“Artificial Intelligence (AI) is like a child, who needs time to learn and adapt, whereas a typical IT system is like Buddha who knows everything about the problem it was supposed to solve.” - Devesh Rajadhyax, Founder, Cere Labs. Images: By Purshi - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=9827197 By Shaun MItchem - Diggy starts to learn to walk, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=4762045 Let us in this post try to elaborate on this Buddha vs Child Paradigm which Devesh coined for differentiating between conventional IT systems and AI. It is essential to know the difference because it helps in building the right kind of attitude towards implementing AI systems. A typical IT system such as ERP does the job for what it was implemented. It is assumed that it will solve the problem for what it was made. Take for example an accounting system like Tally. It will help you to manage your a
In "Understanding Generative Adversarial Networks - Part I" you gained a conceptual understanding of how GAN works. In this post let us get a mathematical understanding of GANs. The loss functions can be designed most easily using the idea of zero-sum games. The sum of the costs of all players is 0. This is the Minimax algorithm for GANs Let’s break it down. Some terminology: V(D, G) : The value function for a minimax game E(X) : Expectation of a random variable X, also equal to its average value D(x) : The discriminator output for an input x from real data, represents probability G(z): The generator's output when its given z from the noise distribution D(G(z)) : Combining the above, this represents the output of the discriminator when given a generated image G(z) as input Now, as explained above, the discriminator is the maximizer and hence it tries to maximize V(D, G) . The discriminator wants to correctly label an imag