Although not the first video game ever produced, Pong was the first to achieve commercial success and has had a tremendous influence on our culture as a whole. In Pong’s time, its popularity ushered in the arcade era that would last for more than two decades. Today, it retains a similar popularity partially for approachability: gameplay is relatively simple, has hardwired logic, and provides insights about the state of computer science at the time. For these reasons, [Nick Bild] has decided to recreate this arcade classic, but not in a traditional way. He’s trained a neural network to become the game instead.
To train this neural network, [Nick] used hundreds of thousands of images of gameplay. Much of it was real, but he had to generate synthetic data for rare events like paddle misses. The system is a transformer-based network with separate branches for predicting the movements of the ball, taking user input, and predicting paddle motion. A final branch is used to integrate all of these processes. To play the game, the network receives four initial frames and predicts everything from there.
From the short video linked below, the game appears to behave indistinguishably from a traditionally coded game. Even more impressive is that, due to [Nick]’s lack of a GPU, the neural network itself was trained using only a pair of old Xeon processors. He’s pretty familiar with functionally useful AI as well. He recently built a project that uses generative AI running on an 80s-era Commodore to generate images in a similar way to modern versions, just with slightly fewer pixels.
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This articles is written by : Nermeen Nabil Khear Abdelmalak
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