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 appli...

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...

The landscape of open source AI is big. To identify suitable open source tools to make your AI dream product is a herculean task. Selecting an AI toolkit for your product might turn out costly when you need to scale your software, thus it turns out to be a strategic decision. We at CereLabs have developed a criteria to choose Open Source AI Toolkit. Vision/ Reason for open source If you need to  trust an open source platform, you need to start with the vision statement with which the open source AI platform is launched. The  vision statement portrays the commitment of the company or community towards the toolkit.     Following are the visions of few of the reputed AI Open Source Platforms:       OpenCog : “OpenCog is a unique and ambitious open-source software project. Our aim is to create an open source framework for Artificial General Intelligence , intended to one day express general intelligence at the human level ...

With the advent of AI in almost every industry, right from self driving cars to robot nurses, there is a general concern as to how AI might impact humanity. Although AI offers a lot including medical industry and space exploration, it is slowly making a pathway into every technology. The presence of AI can be felt on every device including mobile phones. There is a general belief that AI is a threat to humanity. The reach of AI in every aspect of our life is inevitable. So how do we make sure that it benefits humanity as a whole? Elon Musk along with other visionaries have come together to take up the baton to help the AI community to work towards a common goal – to make AI benefit humanity. OpenAI is planning to establish itself as a leading non-profit research institution. To make its research accessible to all, OpenAI will collaborate with other institutions and researchers to make their research open source. To know more about OpenAI follow their official website whic...

XOR is considered as the 'Hello World' of Neural Networks. It seems like the best problem to try your first TensorFlow program. Tensorflow makes it easy to build a neural network with few tweaks. All you have to do is make a graph and you have a neural network that learns the XOR function. Why XOR? Well, XOR is the reason why backpropogation was invented in the first place. A single layer perceptron although quite successful in learning the AND and OR functions, can't learn XOR (Table 1) as it is just a linear classifier, and XOR is a linearly inseparable pattern (Figure 1). Thus the single layer perceptron goes into a panic mode while learning XOR – it can't just do that.  Deep Propogation algorithm comes for the rescue. It learns an XOR by adding two lines L1 and L2 (Figure 2). This post assumes you know how the backpropogation algorithm works. Following are the steps to implement the ne...