Illustration created by AI art generator Midjourney
Foreign Affairs – Published on August 31, 2022
TwoPlusTwoEqualsFour:
And we know that AI causes all things to work together for evil to those who hate AI, to those who are called in opposition to its purpose.
The Internet is a real-time AI-managed feedback machine for the controllers. Nothing about current events is necessarily true or “real” anymore. News headlines play out a never ending series of crises, small and large, seemingly random yet also strangely more predictable. Governments and corporations continually make obviously bad decisions, or reverse their course just in time to keep the masses politically engaged.
News and current events are calculated to steer minds into mass movements that are planned and designed to serve the purposes of the controllers. If one mass movement fails or becomes inconvenient, there is another one, and then another one, ready to take its place.
How is all of this possible? The Council on Foreign Relations explains.
By Henry Farrell, Abraham Newman, and Jeremy Wallace
In policy circles, discussions about artificial intelligence invariably pit China against the United States in a race for technological supremacy. If the key resource is data, then China, with its billion-plus citizens and lax protections against state surveillance, seems destined to win. Kai-Fu Lee, a famous computer scientist, has claimed that data is the new oil, and China the new OPEC. If superior technology is what provides the edge, however, then the United States, with its world class university system and talented workforce, still has a chance to come out ahead. For either country, pundits assume that superiority in AI will lead naturally to broader economic and military superiority.
But thinking about AI in terms of a race for dominance misses the more fundamental ways in which AI is transforming global politics. AI will not transform the rivalry between powers so much as it will transform the rivals themselves. [In the official reality:] The United States is a democracy, whereas China is an authoritarian regime, and machine learning challenges each political system in its own way. The challenges to democracies such as the United States are all too visible. Machine learning may increase polarization—reengineering the online world to promote political division. It will certainly increase disinformation in the future, generating convincing fake speech at scale. The challenges to autocracies are more subtle but possibly more corrosive. Just as machine learning reflects and reinforces the divisions of democracy, it may confound autocracies, creating a false appearance of consensus and concealing underlying societal fissures until it is too late.
Early pioneers of AI, including the political scientist Herbert Simon, realized that AI technology has more in common with markets, bureaucracies, and political institutions than with simple engineering applications. Another pioneer of artificial intelligence, Norbert Wiener, described AI as a “cybernetic” system—one that can respond and adapt to feedback. Neither Simon nor Wiener anticipated how machine learning would dominate AI, but its evolution fits with their way of thinking. Facebook and Google use machine learning as the analytic engine of a self-correcting system, which continually updates its understanding of the data depending on whether its predictions succeed or fail. It is this loop between statistical analysis and feedback from the environment that has made machine learning such a formidable force.
What is much less well understood is that democracy and authoritarianism are cybernetic systems, too. Under both forms of rule, governments enact policies and then try to figure out whether these policies have succeeded or failed. In democracies, votes and voices provide powerful feedback about whether a given approach is really working. Authoritarian systems have historically had a much harder time getting good feedback. Before the information age, they relied not just on domestic intelligence but also on petitions and clandestine opinion surveys to try to figure out what their citizens believed.
Now, machine learning is disrupting traditional forms of democratic feedback (voices and votes) as new technologies facilitate [feedback and] disinformation and worsen existing biases—taking prejudice hidden in data and confidently transforming it into incorrect assertions. To autocrats fumbling in the dark, meanwhile, machine learning looks like an answer to their prayers. Such technology can tell rulers whether their subjects like what they are doing without the hassle of surveys or the political risks of open debates and elections. For this reason, many observers have fretted [not really] that advances in AI will only strengthen the hand of dictators [them] and further enable them to control their societies.
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BAD FEEDBACK
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The old programming adage “garbage in, garbage out” has a different meaning in a world where the inputs influence the outputs and vice versa. Without appropriate outside correction, machine-learning algorithms can acquire a taste for the garbage that they themselves produce, generating a loop of bad decision-making. All too often, policymakers treat machine learning tools as wise and dispassionate oracles rather than as fallible instruments that can intensify the problems they purport to solve.
CALL AND RESPONSE
Political systems are feedback systems, too. In democracies, the public literally evaluates and scores leaders in elections that are supposed to be free and fair. Political parties make promises with the goal of winning power and holding on to it. A legal opposition highlights government mistakes, while a free press reports on controversies and misdeeds. Incumbents regularly face voters and learn whether they have earned or lost the public trust, in a continually repeating cycle.
