientific work began when the journal Avtomatika published his article in 1968. The title read Group Method of Data Handling , a rival of the method of stochastic approximation. This publication marked the official birth of what would become known as GMDH. Ivakhnenko led a professional team of mathematicians and engineers at the Institute of Cybernetics during this period. They developed an approach that diverged sharply from traditional deductive methods used by other researchers. Instead of building models from general theory down to particular cases they worked from specified data upward. A researcher selects a class of models after inputting raw data into the system. The computer then handles most routine work minimizing human influence on objective results. This shift represented one of the earliest implementations of artificial intelligence thesis principles. Modern computers enabled them to test ideas that previous generations could not even imagine exploring. The method allowed for pattern recognition and complex systems forecasting without rigid pre-existing assumptions. It opened doors to solving problems where uncertainty played a major role in outcomes. The team proved that machines could act as powerful advisors to humans rather than simple calculators. Their work laid foundations for future developments in deep learning networks across multiple disciplines worldwide.