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In 1965, Glushkov's team designed the MIR-1 computer for educational institutions, small engineering offices, and research departments. MIR had one remarkable feature over Promin—a hardwired machine language close to high-level programming languages. It could perform calculations much faster, with the ability to complete 200-300 operations with 5-bit numbers per second.
A data entry operator types information
into the MIR-1 computer.
Inside the same MIR-1 computer exhibited
at Borys Paton State Polytechnic Museum.
MIR Logo

Glushkov presented MIR-1 and its step-by-step microprogrammed control to the world at the London exhibition in 1967. It caught the attention of IBM, who purchased the machine. However, now we know that IBM bought the machine not for its practical use but to prove to their competitors that the "Soviets" had known about the principle for a long time and had implemented it in a mass-produced machine. In fact, the Kyiv-based team had already used it on the Promin computer. Each new iteration of these computers was a step forward in intelligent computer building.

MIR People
             Engineers at the control panel
                      of the new MIR computer
Promin photo
The MIR-2 computer, first manufactured in 1969, had a capacity of roughly 8,000 with 13-bit numbers per second. The most intriguing innovations were the vector display and a graphic pen, so familiar to us today. Glushkov writes: "Displays look like television screens, and computers can put letters, numbers, or graphics on them. By using a special device called a "graphic pen," people can draw on the screen or erase images, move symbols or parts of a picture, etc."
While developing the MIR-series computers, Glushkov had another task: creating a computer language as close to human language as possible (particularly mathematical.) In 1971, the MIR-3 was released with 20 times greater productivity than the MIR-2. However, it never enjoyed the popularity its predecessors.
During the 70s and 80s, researchers at the Institute of Cybernetics actively attempted to model thought processes, focusing on artificial intelligence and image recognition. "Universal digital machines make it possible to model and test various ways of recognizing images, including machine learning systems," wrote Viktor Glushkov. "As of today, numerous such systems have been developed and tested. For instance, the system learning to recognize shapes imitates the human brain’s adaptive traits in that particular activity."
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