Artificial intelligence (AI) has come a long way since Deep Blue beat chess grandmaster Garry Kasparov at his own game in 1997. Today's AIs have moved on from chess to conquer other games such as Go and Shogi. Fantastical tales of AI abound, but their biggest impact on our daily lives is less explicit. Machine learning algorithms have revolutionized the way businesses and organizations use big data. From Netflix predicting content for your next media binge to Amazon suggesting new products for your shopping cart, machine learning algorithms can process large volumes of unstructured data into valuable insights, predictions, decisions, and services.

In our quest to understand and replicate the cognitive capabilities of the human brain, the AI discipline has focused on the subject of learning rather unevenly. Regardless of the non-scientific reasons, I felt compelled to raise awareness of the most important 3 components of learning mainly distinguished by the "time" factor. Without taking into account all 3 forms of learning, it is unlikely to achieve ambitious goals in AI regardless of how much computing power, or data collection is available to us. When a wildebeest calf is born, it takes only a few minutes for her to run fast enough to escape from predators. It is obvious, evolution has hard-wired some of its learning in the blue print of a new born calf in terms of motor skills.

Although human intervention will occur when needed, the DeepMind system will take screenshots every five minutes to analyse where and when energy consumption levels can be reduced. DeepMind said in a recent blog post: "Even minor improvements would provide significant energy savings and reduce CO2 emissions to help combat climate change." Any necessary changes are checked for predicted energy efficiency and temperature levels, as well as any possuble safety repercussions, before being implemented. Google have reported energy savings of approximately 30% as a result of the use of DeepMind's cooling system. See also: Going green: technology's battle to save the planet The multinational search engine have been using a DeepMind cooling system for the last few months, but until this past week, the system merely offered recommendations for humans to accept or reject.

AI team joined the Movidius team in Intel's Artificial Intelligence Products Group. With this acquisition, Intel gained an experienced team and IP to further enable flexible deep learning at the edge. Additional details and terms are not being disclosed." said Intel in a statement to Techcrunch.

Text classification implementation with TensorFlow can be simple. One of the areas where text classification can be applied -- chatbot text processing and intent resolution. I will describe step by step in this post, how to build TensorFlow model for text classification and how classification is done. Please refer to my previous post related to similar topic -- Contextual Chatbot with TensorFlow, Node.js and Oracle JET -- Steps How to Install and Get It Working. I would recommend to go through this great post about chatbot implementation -- Contextual Chatbots with Tensorflow.

Such feats of work are a testament to modern machine learning. But for all its power, Facebook's world-mapping AI has a severe weakness – it did not understand what it had done. If asked to explain itself, it wouldn't even understand the question. Unlike a human, Facebook's superhuman mapping system is completely incapable of even ordering a cup of coffee, let alone speaking French or shooting hoops. This is typical of modern artificial intelligence, which is built around a technique that apes the human visual system: deep learning.

In 2016 Google and DeepMind collaborated to develop an AI-powered recommendation system with the goal to improve the energy efficiency of Google's data centres. And now, Google has taken this same AI system and enhanced it to remove human-implemented recommendations and instead let it directly control data centre cooling itself – while under expert supervision of course. So how does it work? "Every five minutes, our cloud-based AI pulls a snapshot of the data centre cooling system from thousands of sensors and feeds it into our deep neural networks, which predict how different combinations of potential actions will affect future energy consumption," Google's Amanda Gasparik and DeepMind's Chris Gamble and Jim Gao reported in a release. "The AI system then identifies which actions will minimise the energy consumption while satisfying a robust set of safety constraints. Those actions are sent back to the data centre, where the actions are verified by the local control system and then implemented."

In this tutorial you will learn how to use OpenCV to detect text in natural scene images using the EAST text detector. OpenCV's EAST text detector is a deep learning model, based on a novel architecture and training pattern. It is capable of (1) running at near real-time at 13 FPS on 720p images and (2) obtains state-of-the-art text detection accuracy. In the reminder of this tutorial you will learn how to use OpenCV's EAST detector to automatically detect text in both images and video streams. To discover how to apply text detection with OpenCV, just keep reading!

Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research works have proposed machine learning based methods for them, no existing work has summarized and compared these methods from a unified perspective. This survey aims to provide a systematic review of machine learning for STSF. In this survey, we define the STSF problem and classify it into three subcategories: Trajectory Forecasting of Moving Point Cloud (TF-MPC), STSF on Regular Grid (STSF-RG) and STSF on Irregular Grid (STSF-IG). We then introduce the two major challenges of STSF: 1) how to learn a model for multi-step forecasting and 2) how to adequately model the spatial and temporal structures. After that, we review the existing works for solving these challenges, including the general learning strategies for multi-step forecasting, the classical machine learning based methods for STSF, and the deep learning based methods for STSF. We also compare these methods and point out some potential research directions.

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.