In the past few years, AI has crossed the threshold from hype to reality. Today, with unstructured data growing by 23% annually in an average organization, the combination of knowledge graphs and high performance computing (HPC) is enabling organizations to exploit AI on massive datasets. Full disclosure: Before I talk about how critical graph computing HPC is going to be, I should tell you that I'm CEO of a graph computing, AI and analytics company, so I certainly have a vested interest and perspective here. But I'll also tell you that our company is one of many in this space -- DGraph, MemGraph, TigerGraph, Neo4j, Amazon Neptune, and Microsoft's CosmosDB, for example, all use some form of HPC graph computing. And there are many other graph companies and open-source graph options, including OrientDB, Titan, ArangoDB, Nebula Graph, and JanusGraph.

Data augmentation is a popular pre-processing trick to improve generalization accuracy. It is believed that by processing augmented inputs in tandem with the original ones, the model learns a more robust set of features which are shared between the original and augmented counterparts. However, we show that is not the case even for the best augmentation technique. In this work, we take a Domain Generalization viewpoint of augmentation based methods. This new perspective allowed for probing overfitting and delineating avenues for improvement. Our exploration with the state-of-art augmentation method provides evidence that the learned representations are not as robust even towards distortions used during training. This suggests evidence for the untapped potential of augmented examples.

I am a baseball fan. This will come as no surprise to anyone who has followed me for any length of time. This year, I was recruited to be a part of a fantasy league. For those who know about such things, it's a 5x5 league (Batting SB, R, RBI, Avg, HR and Pitching W, Saves, WHIP, ERA, Strikeouts). I was honored to be invited because this particular league has been around since before baseball statistics became so ubiquitous.

Whether it boosts productivity or increases workplace satisfaction, artificial intelligence (AI) is increasingly recognized as an essential tool to unlock a competitive advantage in business. It's hard to dispute the potential of AI to transform the way we work. This is a technology that Alphabet CEO Sundar Pichai stated at Davos was akin to "electricity and fire" in its impact on humanity. And I agree -- AI will be (and is) revolutionary. However, for AI to achieve a meaningful, positive impact on businesses and the world, we have to understand it and challenge our preconceptions.

Few insurers have sufficient high-quality and correctly classified training data with which to learn AI algorithms. Smart technologies, artificial intelligence (AI) and machine learning are all on the agenda for insurers. But how intelligent are the systems really? Google, Amazon or Facebook have led the way, and data is the currency of the future. This is particularly true for insurers, because, if you know your customers' behavior and life situation, you can price risks more accurately, minimize fraud and better meet customer expectations.

The internet is always speculating and wondering about what's to come, in terms of access modes, once mobile and screens have ceased to be the latest shiny new thing. The theory is that artificial intelligence (AI) is going to be the next evolution; and precisely because of that prediction about AI (plus its other uses), we witnessed an unprecedented technology gold rush in 2017. The craze about AI had people investing millions of dollars into new products and services, and hand-wringing skeptics forming think tanks to postulate about the end of the world. But, for all of the speculation, what did AI actually do in 2017? And, what's it poised to do in the coming year?

Artificial intelligence holds an untapped potential for Marketing departments and changes customer relationships in depth. Predictive Marketing could help you unlock the untapped potential in your business. Everything we do impacts on our customers buying behaviour. Marketing, sales promotion, pricing, competitor activity and service can all result in more sales or lost sales. Organisations that have developed a single customer view are able to develop customer segmentation based on customer value, understand exactly how much marketing activity to carry out to which customers and which of their most valuable customers are at risk of leaving.

The moment of the next revolution in the history of mankind is fast approaching – the difference between this and all previous revolutions, both social and economic, is that this time we won't be in it alone. We will be accompanied by complex computer systems carrying out activities, which, due to our cognitive abilities, were previously reserved only for human beings. The era of artificial intelligence is here. Probably the most famous examples of the application of artificial intelligence, though rather simplistic, were the chess duels between grandmaster Garry Kasparov and IBM computer Deep Blue in 1996 and 1997. These duels were supposed to show that a machine can not only perform certain actions similar to a human being, but also outperform its human counterpart.

You've probably seen this scenario play out on a police procedural show on television: A crime has been committed and officers are tasked with looking through security footage to see if any of it was caught on camera. On TV, they can cut away to commercial and have the answer back as soon as they return. In real life, however, analyzing huge quantities of video data is a task that's rarely accomplished effectively by human operators. There's just too much data to sift through, and the cost for the man hours required is too high. But that problem is being overcome with machine learning and video analytics.

The Outrageous Computing Company Nvidia is all ready to dive into diverse markets like Driverless cars, deep learning and artificial intelligence.