Here at Robohub, we're celebrating by sharing our list of 20 robot related books (in no particular order) aimed at engaging kids and/or teens with everything robotics. Ranging from funny fictional narratives to DIY drone building and coding, these books are sure to stoke the imaginations of budding innovators and creators. If you can think of any other books that deserve inclusion, we'd love to hear about them! Robot Building for Teens will teach you how to get started in this fascinating hobby. The projects in this book include the "Digital Brain Robot," the "Bug Bot," and even a robot on wheels!

Dipayan Dev has completed his M.Tech from National Institute of Technology, Silchar with a first class first and is currently working as a software professional in Bengaluru, India. He has extensive knowledge and experience in non-relational database technologies, having primarily worked with large-scale data over the last few years. His core expertise lies in Hadoop Framework. During his postgraduation, Dipayan had built an infinite scalable framework for Hadoop, called Dr. Hadoop, which got published in top-tier SCI-E indexed journal of Springer. Dr. Hadoop has recently been cited by Goo Wikipedia in their Apache Hadoop article.

Robots are making people redundant, and you may be next. Once a flight of fancy, we are now in an age where workers of all stripes, including professionals and white-collar workers, could be on the receiving end of pink slips thanks to robots. The debate over robots and artificial intelligence has mostly been confined to advanced economies, but the trend will no doubt have wide implications for emerging economies in Asia as well. The Nikkei and the Financial Times conducted a joint research project regarding the possible impact of automation in the workplace. Using an interactive calculator developed by the FT that draws on data from the McKinsey Global Institute, the Nikkei Asian Review looked at some jobs that Asia's workers can expect to lose to robots.

There are many choices we make over the course of our lives. Some are fairly insignificant, like the clothes we put on in the morning; others, such as the vocations we settle on, have life-changing consequences. But there's one critical decision we don't get to make: the choice of being born into a human body--and all the arbitrary ailments and inevitable biological breakdowns that follow. This is what sets health care apart from other industries. The business of medicine is quite literally one of life and death. And throughout much of the world, it remains a messy, inefficient, expensive sector in need of radical reform. Just consider some of the heart-wrenching numbers.

Since 2012, private robo-advisors have raised over $1.32B globally across 119 equity investments. Robo-advisors make up the largest sub-category of companies in wealth tech and account for roughly 30% of total funding. Three of the earliest robo-advisors firms and largest in terms of total funding are Betterment, Personal Capital, and Wealthfront. Though they lead in the US, expanding internationally is a challenge because of the complex international regulatory environment, differing investment practices, and other barriers to entry. Seeing the market opportunity outside the US, new early-stage (seed/angel or Series A) robo-advisors have been launching in many different markets and span at least 17 countries outside of the US.

There is a cliff approaching fast that India is unprepared for. It's in the near future and will be upon us in 25 or 30 years, according to the people who have understood it best. In brief it is the creation of an artificial intelligence that is smarter than man. Once this is created, it will replicate itself and improve itself faster than we can imagine, leave alone compete with. Science writers call this moment the technological singularity.

I am going out on a limb here trying to better understand our future and those who now have a hand in changing our lives to make it better -- or worse -- depending on your personal bent. In almost every informational email I receive, there is reference to robotics in the workplace and our daily lives. As I mentioned in last month's articles attesting to the coming explosion of robotics in the workplace -- including in those places least expected -- the possibility of sitting side-by-side with a robot intrigues me -- or maybe soon it will be the robot who sits here instead of me! Just about every comic strip I have seen in the Nevada Appeal this month made some reference to robots. Though I am not particularly a fan of the Dilbert strip, what attracted my attention this month was the love relationship one of the main female characters has with a robot "boyfriend."

Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypotheses. For high-dimensional multivariate distributions, these hypotheses may concern a wide range of parameters, with complex and unknown dependence structures among variables. In analyzing such hypothesis testing procedures, gains in efficiency and power can be achieved by performing variable reduction on the set of hypotheses prior to testing. We present in this paper an approach using data-adaptive multiple testing that serves exactly this purpose. This approach applies data mining techniques to screen the full set of covariates on equally sized partitions of the whole sample via cross-validation. This generalized screening procedure is used to create average ranks for covariates, which are then used to generate a reduced (sub)set of hypotheses, from which we compute test statistics that are subsequently subjected to standard multiple testing corrections. The principal advantage of this methodology lies in its providing valid statistical inference without the \textit{a priori} specifying which hypotheses will be tested. Here, we present the theoretical details of this approach, confirm its validity via a simulation study, and exemplify its use by applying it to the analysis of data on microRNA differential expression.

This work presents a novel objective function for the unsupervised training of neural network sentence encoders. It exploits signals from paragraph-level discourse coherence to train these models to understand text. Our objective is purely discriminative, allowing us to train models many times faster than was possible under prior methods, and it yields models which perform well in extrinsic evaluations.

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.