When Chinese Go master Ke Jie was comprehensively defeated by Google's artificial intelligence (AI) program AlphaGo on May 27 in the ancient canal town of Wuzhen, technology watchers around the world had to redraw the timeline in which AI rules the world. The artificial mind had swept all three matches against the world's top player, a feat once thought impossible, given Go's deep complexity compared with previous AI wins, such as in chess or checkers. The event also underscored continued Western dominance in the field of AI. AlphaGo was developed by the American search juggernaut's Deepmind unit, a British firm it acquired in 2014. But China is today nipping at the heels of the United States, long the undisputed leader in AI technology, and may soon be poised to edge ahead as it brings products to market at a quicker pace.

Emotion being a subjective thing, leveraging knowledge and science behind labeled data and extracting the components that constitute it, has been a challenging problem in the industry for many years. With the evolution of deep learning in computer vision, emotion recognition has become a widely-tackled research problem. In this work, we propose two independent methods for this very task. The first method uses autoencoders to construct a unique representation of each emotion, while the second method is an 8-layer convolutional neural network (CNN). These methods were trained on the posed-emotion dataset (JAFFE), and to test their robustness, both the models were also tested on 100 random images from the Labeled Faces in the Wild (LFW) dataset, which consists of images that are candid than posed. The results show that with more fine-tuning and depth, our CNN model can outperform the state-of-the-art methods for emotion recognition. We also propose some exciting ideas for expanding the concept of representational autoencoders to improve their performance.

In this paper, a novel architecture for a deep recurrent neural network, residual LSTM is introduced. A plain LSTM has an internal memory cell that can learn long term dependencies of sequential data. It also provides a temporal shortcut path to avoid vanishing or exploding gradients in the temporal domain. The residual LSTM provides an additional spatial shortcut path from lower layers for efficient training of deep networks with multiple LSTM layers. Compared with the previous work, highway LSTM, residual LSTM separates a spatial shortcut path with temporal one by using output layers, which can help to avoid a conflict between spatial and temporal-domain gradient flows. Furthermore, residual LSTM reuses the output projection matrix and the output gate of LSTM to control the spatial information flow instead of additional gate networks, which effectively reduces more than 10% of network parameters. An experiment for distant speech recognition on the AMI SDM corpus shows that 10-layer plain and highway LSTM networks presented 13.7% and 6.2% increase in WER over 3-layer baselines, respectively. On the contrary, 10-layer residual LSTM networks provided the lowest WER 41.0%, which corresponds to 3.3% and 2.8% WER reduction over plain and highway LSTM networks, respectively.

"The year 2017 has arrived and we humans are still in charge. That reassuring proclamation came from a New Year's editorial in the Chicago Tribune. If you haven't been paying attention to the news about artificial intelligence, and particularly its newest iteration called deep learning, then it's probably time you started. This technology is poised to completely revolutionize just about everything in our lives. Experts say Canadian workers could be in for some major upheaval over the next decade as increasingly intelligent software, robotics and artificial intelligence perform more sophisticated tasks in the economy. Today, machines are able to "think" more like humans than most of us, even the scientists who study it, ever imagined. They are moving into our workplaces, homes, cars, hospitals and schools, and they are making decisions for us. Artificial intelligence has enormous potential for good. But its galloping development has also given rise to fears of massive economic ...

"This applies to any deep-learning architecture, and the technique scales sublinearly, which means that the larger the deep neural network to which this is applied, the more the savings in computations there will be," said lead researcher Anshumali Shrivastava, an assistant professor of computer science at Rice. The research will be presented in August at the KDD 2017 conference in Halifax, Nova Scotia. It addresses one of the biggest issues facing tech giants like Google, Facebook and Microsoft as they race to build, train and deploy massive deep-learning networks for a growing body of products as diverse as self-driving cars, language translators and intelligent replies to emails. Shrivastava and Rice graduate student Ryan Spring have shown that techniques from "hashing," a tried-and-true data-indexing method, can be adapted to dramatically reduce the computational overhead for deep learning. Hashing involves the use of smart hash functions that convert data into manageable small numbers called hashes.

The rise of neural networks and deep learning is correlated with increased computational power introduced by general purpose GPUs. The reason is that the optimisation problems being solved to train a complex statistical model, are demanding and the computational resources available are crucial to the final solution. Using a conventional CPU, one could spend weeks of waiting for a simple neural network to be trained. This problem is amplified when one is trying to spawn multiple experiments to select optimal parameters of a model. Having computational resources such as a high-end GPU is an important aspect when one begins to experiment with deep learning models as this allows a rapid gain in practical experience.

At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance. However, when we researched what technologies were available, we could not find a concise summary document to reference for starting a new deep learning project. One way to give back to the open source community that provides us with tools is to help others evaluate and choose those tools in a way that takes advantage of our experience. We offer the chart below, along with explanations of the various criteria upon which we based our decisions.

We at Talespin, In the past year have worked extensively on deep learning in Fashion. Implementations of Deep Learning in the past year have many success stories. Using deep learning libraries you can train models to capture the general attributes of clothes (like shirts, pants, shoes or even collar, sleeves etc). We will be posting more use cases and case studies of deep learning in fashion soon.

There is no denying the fact that Artificial Intelligence is the breakthrough technology of recent times. The machines have come a long way from assisting humans in mechanical operations to performing smarter tasks using cognitive intelligence. Every day, we are coming across interesting applications of AI. The ability of Deep Learning algorithms to learn and predict efficiently has opened the doors of possibilities. Nowadays, AI is impacting many other areas as well. In this blog post, we will discuss some niche applications of AI.

How will artificial intelligence change the way we do business five years from now? In part two of our conversation with Seal Software CTO Kevin Gidney, he explains how AI is revolutionizing industries and changing ideas about what jobs will require humans. The Enterprisers Project (TEP): What can we do with AI now that we couldn't do five years ago? Gidney: So many things become possible when hardware and software performance increases to allow the most complex of tasks to be done within a few seconds. Look at deep neural networks and how the training of those is now possible within a few hours (or days) with enough hardware.