As the TechSparks countdown begins, we list the top Bengaluru startups that have grabbed the spotlight due to the work they do and the growth they are projecting. Bengaluru has always been referred to as India's Silicon Valley. This is not just in terms of the number of startups that have mushroomed in the city, but also keeping in mind the funding pumped into the Bengaluru startup ecosystem. According to YourStory data, between 2016 and 2017 (YTD), the total amount of funding raised by Bengaluru startups was a whopping $6.6 billion. As many as 460 startups have made Bengaluru their home in the past two years, be it e-commerce giant Flipkart, cab aggregator Ola or Practo, one of the top funded healthcare startups in India.

Artificial intelligence (AI) promises to revolutionise our lives, drive our cars, diagnose our health problems, and lead us into a new future where thinking machines do things that we're yet to imagine. Even billionaire entrepreneur Elon Musk, who admits he has access to some of the most cutting-edge AI, said recently that without some regulation "AI is a fundamental risk to the existence of human civilization". So what is the future of AI? Michael Milford and Peter Stratton are both heavily involved in AI research and they have different views on how it will impact on our lives in the future. How widespread is artificial intelligence today? Answering this question depends on what you consider to be "artificial intelligence".

In the last one year, many of the global customers of connectivity and authentication provider, LogMeIn often enquired about automation solutions to improve personalization for customer service interactions. William Wagner, CEO, LogMeIn decided to act upon it. They acquired Israel based Nanorep which creates chat bots and AI tools for customers. And India, Wagner believes is going to be one of their major markets for AI based solution." We have 500 customers in India like Tech Mahindra and Cognizant and with Nanorep, India will be one of our biggest markets."

Given no one really understands the enormity of the strides in artificial intelligence/tech, the doomsday predictions of what it will do for jobs could either be quite correct, or totally wrong. And while the progress is truly impressive, it is not clear if this is good enough to destroy the traditional jobs that humans do, nor is it clear what sectors tech will take over, or in what order. For now, what is clear, however, is that countries like India, so far from the productivity frontier, stand to benefit a lot more from tech--not only can this help contribute to dramatically increasing productivity, it can help overcome critical shortages in areas like healthcare and education. An article in Wired that discusses an "intelligent" first-aid kit developed by Mobilize Rescue Systems gives a hint as to how this could pan out. The emergency kit, that comes equipped with the usual suspects like gauze, bandages, ointments, etc, even has tourniquets, chest seals and clotting agents--the real game-changer, though, is the iPad embedded in its lid.

For many Russian students, the academic year started last Friday with tips on planetary domination from President Vladimir Putin. "Artificial intelligence is the future, not only for Russia but for all humankind," he said, via live video beamed to 16,000 selected schools. Putin's advice is the latest sign of an intensifying race among Russia, China, and the US to accumulate military power based on artificial intelligence. All three countries have proclaimed intelligent machines as vital to the future of their national security. Technologies such as software that can sift intelligence material or autonomous drones and ground vehicles are seen as ways to magnify the power of human soldiers.

It has become a cliche to declare that the future is full of both "great promise and great peril."

This paper aims to address two issues existing in the current speech enhancement methods: 1) the difficulty of phase estimations; 2) a single objective function cannot consider multiple metrics simultaneously. To solve the first problem, we propose a novel convolutional neural network (CNN) model for complex spectrogram enhancement, namely estimating clean real and imaginary (RI) spectrograms from noisy ones. The reconstructed RI spectrograms are directly used to synthesize enhanced speech waveforms. In addition, since log-power spectrogram (LPS) can be represented as a function of RI spectrograms, its reconstruction is also considered as another target. Thus a unified objective function, which combines these two targets (reconstruction of RI spectrograms and LPS), is equivalent to simultaneously optimizing two commonly used objective metrics: segmental signal-to-noise ratio (SSNR) and logspectral distortion (LSD). Therefore, the learning process is called multi-metrics learning (MML). Experimental results confirm the effectiveness of the proposed CNN with RI spectrograms and MML in terms of improved standardized evaluation metrics on a speech enhancement task.

Residual Network (ResNet) is the state-of-the-art architecture that realizes successful training of really deep neural network. It is also known that good weight initialization of neural network avoids problem of vanishing/exploding gradients. In this paper, simplified models of ResNets are analyzed. We argue that goodness of ResNet is correlated with the fact that ResNets are relatively insensitive to choice of initial weights. We also demonstrate how batch normalization improves backpropagation of deep ResNets without tuning initial values of weights.