In December, footage emerged of the Japanese film director Hayao Miyazaki visiting the Dwango Artificial Intelligence Laboratory in Tokyo. In the clip, which was broadcast as part of an NHK documentary, the director of Spirited Away is shown a video of a computerised humanoid creature that has taught itself to walk by using its head and buttocks to shimmy along the ground. After the presentation Miyazaki sits in thought, before issuing his verdict. "Whoever creates this stuff has no idea what pain is whatsoever," he says. Miyazaki's delivery has none of the vein-throbbing fury of a Gordon Ramsay – only the life-haunting melancholy of the disappointed father.

Bottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

The current speed of development and the potential applications of Artificial Intelligence (AI) suggest that it's time for CEOs to pay attention. So what are the questions every CEO should be asking? Here are the ten questions that I believe to be most important in order to assess and invest in AI's transformative potential: However, in the case of AI it may well be the most important change we'll see in the philosophy, practice and management of business. AI draws on - and is combining with - exponential performance improvements in technologies such as computer hardware, big data management, the Internet of Things and the fields of machine learning, neural networks and robotics. As a result, AI is beginning to fulfill its true potential of transforming businesses and replacing even senior management and leadership roles.

This book of projects highlights how TensorFlow can be used in different scenarios – this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production. Rodolfo Bonnin is a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.

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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.