In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that that vast Map was Useless, and not without some Pitilessness was it, that they delivered it up to the Inclemencies of Sun and Winters. In the Deserts of the West, still today, there are Tattered Ruins of that Map, inhabited by Animals and Beggars; in all the Land there is no other Relic of the Disciplines of Geography. Jorge Luis Borges, Collected Fictions, translated by Andrew Hurley. The above paragraph is the entire "fiction" by Borges entitled On Exactitude in Science.

Moses led his people from the Pharaoh. William Wallace (or was it Mel Gibson?) helped bring FREEEDOOOOM for the Scots. In the past, you've seen me opine on the necessary destruction of the CMO [Chief Marketing Officer Is The Most Dangerous Title Around] as a standalone c-suiter--often more focused on grand vision and strategy than driving bottom-line results. While some called my report of the traditional CMO's death greatly exaggerated, the rise of the machines may actually provide a stay of execution. Now, I'm not reversing course and saying that the CMO should continue to exist in its present cost-draining ivory tower. Rather, I'm advocating that AI may provide salvation for the CMO by giving them the requisite free time to pursue the necessary bottom-line-moving efforts that make marketing worthwhile and justifiably profitable.

Earlier in this series, we learned why IBM is focusing on investments and acquisitions in the IoT (Internet of Things) space. Let's see how AI (artificial intelligence) and ML (machine learning) could drive the expected $2 trillion in spending during the next new computing cycle. The SMAC (social, mobile, analytics, and cloud) revolution is rapidly transforming the technology space. The influx of data, the majority of which is unstructured, coupled with the advances in processing power and cognitive technology, has led to the necessity of machine learning to facilitate better-informed decisions. In today's scenario, understanding the content of images as well as organizing and extracting relevant information from raw media and data pose a significant challenge. The specialty of deep learning is that it can be deployed in structured and unstructured data and context.

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.