Artificial intelligence (AI) has officially gone mainstream. Industry research firm Gartner named AI as its number one strategic technology for a second year in a row. The acquisitions race among giants like Google, IBM, Salesforce and Apple to purchase private AI companies keeps heating up -- 2016 alone saw 40 AI-related acquisitions and our own research found that 62% of large enterprises will be using AI-technologies by 2018. Since everyone seems to be talking about AI broadly, we at Narrative Science*–where we work with enterprises to close the communication gap between man and machine– focused our predictions this year on what we see happening with communications and AI. For 2017, we predict changes in to how we'll communicate with computers and other devices, how AI systems will communicate with each other, and how we'll communicate with each other about AI. The recent, combined efforts of a number of innovative tech giants point to a coming year when interacting with technology through conversation becomes the norm.

Deep learning gradually plays a major role on NLP (Natural Language Processing). Here I note some technical evolution for the NLP problems. A continuous text sequence "to be or not to be" can be modelled by: N-gram model can solve the problem of next word prediction, e.g., the occurrence of 6-gram model can predict the probability of next word is "be" if the previous words are "to be or not to": With neural network, the idea is proposed to train a shared matrix C which can project each word into a feature vector, and put the vector as the input of a neural network to train the main task. Suppose the dimension of feature space is M, and vocabluary is V, the projection C is a V *M matrix. The input layer contains N-1 previous words in a N-gram model, which is encoded by 1-to- V representation.

It's the last day of 2016 and many of us are already planning a better 2017. And we are not alone on that. Many tech companies have plans to bring many and exciting technology trends. Some of them have already made an impact on 2016 and the new year might bring more features. Let's check the most noticeable technology trends for 2017.

The UK's Investigatory Powers Act is now in effect, placing Britain under some of the widest-ranging spying powers ever seen. The law – passed last month but going into effect on 30 December – is intended as an update to Britain's often unwieldy surveillance legislation. But it also includes a large set of new powers – including the ability to collect the browsing records of everyone in the country and have them read by authorities as diverse as the Food Standards Agency and the Department for Work and Pensions. Most of the central parts of the act are now in force. That includes new powers to gather and retain data on citizens, and new ways to force technology companies and others to hand over the data that they have about people to intelligence agencies.

AI and machine learning promise to not only improve the customer experience, but also change the way companies operate. For this reason, enterprises should consider integrating these technologies into digital transformation plans to stay competitive. By 2019, 40 percent of all digital transformation initiatives will be supported by cognitive/AI capabilities, according to IDC. "The time for AI has now finally come, because of the technique of deep reasoning connected with the amazing amounts of computer power and data," said Sanjay Srivastava, chief digital officer of Genpact. The opportunity now is to apply those technologies in the business context." For example, we can now use AI for account management and customer service systems across industries. "The benefit isn't just the fact that you get productivity, but the fact that you can scale very quickly," Srivastava said. Gartner predicted that by 2018, 20 percent of business content (such as shareholder reports, legal documents, and ...

The time of year when crystal balls get a viewing and many pundits put out their annual predictions for the coming year. Rather than thinking up my own, I figured I'd regurgitate what many others are expecting to happen. Chris Preimesberger (@editingwhiz), who does a monthly #eweekchat on twitter, covers many of the worries facing organizations. People focus so much on the'things' themselves rather than the risk of an internet connection. This list discusses how IoT will grow up in 2017, how having a service component will be key, the complete mess of standards and simply, 'just because you can connect something to the Internet doesn't mean that you should.' NW talks about how cyber attacks will get worse due to IoT and gives some ideas on how to protect your data in 2017.

Today's tech buzzword, Artificial Intelligence (AI), can be a difficult concept for many people to wrap their heads around. AI technology, which arguably holds vast potential to revolutionize the way the world works, is nonetheless often used to paint an apocalyptic view of the future. We now live in a world where talking computers and self-driving cars are no longer a thing of the future – but are we also at risk of developing computers so smart that they can replace humans? Influential tech figures such as Elon Musk and Stephen Hawking have cautioned the industry against diving head-on into AI – because the technology's implementation could do more harm than good if we aren't careful. While there's no arguing that some of the cautionary tales around AI hold merit, our current reality is far from the robot-run society we've seen in the movies.

Insomnobot has been described as "easily distracted." It was 2:37 a.m. and every moment of wakefulness that passed would leave me more tired in the morning, but I just couldn't fall back asleep. At the very least, I could use someone to talk to. This was no fellow insomniac, nor the hotline for Night Owls Anonymous. It was Insomnobot-3000, a chatbot designed by mattress company Casper to keep company with the sleepless from 11 p.m. and 5 a.m. EST, while most humans are catching their Zs.

It was certainly a year of ups and downs for Apple, which took steps to shore up many of its products outside the core iOS portable offerings. The Apple Watch got a new user interface, but the smartwatch category drags. Apple TV got a new content navigation app (but not an integrated Apple television service). The redesigned MacBook, of course, got a wee strip of touchy goodness. Even the EarPods evolved into a game-changing wireless successor.

A theme in my blogs is how the "structure" of data - rather than just the "content" - affects what that data can say and is capable of doing. In particular, I suggest that certain structures tend to reinforce certain contents; this means that a structural imposition can have an effect similar to a contextual imposition. Structure is an interesting conversation because, at first glance, arguably data has none. Structure is something that is attributed to physical things. But data normally has no physical existence - at least not in a tangible sense except maybe in relation to storage devices. Yet I am certain that almost everyone who has programmed in an object-oriented language has had to deal with multifarious data structures.