Name: AI can help explain complex topics easily by throwing together a simulation. As Eric says later in the thread, a newer paper argues that this pattern is actually collider bias (the authors disagree). What is collider bias? Gemini one-shots an explanation:
Uploaded: 2025-12-21T19:15:39.000Z
Duration: PT17.414S
Channel: Ethan Mollick
Description: Ethan Mollick shorts video about AI can help explain complex topics easily by throwing together a simulation. As Eric says later in the thread, a newer paper argues that this pattern is actually collider bias (the authors disagree). What is collider bias? Gemini one-shots an explanation:

AI can help explain complex topics easily by throwing... show more

Ethan Mollick

327,525 次观看 • 10 个月前

Real-time water force field simulation in Unreal 5.8, now... show more

Jesse Pitela

48,116 次观看 • 4 个月前

MLP in PyTorch by hand ✍️ ~ 7 steps... walkthrough below Goal: fill in every blank in the PyTorch code to build a multi-layer perceptron. 1. Given Let us start with a code template on the left and the network it is supposed to build on the right. Every blank in the code can be worked out from the picture. 2. Linear layer We count: 3 features in, 4 features out. So the weight matrix is 4 by 3. There is an extra column for the biases, which means bias = T. 3. ReLU Let us apply the activation. ReLU crosses out the negatives, so -1 becomes 0. 4. Linear layer The input size is 4, because that is what the previous layer put out. The output size is 2. A 2 by 4 weight matrix, and this time no extra column, so bias = F. 5. ReLU We cross out the negatives again. 6. Linear layer Two features in, five out. A 5 by 2 weight matrix, with a bias column, so bias = T. 7. Sigmoid Let us finish. Sigmoid squashes the raw scores (3, 0, -2, 5, -5) into probabilities between 0 and 1. You have just implemented a three-layer deep neural network by hand. ✍️ == Story == Three years ago I gave this exercise to my students, to connect the code to the math. They found it odd. Every other AI course they were taking lived inside a Jupyter notebook, and here I was handing out paper. Three years later, my colleagues are the ones rushing to move their materials to paper. The exercise has not changed. Paper still asks the one thing a notebook lets you skip: do you actually understand what the code is doing? If you can tell me why the weight matrix is 4 by 3, and why bias is F on the second layer, you understand nn.Linear better than someone who has been copy-pasting it for a year. 💾 Save this post! #AIbyHand #PyTorch #DeepLearningshow more