Would a government run with algorithms and AI be better or worse than the government we have now? The answer to this question, which should fill everyone who ponders it with a kind of religious dread, can only be approached by beginning with a consideration of in what sense a social system run with algorithms and artificial intelligence—an automated system—could actually amount to a government. It strains the limits of out psychological tolerance to accept the suggestion that machines could govern people; rule by machine is hard to describe as governance, which rightly feels to us as inherently a matter in which humans play the definitive role on both sides of the governing relation.
Some government employees operate like inhuman robots. When they want too.
Buying stocks is some sort of AI.
Twitter and Facebook have thousands of bots to keep traffic high.
Could go on...
How is any of that good? Or how in its current use is it good? How is this working out for us? All has to do with money and shirking responsibility. Lowest denominator. Something goes wrong you get the guy from “Hogan’s Hero’s” with “I know nothing!”. All machines break, all tools break, nothing lasts forever.
Automate everything people might forget how to live and exist. At least in the West. The real horde will come soon after...
People should use tools and not to create robotic tool overlords because they’re to weak to handle life.
At the risk of going full Classics nerd here, I think you can make a solid case that Aristotle gave the best assessment of the power of data analytics avant la lettre in the Posterior Analytics.
The important thing to understand about 'algorithms' is that because they are essentially a form of dialectic or rhetoric--that is, an art of making, not of knowing or acting. Like rhetoric or dialectic, algorithms do not know or act, strictly, speaking--they only make. The only thing that distinguishes algorithmic data 'science' from ancient dialectic or rhetoric is its mathematical precision--but this mathematical precision always starts from a set of presuppositions given to data science from outside--either a prudential supposition or a scientific fact, or sometimes just an opinion.
The problem with algorithms is that to the untrained eye, mathematical precision looks like knowledge and prudence, and the more complex the algorithms become the more their starting-points outside techne are obscured. Aristotle writes in the Posterior Analytics:
“Insofar as one tries to make use of dialectic and [rhetoric], not as what they are, namely, powers, but rather as precise sciences, he will destroy their nature unawares by trying to make use of them as sciences, the subjects of which are things, not just accounts.”
Robocalls are an AI.
Some government employees operate like inhuman robots. When they want too.
Buying stocks is some sort of AI.
Twitter and Facebook have thousands of bots to keep traffic high.
Could go on...
How is any of that good? Or how in its current use is it good? How is this working out for us? All has to do with money and shirking responsibility. Lowest denominator. Something goes wrong you get the guy from “Hogan’s Hero’s” with “I know nothing!”. All machines break, all tools break, nothing lasts forever.
Automate everything people might forget how to live and exist. At least in the West. The real horde will come soon after...
People should use tools and not to create robotic tool overlords because they’re to weak to handle life.
At the risk of going full Classics nerd here, I think you can make a solid case that Aristotle gave the best assessment of the power of data analytics avant la lettre in the Posterior Analytics.
The important thing to understand about 'algorithms' is that because they are essentially a form of dialectic or rhetoric--that is, an art of making, not of knowing or acting. Like rhetoric or dialectic, algorithms do not know or act, strictly, speaking--they only make. The only thing that distinguishes algorithmic data 'science' from ancient dialectic or rhetoric is its mathematical precision--but this mathematical precision always starts from a set of presuppositions given to data science from outside--either a prudential supposition or a scientific fact, or sometimes just an opinion.
The problem with algorithms is that to the untrained eye, mathematical precision looks like knowledge and prudence, and the more complex the algorithms become the more their starting-points outside techne are obscured. Aristotle writes in the Posterior Analytics:
“Insofar as one tries to make use of dialectic and [rhetoric], not as what they are, namely, powers, but rather as precise sciences, he will destroy their nature unawares by trying to make use of them as sciences, the subjects of which are things, not just accounts.”