Ai for Business

  Image by Rajashree Rajadhyax Language models are a buzzword these days. The current state-of-the-art in language modelling is the transformer based GPT-3, which was trained on a staggering amount of text data. Here’s a quick look at what exactly is a language model. Researchers believe that language began somewhere between 50,000 and 1,00,000 years ago. Language evolved from the human need to communicate with each other. The ability to communicate using language has given the human species a better chance at survival. Language is an incredibly important tool to pass on knowledge and to communicate thought. To acquire knowledge we read or simply listen to others and all of this is possible because of language. As my friend puts it, the human species has always remained a step ahead because of their ability to augment their capabilities. I’m adding a link to his article  here . AI is one more such attempt; the attempt to augment intelligence. Recent developments in AI have int...

Usually, it is the false negatives that are associated with risk to life. Consider the case of a medical test for a serious disease. You don’t want the test to miss the disease in a patient. The test might flag the disease even when it is not there. This is bad but not so disastrous as missing it. In other words, false negatives (missing the disease) are life threatening, but false positives (predicting disease that is not there) are not. This is generally true, even in applications that are not life threatening but are still damaging. Take the example of  fraud detection. False positives (signaling fraud by mistake) is alright but false negatives (missing a fraud) is not. The major problem with false positives is their nuisance. An interesting case is that of the fall detection device for elderly people. This device sends emergency messages to close relatives when a senior person falls down. Sometimes, it signals a fall by mistake. The son or daughter leaves the office in a worri...

  Buddha vs Child Paradigm “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. 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 accounts in highly accurate manner. This system is like a Buddha, who is enlightened from the start. We can’t expect it to do any mistakes (exc...

Understanding Generative Adversarial Networks - Part II  I n   "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...