In 1770, a wonderful machine was revealed. The Turk (also known as Automaton Chess Player) was a chess-playing machine that beat various famous opponents.
If you've ever heard of "Amazon Mechanical Turks," this machine is where they got their name. Amazon Mechanical Turk is a marketplace where people who are willing to do "Human Intelligence Tasks" choose from available tasks which companies are willing to pay to have done. These tasks are generally things computers are not great at -- classifying a sentence as funny or not for instance -- but that humans can do easily. As you probably guessed by now, the original Turk was not actually a machine, but in fact had a chess master hide inside it. I highly recommend reading the wikipedia link, how elaborate the hoax was made my day.
So how does this relate to decision making? First off, you might use the data directly to make decisions. However, while computers may not be good at determining if a sentence is funny, you can train them on data to predict whether a sentence is funny. And it turns out you can use Turkers to generate that training data! Kartik Hosanagar used Natural Language Processing algorithms in conjunction with Turker-generated data on various corporate Facebook posts to attempt to infer what drives consumer engagement (the abstract is available here). This seems like a really cool technique which can be used to inform a whole lot of decision making in the future.
If you've ever heard of "Amazon Mechanical Turks," this machine is where they got their name. Amazon Mechanical Turk is a marketplace where people who are willing to do "Human Intelligence Tasks" choose from available tasks which companies are willing to pay to have done. These tasks are generally things computers are not great at -- classifying a sentence as funny or not for instance -- but that humans can do easily. As you probably guessed by now, the original Turk was not actually a machine, but in fact had a chess master hide inside it. I highly recommend reading the wikipedia link, how elaborate the hoax was made my day.
So how does this relate to decision making? First off, you might use the data directly to make decisions. However, while computers may not be good at determining if a sentence is funny, you can train them on data to predict whether a sentence is funny. And it turns out you can use Turkers to generate that training data! Kartik Hosanagar used Natural Language Processing algorithms in conjunction with Turker-generated data on various corporate Facebook posts to attempt to infer what drives consumer engagement (the abstract is available here). This seems like a really cool technique which can be used to inform a whole lot of decision making in the future.
No comments:
Post a Comment