According to Chris Olah (@ch402), a simple transcoder can mimic the absolute value function by using two features per dimension, as illustrated in his recent tweet. This approach highlights how AI models can be structured to represent mathematical functions efficiently, which has implications for AI interpretability and neural network design (source: Chris Olah, Twitter). Understanding such feature-based representations can enable businesses to develop more transparent and reliable AI systems, especially for domains requiring explainable AI and precision in mathematical operations.