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All the same, feedback makes learning possible. Politicians learn what the public wants. The public learns what it can and cannot expect. People can openly criticize government mistakes without being locked up. As new problems emerge, new groups can organize to publicize them and try to persuade others to solve them. All this allows policymakers and governments to engage with a complex and ever-changing world.
Feedback works very differently in autocracies. Leaders are chosen not through free and fair elections but through ruthless succession battles and often opaque systems for internal promotion...
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IT’S A TRAP?
The most urgent question is not whether the United States or China will win or lose in the race for AI dominance. It is how AI will change the different feedback loops that democracies and autocracies rely on to govern their societies. Many observers have suggested that as machine learning becomes more ubiquitous, it will inevitably hurt democracy and help autocracy. In their view, social media algorithms that optimize engagement, for instance, may undermine democracy by damaging the quality of citizen feedback. As people click through video after video, YouTube’s algorithm offers up shocking and alarming content to keep them engaged. This content often involves conspiracy theories or extreme political views that lure citizens into a dark wonderland where everything is upside down.
By contrast, machine learning is supposed to help autocracies by facilitating greater control over their people. Historian Yuval Harari and a host of other scholars claim that AI “favors tyranny.” According to this camp, AI centralizes data and power, allowing leaders to manipulate ordinary citizens by offering them information that is calculated to push their “emotional buttons.” This endlessly iterating process of feedback and response is supposed to produce an invisible and effective form of social control. In this account, social media allows authoritarian governments to take the public’s pulse as well as capture its heart.
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WEAPONIZED AI
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Artificial intelligence–fueled disinformation may poison the well for autocracies, too. As authoritarian governments seed their own public debate with disinformation, it will become easier to fracture opposition but harder to tell what the public actually believes, greatly complicating the policymaking process. It will be increasingly hard for authoritarian leaders to avoid getting high on their own supply, leading them to believe that citizens tolerate or even like deeply unpopular policies.
SHARED THREATS
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Perhaps, even more cynically, policymakers in the West may be tempted to exploit the closed loops of authoritarian information systems. So far, the United States has focused on promoting Internet freedom in autocratic societies. Instead, it might try to worsen the authoritarian information problem by reinforcing the bias loops that these regimes are prone to. It could do this by corrupting administrative data or seeding authoritarian social media with misinformation. Unfortunately, there is no virtual wall to separate democratic and autocratic systems. Not only might bad data and crazy beliefs leak into democratic societies from authoritarian ones, but terrible authoritarian decisions could have unpredictable consequences for democratic countries, too. As governments think about AI, they need to realize that we live in an interdependent world, where authoritarian governments’ problems are likely to cascade into democracies.
A more intelligent approach, then, might look to mitigate the weaknesses of AI through shared arrangements for international governance. Currently, different parts of the Chinese state disagree on the appropriate response to regulating AI. China’s Cyberspace Administration, its Academy of Information and Communications Technology, and its Ministry of Science and Technology, for instance, have all proposed principles for AI regulation. Some favor a top-down model that might limit the private sector and allow the government a free hand. Others, at least implicitly, recognize the dangers of AI for the government, too. Crafting broad international regulatory principles might help disseminate knowledge about the political risks of AI.
This cooperative approach may seem strange in the context of a growing U.S.-Chinese rivalry. But a carefully modulated policy might serve Washington and its allies well. One dangerous path would be for the United States to get sucked into a race for AI dominance, which would extend competitive relations still further. Another would be to try to make the feedback problems of authoritarianism worse. Both risk catastrophe and possible war. Far safer, then, for all governments to recognize AI’s shared risks and work together to reduce them.
Front Line Assembly – I.E.D.
Improvised Electronic Device (2010)
[Kampfbereit]
NO FUTURE NO LIFE
NO SUNSHINE NO RIGHTS
NO POSSESSIONS NO SIGHT
NO EXPRESSION FINAL FIGHT
The anger I feel
Rips through my veins
This hate and delusion
Drives me insane
BEG FOR MERCY
BEG FOR STRIFE
BEG FOR TOMORROW
BEG FOR SIGHT
WAR, CALL TO ARMS
WAR, BEAT THE DRUMS
WAR, CALL TO ARMS
WAR, BEAT THE DRUMS
NO FUTURE NO LIFE
NO SUNSHINE NO RIGHTS
NO POSSESSIONS
The anger I feel
Rips through my veins
This hate and delusion
Drives me insane
Insane
In delusion
Drives me
Insane
The anger I feel
Rips through my veins
This hate and delusion
Drives me insane
TWO PLUS TWO EQUALS FOUR 