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Conway game of life question11/27/2023 “For many problems, you don’t have a lot of choice in dataset you get the data that you can collect, so if there is a problem with your dataset, you may have trouble training the neural network,” Springer says. As for the training dataset, in many cases, it isn’t clear which samples are the right ones, and in others, there’s not much of a choice. The most common practice is to pick random values from a normal distribution, therefore settling on the right initial weights becomes a game of luck. Unfortunately, you never know what the initial weights of the neural network should be. ![]() The result of training the neural network was largely affected by the chosen set training examples as well as the initial parameters. This would imply that the AI model had managed to parameterize the rules underlying the Game of Life.īut in most cases the trained neural network did not find the optimal solution, and the performance of the network decreased even further as the number of steps increased. The only way the neural network could reach 100 percent accuracy would be to converge on the hand-crafted parameter values. They initialized the parameters to random values and trained the neural network on 1 million randomly generated examples of the Game of Life. Then, they tried to see if the same neural network could reach optimal settings when trained from scratch. Jacob Springer, computer science student at Swarthmore College This proved that there’s a minimal neural network that can represent the rule of the Game of Life. In their work, the researchers first created a small convolutional neural network and manually tuned its parameters to be able to predict the sequence of changes in the Game of Life’s grid cells. If the network fails even once, then it is has not correctly learned the rules,” Springer says. ![]() It is also very easy to adjust the flexibility of the problem in the Game of Life by modifying the number of timesteps in the future the target deep learning model must predict.Īlso, unlike domains such as computer vision or natural language processing, if a neural network has learned the rules of the Game of Life it will reach 100 percent accuracy. “We can write down by hand a neural network that implements the Game of Life, and therefore we can compare the learned solutions to our hand-crafted one. “We already know a solution,” Jacob Springer, a computer science student at Swarthmore College and co-author of the paper, told TechTalks. There are a few reasons the Game of Life is an interesting experiment for neural networks. With neural networks being very good prediction machines, the researchers wanted to find out whether deep learning models could learn the underlying rules of the Game of Life.Īrtificial neural networks vs the Game of Life Interestingly, no matter how complex a grid becomes, you can predict the state of each cell in the next timestep with the same rules. You can also use the Game of Life to create very complex pattern, such as this one.
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