Chronological feed of everything captured from Andrej Karpathy.
Andrej Karpathy shared a link to his ~2-month-old interview from Data Science Weekly. The interview covers ConvNetJS, his professional background, and perspectives on neural network trends, particularly in academia. It provides early insights into the field's direction as of early 2014.
Karpathy collected 47 days of Hacker News data by scraping front and new stories pages every minute from August 22 to October 30, storing ~10MB per day as gzipped pickles. BeautifulSoup parsed unstructured HTML tables into JSON with fields like domain, title, points, rank, user, and comments, handling edge cases in a 100-line function. Analysis in an IPython Notebook visualizes upvote trajectories, top domains/users/topics, and optimal posting times, with data/code once publicly available.
Chrome extensions require only a manifest file and JavaScript/HTML, deployable in minutes to inject code into any webpage's DOM. They support adding UI elements, persistent storage, and periodic execution, allowing removal of unwanted content, auto-interactions, and novel features like tweet rarity highlighting. Twitter modifications demonstrate hiding promoted tweets, auto-loading new content, and visually prioritizing infrequent posters using scraped user frequencies stored locally.
Interpreting a simple image like Obama pranking a scale requires fusing 3D scene structure, physics, human psychology, social norms, and predictive reasoning about mental states—none of which emerge from pixels alone. State-of-the-art CV benchmarks like ImageNet and PASCAL VOC test narrow, disconnected tasks that ignore this iceberg of prior knowledge. Progress demands not just more image data and learning tricks, but structured experiential data, embodiment, and active inference architectures to approximate human-like comprehension.
Andrej Karpathy manually classified 400 CIFAR-10 test images to 94% accuracy, far surpassing the 2011 state-of-the-art of 80% from Coates et al.'s k-means centroids with whitening and SVM. The exercise reveals high intra-class variability in poses, scales, and partial views, plus dataset undersampling causing test images without close training matches. Random clusters yield 70% and random patches 74%, suggesting k-means mainly spreads activations near data manifolds; post-2015 deep learning pushed accuracy to 95%.