Rising attention to feedback from customers is a fast-growing trend in all markets today, and surveying different aspects of businesses is used as never before.
What we do here at Compellon may be viewed as a return to this original AI agenda. In no way do we discard the achievements of machine learning. On the contrary, we build upon the efforts of ML research to attack more ambitious challenges. At our core, to achieve the goal of resetting the man/machine borderline, we make two major deviations from the prevailing practices of machine learning today.
Long before founding Compellon and prior to immigrating to the US, I built advanced statistical analysis systems for companies both as a professor and a researcher in the Soviet Academy of Sciences. The most intriguing part of my work was the investigation of projects which all pursued reasonable objectives, had sufficient funding, were well equipped, and were conducted by a team of highly qualified specialists…and in spite of all this—failed. I saw such examples across many fields: manufacturing, environmental monitoring, medical diagnostics, acoustical testing of materials, and others. Figuratively speaking, my job in such projects was “autopsy” and “resurrection”. Overall, the main culprit in the overwhelming majority of cases was the use of inadequate assumptions based on well-established beliefs.