Unravelling the mysteries of Artificial Intelligence

The day AI reads a document and answers each and every question asked and do reasoning on it, will be the day when we will call it true intelligence. Welcome to the world of Information Extraction, where algorithms try to extract information from unstructured documents into structured information, which the AI can further access to answer questions. Apparently easy for humans perform such an important task, looks hard for AI to do. The difficulty lies in recognizing named entities, identifying context, relationship extraction, understanding tables and diagrams, and many more. The research in Information Extraction has progressed exponentially since this problem was identified, and today we have lot of open source tools at our disposal. Any toolkit for Information Extraction is expected to contain the following modules Tokenizer - Converts a sequence of characters into a sequence of tokens Gazetteers - Entity dictionaries used as a lookup table Sentence splitter - Under

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe applicati

Widely used in image recognition, Convolutional Neural Networks (CNNs) consist of multiple layers of neuron collection which look at small window of the input image, called receptive fields. The history of Convolutional Neural Networks begins with a famous experiment “Receptive Fields of Single Neurons in the Cat’s Striate Cortex” conducted by Hubel and Wiesel. The experiment confirmed the long belief of neurobiologists and psychologists that the neurons in the brain act as feature detectors. The first neural network model that drew inspiration from the hierarchy model of the visual nervous system proposed by Hubel and Wiesel was Neocognitron invented by Kunihiko Fukushima, and had the ability of performing unsupervised learning. Kunihiko Fukushima’s approach was commendable as it was the first neural network model having the capability of pattern recognition similar to human brain. The model gave a lot of insight and helped future understanding of the brain. A successful ad