Staging the OGAS, 1962 to 1969 119
among others.^15 Optimization modeling, which would have contributed
to the management software running the OGAS economic reform) were
developed from 1962 to 1969 by Vladimir Mikhalevich, O. O. Bakaev, Yu.
M. Ermol’ev, I. V. Sergienko, V. L. Volkovich, B. M. Pshenychniyi, V. V.
Shkurba, N. Z. Shor, and others. Glushkov toiled alongside A. A. Stognii
and A. G. Kukharchuk as principal designer in developing the Dnepr-2, a
transistor computer. He also headed a team that included Y. Blagoveshen-
sky, Aleksandr A. Letichevsky, V. Losev, I. Mochanov, S. Pogrebinsky, and
A. A. Stognii in developing the MIR-1 engineering calculation machine, an
exhibition version of which IBM purchased in London.^16
Other supporting teams in the Glushkov school indirectly reflect on the
OGAS Project. Kapitonova and Letichevsky, for example, helped Glushkov
theorize an “analytic” mathematical human language programming lan-
guage and an algorithmic design in computer design automation.^17 This
team helped nudge the field of artificial intelligence away from the notion
that the brain was machine-inspired (away from McCulloch and Pitts’s
claim that the brain follows logical circuitry). Instead, they worked on
building a brain-inspired machine that was “capable of carrying out com-
plex creative activities,” continuously seeking to reveal the “higher intel-
lect” of machines modeled after mechanisms of the mind.^18 If there was a
danger in the brain and machine metaphor, it ran only one way for Glush-
kov: “the danger is not that machines will begin to think like people,” he
intoned, “but that people will begin to think like machines.”^19
Are National Networks More Like Brains or Nervous Systems?
In 1962, Glushkov imagined the OGAS as a “brainlike” (mozgopodnobyi)
network for managing the national economy and extending the life experi-
ence of the nation and its inhabitants. Consider the implications for the
cybernetic analog between neural networks and national computer net-
works. As already noted, cybernetics brings to bear powerful conceptual
frameworks for imagining structural analogies between ontologically differ-
ent information systems—organisms, machines, societies, and others. The
cybernetic instinct rushes many visionaries to profound structural insights
but also to overly determined design decisions. The circuitry of a com-
puter chip and the neural networks of a mind do not resemble each other,
although cybernetics earns its keep by finding usable analogs between
them. This cybernetic system analog instinct—to design in beautiful sym-
metry where not necessary—helps to explain the consistent hierarchically