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Using Machine Learning to Win at Rock-Paper-Scissors with TensorFlow

Using machine learning to win at rock-paper-scissors with @TensorFlow:

  • Using Machine Learning to Win at Rock-Paper-Scissors with TensorFlowWe often see machine learning being used to solve complicated problems, particular those involving large data sets.
  • But, if you’re a kid interested in experimenting with machine learning, that’s probably pretty boring.
  • (📷: Kaz Sato / Google)So when Googler Kaz Sato and his son were looking for a project to build using machine learning, they settled on something a little more familiar to children: rock-paper-scissors.
  • Because there are only three possible moves in the game, the easiest solution would be to just program those manually.But, Sato and son were wanting to take advantage of machine learning.
  • Instead of programming the moves, they fed the flex sensor data into Google’s TensorFlow.

We often see machine learning being used to solve complicated problems, particular those involving large data sets. Machine learning excels at finding the patterns that connect large numbers of…

Using Machine Learning to Win at Rock-Paper-Scissors with TensorFlowWe often see machine learning being used to solve complicated problems, particular those involving large data sets. Machine learning excels at finding the patterns that connect large numbers of inputs to large numbers of outputs. But, if you’re a kid interested in experimenting with machine learning, that’s probably pretty boring. Not many kids are interested in finding the correlations between Tweets and local temperatures, for example.TensorFlow lends a hand to build a rock-paper-scissors machine. (📷: Kaz Sato / Google)So when Googler Kaz Sato and his son were looking for a project to build using machine learning, they settled on something a little more familiar to children: rock-paper-scissors. Their creation uses a glove outfitted with flex sensors that is connected to an Arduino to detect the player’s finger positions. Because there are only three possible moves in the game, the easiest solution would be to just program those manually.But, Sato and son were wanting to take advantage of machine learning. Instead of programming the moves, they fed the flex sensor data into Google’s TensorFlow. Eventually, TensorFlow was able to determine what hand gesture was most likely being given by the player, without them ever programming those instructions specifically. If the game were to be expanded to include more moves, TensorFlow could learn those too.Graph showing the probability distribution of sensor data going into TensorFlow.With TensorFlow able to determine what move the player is playing, it is a simple matter for it to know which…

Using Machine Learning to Win at Rock-Paper-Scissors with TensorFlow

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