Derek Schloss

“While humans lack AI’s ability to analyze huge numbers of data points at the same time, people have a unique ability to draw on experience, abstract concepts, and common sense to make decisions. By contrast, in order for deep learning to function well, the following are required: massive amounts of relevant data, a narrow domain, and a concrete objective function to optimize. If you’re short on any one of these, things may fall apart.”
― Kai-Fu Lee, AI 2041: Ten Visions for Our Future

“Deepfakes are built on a technology called generative adversarial networks (GAN). As the name suggests, a GAN is a pair of “adversarial” deep learning neural networks. The first network, the forger network, tries to generate something that looks real, let’s say a synthesized picture of a dog, based on millions of pictures of dogs. The other network, the detective network, compares the forger’s synthesized dog picture with genuine dog pictures, and determines if the forger’s output is real or fake.”
― Kai-Fu Lee, AI 2041: Ten Visions for Our Future

“Many people assume AI is “programmed” or “taught” by humans with specific rules and actions, like “cats have pointy ears and whiskers.” But deep learning actually works better without these external human rules. Instead of being nudged by humans, many examples of a given phenomenon are fed into the input layer of a deep learning system, along with the “correct answer” at the output layer. In this way, the network in between the input and output can be “trained” to maximize the chance of getting the correct answer to a given input.”
― Kai-Fu Lee, AI 2041: Ten Visions for Our Future

“Computer vision (CV) is the subbranch of AI that focuses on the problem of teaching computers to see. The word “see” here does not mean just the act of acquiring a video or image, but also making sense of what a computer sees. Computer vision includes the following capabilities in increasing complexity: Image capturing and processing—use cameras and other sensors to capture real-world 3D scenes in a video. Each video is composed of a sequence of images, and each image is a two-dimensional array of numbers representing the color, where each number is a “pixel.” Object detection and image segmentation—divide the image into prominent regions and find where the objects are. Object recognition—recognizes the object (for example, a dog), and also understands the details (German Shepherd, dark brown, and so on). Object tracking—follows moving objects in consecutive images or video. Gesture and movement recognition—recognize movements, like a dance move in an Xbox game. Scene understanding—understands a full scene, including subtle relationships, like a hungry dog looking at a bone.”
― Kai-Fu Lee, AI 2041: Ten Visions for Our Future