Python script
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Blocking AI Filler with Shannon Entropy
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Frustrated with AI models' tendency to include unnecessary apologies and filler phrases, a developer created a Python script to filter out such content using Shannon Entropy. By measuring the "smoothness" of text, the script identifies low-entropy outputs, which often contain unwanted polite language, and blocks them before they reach data pipelines. This approach effectively forces AI models to deliver more direct and concise responses, enhancing the efficiency of automated systems. The open-source implementation is available for others to use and adapt. This matters because it improves the quality and relevance of AI-generated content in professional applications.
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AI Learns to Play ‘The House of the Dead’
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A neural-network-based AI was developed to autonomously play the classic arcade game "The House of the Dead" by learning from recorded gameplay. A Python script captured the frames and mouse movements during gameplay, which were then stored in a CSV file for training purposes. To efficiently process the large volume of frames, a convolutional neural network (CNN) was employed. The CNN applied convolutional operations to the frames, which were then fed into a feedforward neural network, enabling the AI to mimic and eventually play the game independently. This matters because it demonstrates the potential of neural networks to learn and replicate complex tasks through observation and data analysis.