Nvidia is on a quest to find the most disruptive artificial intelligence startups. This quest is part of a larger contest dubbed Nvidia Inception, which is screening more than 600 entrants to cull the best AI startups in three big categories. We wrote about the first four candidates for the hottest emerging startup on Friday. And now we're focusing on the next five candidates in the category dubbed the "most disruptive" startups. Jen-Hsun Huang, CEO of Nvidia, hosted a Shark Tank-style event this week as part of the search to find the best AI startups.

Steve Cousins is founder and CEO of Savioke, which develops and deploys autonomous robots that work in human environments to improve people's lives. Steve was previously president and CEO of robotics incubator Willow Garage. When Bill Gates recently suggested robots should pay income tax like any other employee, I didn't immediately disagree. I applaud Gates' bold thinking to help solve one of society's biggest upcoming challenges: embracing automation in a way that "lifts all boats" instead of leaving large swaths of society behind. A robot tax would help offset the reduced revenues flowing into public coffers as machines take some jobs previously held by humans.

When it comes to alleviating some of the world's most pressing problems, perhaps we should look to the skies. The word "drone" might inspire images of counterterrorism strikes and the future of package delivery. But quadcopters and other autonomous flying vehicles are revolutionizing the ways we tackle the biggest social and environmental issues of our time. While there are definite drawbacks to using drones in this capacity -- problems of privacy, ethics, and cost among them -- the technology, when executed responsibly, helps aid organizations, scientists, and everyday citizens transform the act of doing good. From edible drones delivering lifesaving assistance to rural communities to quadcopters tracking illegal logging in rainforests, here are just a few of the recent ways people have used drones for social good. Unmanned aerial vehicles have a proven track record of being useful in disaster relief efforts.

How AI will transform Germany's industrial sector. Artificial intelligence (AI) is finally bringing a multitude of capabilities to machines that were long thought to belong exclusively to the human realm, such as processing natural language or visual information. In this report, we explain how and where AI could affect the German industrial sector by exploring several questions: Which subindustries are most strongly affected by the automation potential of AI? What are the most promising use cases? What are pragmatic recommendations for managers of industrial players planning to harness the power of AI? Highly developed economies like Germany, with a high GDP per capita and challenges such as a quickly aging population, will increasingly need to rely on automation based on AI to achieve GDP targets. About one-third of Germany's GDP aspiration for 2030 depends on productivity gains.

The EU is launching an unprecedented public consultation to find out what Europeans fear most about the future of the internet. A succession of surveys over the coming weeks will ask people for their views on everything from privacy and security to artificial intelligence, net neutrality, big data and the impact of the digital world on jobs, health, government and democracy. A dozen leading European publications, including the Guardian, are to publicise the surveys over the coming three weeks. Results will be compiled in early June. Readers can complete the first questionnaire here.

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.

In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular). Based on the Stanford Computer Science course CS246 and CS35A, this book is aimed for Computer Science undergraduates, demanding no pre-requisites. This book has been published by Cambridge University Press. This book holds the prologue to statistical learning methods along with a number of R labs included. This Deep Learning textbook is designed for those in the early stages of Machine Learning and Deep learning in particular.

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.

Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.

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.