CIA “Siren Servers’”Can Predict Social Unrest Days Before It Happens

The Central Intelligence Agency (CIA) is reported that it’d be using powerful supercomputers, dubbed “Siren Servers” by computer philosophy writer Jaron Lanier, to predict social unrest days before it happens. The technology is based on a combination of machine learning and deep learning.

“We have, in some instances, been able to improve our forecast to the point of being able to anticipate the development of social unrest and societal instability some I think as near as three to five days out,” said CIA Deputy Director for Digital Innovation Andrew Hallman on Tuesday at the Federal Tech event.

According to Hillman, the agency has built up its “anticipatory intelligence” through the use of deep learning and machine learning servers that can process an incredible amount of data.

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computationallearning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”.

Deep learning, also known as deep structured learning, is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple processing layers.

What comes with CIA’s claims are some interesting theoretical possibilities, if not conundrum, for the future of the agency.

One is that the CIA’s siren servers will become so efficient that they will predict all social uprising and will be able to prevent it. If they are successful in doing that, there would be no need for the CIA as their technology could predict and prevent any societal upheavals, and the agency would be obsolete.

Another potential outcome would be that the CIA could use the data and not tell anyone, just like the finance sector did, and then make calculated decisions on whether or not to intervene in any socially distressing situation.

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