Credit: OpenAI Microscope, so named because it puts neural networks under a metaphorical microscope to expose their inner workings, is a visual method for showing what a neural network’s expansive system of layers and nodes looks like. According to OpenAI’s Microscope website: Researchers can use these accurate models to compare differences in neural networks, much like they would compare cells under an actual microscope. Without such visualizations, developers are forced to compare raw output data – something that’s not always useful. Read: MIT scientists can ‘hack’ your dreams with sounds and smells The project currently has images for eight of the most popular computer vision AI systems ever created, including AlexNET, Inception, and ResNet. Each neural network is represented by a menagerie of images that would challenge a kaleidoscope for sheer exuberance of color and beauty. Something about the neural network trained on AlexNet (Places) makes me think of Paris: Credit: OpenAI And ResNet looks like an aquarium made of stained-glass windows: Credit: OpenAI According to OpenAI, the researchers focused on these particular models because they’re representative of the field: Hopefully OpenAI’s work will help researchers understand why certain neural networks act one way, and those designed with different algorithms act another. Currently, many of these systems operate in a “black box,” meaning it’s unclear why they make the decisions they do. These visualizations should help researchers figure out how to back-trace neural network decisions, sort of like an inside-out map. Even the models we’ve included result in hundreds of thousands of neurons and many model idiosyncrasies. Making everything work smoothly turns out to be more difficult than one would expect! You can check out the rest of the images and learn more about Microscope here.
