From Neurons to Networks

In order to build flying machines, we don’t build airplanes that flap their wings, or that are made of bones, muscle, and feather.Likewise, in artificial neural networks, the internal mechanism of the neurons is usually ignored, and artificial neurons are often much simpler than their natural counterparts. If the goal is to build AI systems […]

Generative + Discriminative = GAN

Generative Adversarial Networks are a machine learning model family that can learn to “hallucinate” new examples of complex data. GANs may be used for generating images, for instance—so-called “deepfake” images use GANs. A GAN’s Training Process The GAN has two parts: a Generator, and a Discriminator. The Generator learns to produce increasingly realistic pictures by […]

Robotics and Machine Learning

Robotics means building and programming robots so that they can operate in complex real-world scenarios. In a way, robotics is the ultimate challenge of AI since it requires a combination of virtually all areas of AI: Computer vision and speech recognition for sensing the environment, Natural language processing, information retrieval, and reasoning under uncertainty for […]