With the big data growing bigger and bigger and social media penetrating every facet of the society, construing and monitoring data is one of the biggest challenges faced by the enterprises. Gone are those days when customers have to lodge a formal complaint to register the malfunctioning of any product/services provided by the business enterprise, rather, users these days take it to the social media forum to express their dissatisfaction and anguish towards any improper services/products. Inputs such as tweets, facebook comments could be of significant value to the enterprise to analyze their products/services/ performances, customer behavior and demands. Below is a small case study on Flipkart and Snapdeal performance when'World Book Day' was trending on Twitter. Below is the screenshot of'Flipkart' and'Snapdeal' on the occasion of'World Book Day'.

Inside Science Minds presents an ongoing series of guest columnists and personal perspectives presented by scientists, engineers, mathematicians, and others in the science community showcasing some of the most interesting ideas in science today. In fact, upon reflection, it was amazing how often the word "algorithm" came up in the course of our conversations with these accomplished scientists. The boom in software and computing has achieved powerful and profound results in our society. And, yes, the world is a better place, thanks to data analytics. But we need to slow down and regain our perspective, because Big Data and machine learning are absolutely not ends unto themselves, and they certainly aren't a replacement for basic scientific research and exploration.

Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory. Short-term memory of RNNs has been previously studied analytically only for the case of orthogonal networks, and only under annealed approximation, and uncorrelated input. Here for the first time, we present an exact solution to the memory capacity and the task-solving performance as a function of the structure of a given network instance, enabling direct determination of the function–structure relation in RNNs. We calculate the memory capacity for arbitrary networks with exponentially correlated input and further related it to the performance of the system on signal processing tasks in a supervised learning setup. We compute the expected error and the worst-case error bound as a function of the spectra of the network and the correlation structure of its inputs and outputs.

Here's what it seems like when artificial intelligence learns to enjoy "Ode To Joy" in the style of EDM, Brazilian guitar, and The Beatles' "Penny Lane". The Sony Laptop or computer Science Laboratory in Paris was challenged to reorchestrate the concept music of the European Union. Employing the max entropy technique of equipment studying, they taught a laptop how to acknowledge the core features of various types of audio. Sony's CSL believes their system could be a stepping stone to creating AI that can compose authentic melodies we discover catchy and memorable. If you think the radio seems like audio manufactured by computer systems, just wait a couple decades for cyberBach.

Google's DeepMind artificial intelligence (AI) research group today announced that for all future research it will use TensorFlow, a machine learning library that Google open-sourced last year, instead of Torch, an older framework. The move suggests that some of Google's brightest AI minds are convinced of the promise of Google's own open source software; TensorFlow is now good enough for DeepMind. "We believe that TensorFlow will enable us to execute our ambitious research goals at much larger scale and an even faster pace, providing us with a unique opportunity to further accelerate our research programme," Koray Kavukcuoglu, a research scientist at Google DeepMind and one of Torch's core contributors, wrote in a blog post. This is important because of DeepMind's considerable capabilities -- earlier this year its AlphaGo AI player of the ancient Chinese board game Go beat top-ranked Go player Lee Sedol. To be sure, DeepMind is not Google's only AI research unit.

Taking a break from the tradition where Google founders Larry Page and Sergey Brin shared the company's progress and vision every year, this time it was Indian-origin CEO Sundar Pichai who updated the world with some of Google's achievements and key highlights. In a letter posted on official Google blog on Friday, Pichai reiterated "to organise the world's information and make it universally accessible and useful". Touching upon artificial intelligence (AI), powerful computing platforms and cloud, he stressed that mobile phone has become the remote control for daily lives and people are communicating, consuming, educating and entertaining themselves on smartphones "in ways unimaginable just a few years ago". "Search -- the very core of Google, comes from mobile and an increasing number of them via voice. The company made this easy and via Google Now, user can get information like the weather in your upcoming vacation spot," he posted.

Taking a break from the tradition where Google founders Larry Page and Sergey Brin shared the company's progress and vision every year, this time it was Indian-origin CEO Sundar Pichai who updated the world with some of Google's achievements and key highlights. In a letter posted on official Google blog on Friday, Pichai reiterated "to organize the world's information and make it universally accessible and useful". Touching upon artificial intelligence (AI), powerful computing platforms and cloud, he stressed that mobile phone has become the remote control for daily lives and people are communicating, consuming, educating and entertaining themselves on smartphones "in ways unimaginable just a few years ago". "Search -- the very core of Google, comes from mobile and an increasing number of them via voice. The company made this easy and via Google Now, user can get information like the weather in your upcoming vacation spot," he posted.

When it comes to the potential impact AI could bring, mass-unemployment is probably a more realistic concern for us than, say, the Skynet (the murderous AI system of the Terminator film franchise), says Martin Ford, a technology entrepreneur and author of two books about how tomorrow's technology might give a fatal blow to the social structure that we thrive on today. If we look far enough into the future, Ford says, few jobs would be safe from being automated, as algorithms with deep learning capabilities would take over not only entry-level jobs, but also those requiring years of training and experience. "In terms of jobs [that may be done by AI]… the important word there is'predictable'," Ford says. "If another smart person could study a record of everything you've done in the past in your job and based on that, learn how to do your job, then someday, maybe a machine might be able to do the same thing." Ford's warning of a jobless future is not entirely new; and as always, the idea is controversial because opponents argue that historically, workers have survived rounds of technological revolution and they always managed to find other jobs in newly emerged industries.

Here's what it sounds like when artificial intelligence learns to play "Ode To Joy" in the style of EDM, Brazilian guitar, and The Beatles' "Penny Lane". The Sony Computer Science Laboratory in Paris was challenged to reorchestrate the theme song of the European Union. Using the max entropy approach of machine learning, they taught a computer how to recognize the core features of different types of music. Sony's CSL believes their program could be a stepping stone to making AI that can compose original melodies we find catchy and memorable. If you think the radio sounds like music made by computers, just wait a few years for cyberBach.

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