Deep learning which is currently a hot topic in the academia and industries tends to work better with deeper architectures and large networks. The application of deep learning in many computationally intensive problems is getting a lot of attention and a wide adoption. For example, computer vision, object recognition, image segmentation, and even machine learning classification. Some practitioners also refer to Deep learning as Deep Neural Networks (DNN), whereas a DNN is an Artificial Neural Network (ANN) with multiple hidden layers of units between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships [1]. The DNN architectures for example for object detection and parsing, generates compositional models where the object is expressed as a layered composition of image primitives. The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network.

Johann Sebastian Bach is widely considered one of the great composers of baroque music. Bach lived and worked in Germany during the 18th century and is revered for the beauty of his compositions and his technical mastery of harmony and counterpoint. One form of music that Bach excelled in was a type of polyphonic hymn known as a chorale cantata. These are based on Lutheran texts and sung by four voices. The composer starts with a well-known tune which is sung by the soprano and then composes three harmonies sung by the alto, tenor, and bass voices.

Now, Advanced Micro Devices (AMD) is the process of computing its prowess to release the next big thing in the server market - the machine learning and artificial intelligence. The new machine learning is called "Radeon Instinct." The upcoming device is said to provide server designers and developers a compelling set of infrastructure to track the machine learning. The new Radeon Instinct is composed of hardware and software aspects that have the ability to deliver a full machine intelligence platform. AMD wants that all industries like financial services, life sciences, and the cloud that are now in the machine learning solutions and infrastructure will use Radeon Instinct for their computing requirements.

Deep learning, which is basically neural network machine learning with multiple hidden layers, is all the rage--both for problems that justify the complexity and high computational cost of deep learning, such as image recognition and natural language parsing, and for problems that might be better served by careful data preparation and simple algorithms, such as forecasting the next quarter's sales. If you actually need deep learning, there are many packages that could serve your needs: Google TensorFlow, Microsoft Cognitive Toolkit, Caffe, Theano, Torch, and MXNet, for starters. I confess that I had never heard of MXNet (pronounced "mix-net") before Amazon CTO Werner Vogels noted it in his blog. There he announced that in addition to supporting all of the deep learning packages I mentioned above, Amazon decided to contribute significantly to one in particular, MXNet, which it selected as its deep learning framework of choice. Vogels went on to explain why: MXNet combines the ability to scale to multiple GPUs (across multiple hosts) with good programmability and good portability.

Thanks to advanced new machine learning techniques, artificial intelligences are better at performing human tasks than ever. AIs can tell you what's in your photos, beat you at chess, design typefaces, dream up entirely new cities, and even tweet like Donald Trump--often better than the average person. They can't apply what they've learned from one problem to another--which is why even the best AIs are idiot savants: really smart in one arena, and dumb as sticks in all others. So how can AIs reach this elusive general intelligence? OpenAI--an artificial intelligence research nonprofit backed by Microsoft, Elon Musk, and Peter Thiel--thinks it involves AIs playing video games.

Microsoft Corp. is funding a Montreal startup co-founded by renowned artificial-intelligence expert Yoshua Bengio, another endorsement of the city's reputation as an emerging global centre for one of most anticipated technology trends. The Redmond, Wash.based software giant, which is positioning itself to be a big player in AI, is announcing Monday the launch of a new venture fund to finance AI firms. Its first investment is in Montreal-based Element AI, co-founded by Dr. Bengio, a University of Montreal professor regarded as one of the pioneers of "deep learning," a field that uses algorithms to model and process information similar to how the brain works. Microsoft will also provide Element AI with access to its technology and services. "AI holds great promise to augment human capabilities and improve society," Nagrap Kashyap, corporate vice president of Microsoft Ventures, said.

Human cognitive abilities decline with age. And neuroscientists have long known that this decline correlates with anatomical changes in the brain as well. So it's no surprise to learn that it is possible to spot the signs of aging in MRI images of the brain and even to determine a "brain age." The difference between brain age and chronological age can reveal the onset of conditions such as dementia. But the analysis is lengthy because the MRI data has to be heavily processed before it is suitable for automated aging.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference? There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields.

The Minecraft video game is popular with children; now a version is being used to test artificial-intelligence programs. The Minecraft video game was familiar to José Hernández-Orallo long before he started using it for his own research. The computer scientist, who devises ways to benchmark machine intelligence at the Polytechnic University of Valencia in Spain, first watched his own children play inside the 3D virtual world, which focuses on solving problems rather than shooting monsters. In 2014, Microsoft bought Minecraft, and its science arm, Microsoft Research, gave its own researchers access to a new version of the game that allowed computer programs, as well as people, to explore and customize the 3D environment. Then, after inviting a small group of outside researchers that included Hernández-Orallo to download the machine-friendly version of the world, last July, Microsoft made it freely available to anyone, with the goal of speeding up progress in artificial intelligence (AI).

The Allen Institute for Artificial Intelligence has organized a 4 month contest in Kaggle on question answering. The aim is to create a system which can correctly answer the questions from the 8th grade science exams of US schools (biology, chemistry, physics etc.). DeepHack Lab organized a scientific school hackathon devoted to this contest in Moscow. Our team decided to use this opportunity to explore the deep learning techniques on question answering (although they seem to be far behind traditional systems). We tried to implement Dynamic memory networks described in a paper by A. Kumar et al.