Recently we wrote about the top HR blogs that you should follow. All of the blogs we profiled in that post consistently publish great content, but some content is shared far greater than others. Psychologists, SEO and marketing experts have been studying virality and content sharing for years to understand what are the associated triggers that leads to virality. Why are some articles shared while others are ignored? What common traits are there between content from different blogs and news sources that goes viral?

Convolutional Neural Networks can be visualized as computation graphs with input nodes where the computation starts and output nodes where the result can be read. Here the models that are provided with mxnet are compared using the mx.viz.plot_network The output node is at the top and the input node is at the bottom.

Artificial intelligence (AI) is the most promising technology that can change the world, said Facebook's CEO Mark Zuckerberg on Saturday. "Artificial intelligence will understand senses, such as vision and feeling, better than human beings. Its application in daily lives such as autonomous driving will improve the world," Zuckerberg said at the China Development Forum in Beijing. According to him, though it will take a few more years for the cutting-edge technology to be widely used, its potential is huge. They can always maintain their focus.

Gary Marcus March 15, 2016 For nearly half a century, Artificial Intelligence (AI) has been more science fiction than science: exciting, possible, but just out of reach. And despite significant advances, "strong AI" in many ways remains elusive. Best-selling author and entrepreneur Gary Marcus provides a cognitive scientist's perspective on AI. What are we still struggling with? Perhaps most compelling, is there anything programmers of AI can still learn from studying the science of human cognition?

HP Enterprise Bets on'Machine Learning' Cloud Service HP, having backed away from a key portion of the cloud computing-on-demand market, is expanding into cloud services to help companies analyze data such as photos, audio clips and comments on social media.Read more.

Microsoft's brain is now available for anyone to use in their apps. The company has open sourced the artificial intelligence framework it uses to power speech recognition in its Cortana digital assistant and Skype Translate applications. This means that anyone in the world is now free to view, modify, and use Microsoft's code in their own software. The framework, called, CNTK, is based on a branch of artificial intelligence called deep learning, which seeks to help machines do things like recognize photos and videos or understanding human speech by mimicking the structure and functions of the human brain. Tech giants like Microsoft, Google and Facebook have invested heavily in deep learning research for years, going so far as to hire many of academics who pioneered the field.

When a person's intelligence is tested, there are exams. When artificial intelligence is tested, there are games. But what happens when computer programs beat humans at all of those games? This is the question AI experts must ask after a Google-developed program called AlphaGo defeated a world champion Go player in four out of five matches in a series that concluded Tuesday. Long a yardstick for advances in AI, the era of board game testing has come to an end, said Murray Campbell, an IBM research scientist who was part of the team that developed Deep Blue, the first computer program to beat a world chess champion.

Facebook is releasing for free the designs of a powerful new computer server it crafted to put more power behind artificial-intelligence software. Serkan Piantino, an engineering director in Facebook's AI Research group, says the new servers are twice as fast as those Facebook used before. "We will discover more things in machine learning and AI as a result," he says. The social network's giveaway is the latest in a recent flurry of announcements by tech giants that are open-sourcing artificial-intelligence technology, which is becoming vital to consumer and business-computing services. Opening up the technology is seen as a way to accelerate progress in the broader field, while also helping tech companies to boost their reputations and make key hires.

I have voraciously read endless pro and con scenarios about artificial intelligence since first writing about it years ago. At this point, there is no doubt that concerns about the dangers of runaway AI raised by Elon Musk, Stephen Hawking, Bill Gates, Bill Joy and others are genuine. There also is no doubt whatsoever that the new organizations aimed at mitigating the dangers -- OpenAI, The Future of Life Institute, Machine Intelligence Research Institute and others -- are extremely important developments. Clearly, no sane person or organization wants to see, let alone encounter, runaway AI. However, a base problem is that no one knows where the actual crossover point -- the edge or tipping point -- exists, and thus we mortals are unlikely to be able to prevent it from occurring. Said differently, there is a very high probability that we will misjudge where that crossover point is and will thus go beyond the key threshold.

ABSTRACT: This talk will review several recent papers which aim to modify popular machine learning methods for problems of causal inference, such as evaluating the impact of a treatment using experimental or observational data. We will focus on estimation of treatment effect heterogeneity (that is, which individuals are predicted, based on their features, to have higher or lower benefits of a treatment) in settings such as A/B testing platforms or medical trials where it is important to provide confidence intervals in addition to estimated effects. We analyze the tradeoffs between evaluating model fit based on observed outcomes, and evaluating model fit based on an estimate of the (unobserved) treatment effect. We build on this work to show how random forests can be modified to provide asymptotically centered estimates of treatment effect heterogeneity in experiments or observational studies.