# Topic: Systems/Cybernetics

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# AI Winter

United States/U.S. Government United States Technology Computing Systems Cognitive science Linguistics Computing/Computer science Robotics Transhumanism Linguistics/Applied Linguistics Systems/Cybernetics

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The term was coined by analogy to the idea of a nuclear winter. The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later.

The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research. At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the "winter" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.

Hype is common in many emerging technologies, such as the railway mania or the dot-com bubble. The AI winter was a result of such hype, due to over-inflated promises by developers, unnaturally high expectations from end-users, and extensive promotion in the media . Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in 2002 that "there's this stupid myth out there that AI has failed, but AI is around you every second of the day." In 2005, Ray Kurzweil agreed: "Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry."

Enthusiasm and optimism about AI has increased since its low point in the early 1990s. Beginning about 2012, interest in artificial intelligence (and especially the sub-field of machine learning) from the research and corporate communities led to a dramatic increase in funding and investment.

# Bremermann's limit

Computing Physics Systems Systems/Cybernetics

Bremermann's limit, named after Hans-Joachim Bremermann, is a limit on the maximum rate of computation that can be achieved in a self-contained system in the material universe. It is derived from Einstein's mass-energy equivalency and the Heisenberg uncertainty principle, and is c2/h ≈ 1.36 × 1050 bits per second per kilogram. This value is important when designing cryptographic algorithms, as it can be used to determine the minimum size of encryption keys or hash values required to create an algorithm that could never be cracked by a brute-force search.

For example, a computer with the mass of the entire Earth operating at the Bremermann's limit could perform approximately 1075 mathematical computations per second. If one assumes that a cryptographic key can be tested with only one operation, then a typical 128-bit key could be cracked in under 10−36 seconds. However, a 256-bit key (which is already in use in some systems) would take about two minutes to crack. Using a 512-bit key would increase the cracking time to approaching 1072 years, without increasing the time for encryption by more than a constant factor (depending on the encryption algorithms used).

The limit has been further analysed in later literature as the maximum rate at which a system with energy spread ${\displaystyle \Delta E}$ can evolve into an orthogonal and hence distinguishable state to another, ${\displaystyle \Delta t={\frac {\pi \hbar }{2\Delta E}}.}$ In particular, Margolus and Levitin have shown that a quantum system with average energy E takes at least time ${\displaystyle \Delta t={\frac {\pi \hbar }{2E}}}$ to evolve into an orthogonal state. However, it has been shown that access to quantum memory in principle allows computational algorithms that require arbitrarily small amount of energy/time per one elementary computation step.

# Project Cybersyn (1971)

Computing Economics Systems Systems/Cybernetics Chile

Project Cybersyn was a Chilean project from 1971–1973 during the presidency of Salvador Allende aimed at constructing a distributed decision support system to aid in the management of the national economy. The project consisted of four modules: an economic simulator, custom software to check factory performance, an operations room, and a national network of telex machines that were linked to one mainframe computer.

Project Cybersyn was based on viable system model theory approach to organizational design, and featured innovative technology for its time: it included a network of telex machines (Cybernet) in state-run enterprises that would transmit and receive information with the government in Santiago. Information from the field would be fed into statistical modeling software (Cyberstride) that would monitor production indicators, such as raw material supplies or high rates of worker absenteeism, in "almost" real time, alerting the workers in the first case and, in abnormal situations, if those parameters fell outside acceptable ranges by a very large degree, also the central government. The information would also be input into economic simulation software (CHECO, for CHilean ECOnomic simulator) that the government could use to forecast the possible outcome of economic decisions. Finally, a sophisticated operations room (Opsroom) would provide a space where managers could see relevant economic data, formulate feasible responses to emergencies, and transmit advice and directives to enterprises and factories in alarm situations by using the telex network.

The principal architect of the system was British operations research scientist Stafford Beer, and the system embodied his notions of organisational cybernetics in industrial management. One of its main objectives was to devolve decision-making power within industrial enterprises to their workforce in order to develop self-regulation of factories.

# Johns Hopkins Beast

Systems Robotics Systems/Cybernetics

The Johns Hopkins Beast was a mobile automaton, an early pre-robot, built in the 1960s at the Johns Hopkins University Applied Physics Laboratory. The machine had a rudimentary intelligence and the ability to survive on its own. As it wandered through the white halls of the laboratory, it would seek black wall outlets. When it found one it would plug in and recharge.

The robot was cybernetic. It did not use a computer. Its control circuitry consisted of dozens of transistors controlling analog voltages. It used photocell optics and sonar to navigate. The 2N404 transistors were used to create NOR logic gates that implemented the Boolean logic to tell it what to do when a specific sensor was activated. The 2N404 transistors were also used to create timing gates to tell it how long to do something. 2N1040 Power transistors were used to control the power to the motion treads, the boom, and the charging mechanism.

The original sensors in Mod I were physical touch only. The wall socket was detected by physical switches on the arm that followed the wall. Once detected, two electrical prongs were extended until they entered the wall socket and made the electrical connection to charge the vehicle. The stairway, doors, and pipes on the hall wall were also detected by physical switches and recognized by appropriate logic.

The sonar guidance system was developed for Mod I and improved for Mod II. It used two ultrasonic transducers to determine distance, location within the halls, and obstructions in its path. This provided "The Beast" with bat-like guidance. At this point, it could detect obstructions in the hallway, such as people in the hallway. Once an obstruction was detected, the Beast would slow down and then decide whether to stop or divert around the obstruction. It could also ultrasonically recognize the stairway and doorways to take appropriate action.

An optical guidance system was added to Mod II. This provided, among other capabilities, the ability to optically identify the black wall sockets that contrasted with the white wall.

The Hopkins Beast Autonomous Robot Mod II link below was written by Dr. Ronald McConnell, at that time a co-op student and one of the designers for Mod II.