What is Artificial Intelligence (AI)?

Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial. Recent trends in the arena of Machine Learning have paved the way for advancements in Artificial Intelligence, and with the advent of Context and Contextual Applications for various platforms, the field of Artificial Intelligence just keeps getting better and better Definition of Artificial Intelligence Artificial Intelligence (AI) is fundamentally transforming the way businesses operate. It is a combination of cutting-edge technologies that enables machines to think, comprehend, and execute tasks hitherto carried out by humans. AI can be considered as an intelligent robot which possesses the cognitive characteristics of a human being. “Artificial intelligence is part of computer science concerned with designing intelligent computer systems that is systems that exhibit the characteristics we associate with intelligence in human behavior”. Communication has been one of the important aspects of intelligent behavior where vision and speech are all used effectively for communication purposes. Speech recognition, computer vision and language understanding have been some of the goals of artificial intelligence, because the possession of…

What is Recurrent Neural Network (RNN)
Neural Network , Technology & Science / February 20, 2018

Neural networks are powerful learning models that achieve state-of-the-art results in a wide range of supervised and unsupervised machine learning tasks. They are suited especially well for machine perception tasks, where the raw underlying features are not individually interpretable. The use of recurrent neural networks are often related to deep learning and the use of sequences to evolve models that simulate the neural activity in the human brain. Overview of Recurrent Neural Network (RNN) The fundamental feature of a Recurrent Neural Network (RNN) is that the network contains at least one feed-back connection, so the activations can flow round in a loop. That enables the networks to do temporal processing and learn sequences, e.g., perform sequence recognition/reproduction or temporal association/prediction. Recurrent Neural Networks (RNNs) are connectionist models with the ability to selectively pass information across sequence steps, while processing sequential data one element at a time. Thus they can model input and/or output consisting of sequences of elements that are not independent. Further, recurrent neural networks can simultaneously model sequential and time dependencies on multiple scales. Figure 1: Recurrent Neural Network In other words, the RNN will be a function with inputs​\( x_t \)​  (input vector) and previous state ​\( h_(t-1)…

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