Law in the Internet Society

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JustinFlaumenhaftSecondEssay 8 - 07 Jan 2021 - Main.JustinFlaumenhaft
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  If symbolic AI relied upon the cleverness of its programmers, machine learning relies equally upon the quantity of its training data: machine learning models require vast volumes of training data to work well. For example, Google’s AlphaGo? , which famously won four out of five go matches against the reigning go world champion, was trained on “30 million board positions from 160,000 real-life games taken from a go database”[4]. While mastery of go is a notable achievement, it provides scant evidence of human-like general intelligence. A machine learning algorithm can glean patterns from large datasets to accomplish narrowly defined tasks, but it does not have anything like a mind of their own. AlphaGo? cannot even be said to know that it is playing go.
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<		 However, companies like Facebook and Google, searching for more profitable business models, looked to machine learning algorithms to turn the vast stores of data they collect into valuable commodities to be sold to advertisers. Thus, the quest to build computers that emulated human thought ended not with intelligent computers, but with computers that preyed on human intelligence by monitoring and influencing it.
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>		 However, companies like Facebook and Google, searching for more profitable business models, looked to machine learning algorithms to turn their vast stores of data into valuable commodities to be sold to advertisers. Thus, the quest to build computers that emulated human thought ended not with intelligent computers, but with computers that preyed on human intelligence by monitoring and influencing it.
 [1] http://raysolomonoff.com/dartmouth/boxa/dart564props.pdf

Revision 8r8 - 07 Jan 2021 - 23:56:49 - JustinFlaumenhaft
Revision 7r7 - 07 Jan 2021 - 20:21:46 - JustinFlaumenhaft
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