Unravelling the mysteries of Artificial Intelligence

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 wa...

The Sense of Vision is taken for granted by us in our day to day life, but only a visually impaired person can understand the true value and necessity of Vision. But soon AI based computer vision systems can help the blind and visually impaired to navigate. Tech giants like Google, Baidu, Facebook, Microsoft are working on a range of products that apply Deep Learning for the Visually Impaired. One of them being Image Captioning technology wherein the system describes the content of an image.  To accelerate further research and to boost the possible applications of this technology, Google made the latest version of their Image Captioning System available as an open source model in Tensorflow. It’s called “Show And Tell: A Neural Image Caption Generator”. The project can be found at https://github.com/tensorflow/models/tree/master/im2txt and the full paper can be found at https://arxiv.org/abs/1609.06647 The Show and Tell model is an example of an encoder-decoder neural ...

What does it mean for Deep Learning to recently beat Go champion Lee Sedol? Or what did it mean back in 1997 for Deep Blue to beat chess champion Garry Kasparov? Is the purpose of AI to only demonstrate that it can win against humans, or is it much more than winning? Such wins demonstrate the capabilities of AI, and open up new avenues for the tools and techniques used. In the case of Deep Blue developed by IBM, it was better search and evaluation algorithms, combined with a supercomputer to defeat a world champion. Similar AI algorithms were then applied to other applications including search engines. AI community continued its fascination of winning in games involving intelligence, with IBM Watson turning out to be a winner of quiz show Jeopardy. Watson even received the first place prize of $1 million. The AI techniques such as Natural Language Processing and Machine Learning that Watson used to win the competition are today driving the Watson Cloud Platform to understan...

“There is no scientific study more vital to man than the study of his own brain. Our entire view of the universe depends on it.” ― Francis Crick Source: https://en.wikipedia.org/wiki/File:Francis_Crick_crop.jpg After unraveling the mysteries of DNA, the secret to life, Francis Crick for the rest of his life turned his attention to solve the mysteries of brain and consciousness. He was certain that the answer to intelligence lies deep in the structure of brain. In his book, The Astonishing Hypothesis: The Scientific Search for the Soul, Francis Crick theorizes a framework for studying consciousness. His work turned out to be an inspiration for many AI researchers, as it became evident that deciphering brain might lead to creating general intelligence. Today the research from neuroscience is used by AI researchers to create intelligent algorithms, that are different than traditional symbolic based systems. We at Cere Labs try to draw inspiration from biology and other ...