In recent years, research in Artificial Intelligence has been primarily dominated by impressive advances in Machine Learning, with a strong emphasis on the so-called Deep Learning framework. It has allowed considerable achievements such as human-level performance in visual classification [1] and description [2], in Atari video games [3] and even in the highly complex game of Go [4]. The Deep Learning approach is characterized by supposing very minimal prior on the task to be solved, compensating this lack of prior knowledge by feeding the learning algorithm with an extremely high amount of training data, while hiding the intermediary representations. However, it is important noting that the most important contributions of Deep Learning for Artificial Intelligence often owe their success in part to their integration with other types of learning algorithms. For example, the AlphaGo program which defeated the world champions in the famously complex game of Go [4], is based on the integration of Deep Reinforcement Learning with a Monte-Carlo tree search algorithm. Without the tree search addition, AlphaGo still outperforms previous machine performances but is unable to beat high-level human players. Another example can be found in the original Deep Q-Learning algorithm (DQN, Mnih et al., 2015), achieving very poor performance in some Atari games where the reward is considerably sparse and delayed (e.g.

Geoffrey Hinton is one the most famous researchers in the field of artificial intelligence. His work helped kick off the world of deep learning we see today. He earned his PhD in artificial intelligence back in 1977 and, in the 40 years since, he's played a key role in the development of back-propagation and Boltzmann Machines. So it was a bit hilarious to learn in a Reddit AMA hosted by the Google Brain Team that Hinton was briefly a Google intern in 2012. Prompted by a question about age cut-offs for interns on Brain Team, Jeff Dean, a Google Senior Fellow and leader of the research group, explained that his team has no arbitrary rules limiting the age of interns.

A group of AI experts from The University of Nottingham and Kingston University managed to create a new method by which two-dimensional images of faces can be converted into 3D using machine learning. The researchers trained a convolutional neural-network to perform the task by feeding it tons of data on people's faces. From there it figured out how to guess what a new face looks like from an previously unseen pic, including parts that it can't see in the photograph. The 3D computer vision project really has to be seen to be believed, and you can try it out in a nifty demo here. The website doesn't really do the full technology justice, but it's bloody cool.

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they're like optical illusions for machines. In this post we'll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult. At OpenAI, we think adversarial examples are a good aspect of security to work on because they represent a concrete problem in AI safety that can be addressed in the short term, and because fixing them is difficult enough that it requires a serious research effort. To get an idea of what adversarial examples look like, consider this demonstration from Explaining and Harnessing Adversarial Examples: starting with an image of a panda, the attacker adds a small perturbation that has been calculated to make the image be recognized as a gibbon with high confidence. An adversarial input, overlaid on a typical image, can cause a classifier to miscategorize a panda as a gibbon.

GANs are a type of deep neural network that allow us to generate data. In this webinar, we'll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We'll explore the GAN framework along with its components -- generator and discriminator networks. We'll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we'll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.

This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data.

Results highlight performance of Theano's current full release which does not support CuDNN 6 while all other frameworks do. I don't see how using half baked beta versions for benchmarking is the right way of running the tests. All frameworks can claim that their next beta version has better performance but they also have unresolved issues. Also CuDNN 7 is already available and if tests are run using beta versions, eventually some will support 7 versus Theano's beta support for CuDNN 6 causing the same issue. I hope you see the point I am trying to make.

You may think of German industrial powerhouse Siemens as being primarily a machine builder, but the company has a range of digital offerings that span throughout the entire value chain in manufacturing. From product development, production engineering, and production execution, the company offers a consistent data model across all levels of manufacturing, thanks to its product lifecycle management, digital twin software, and MindSphere IoT platform. Content Director Brian Buntz wrote recently about the resources Siemens is throwing at software, and while that's significant, I'm more interested in Siemens' AI and machine learning work. Michael May, Ph.D., the company's head of technology field business analytics and monitoring, told me at Hannover Messe that the corporation has been working on AI projects for decades. For instance, more than 20 years ago, Siemens implemented neural networks in more than 30 steel plants to monitor and improve quality, process, and efficiencies.

As humans we feel nothing more viscerally -- in the most literal sense -- than our health. That makes this year's gathering of the Medical Image Computing and Computer Assisted Interventions Society -- MICCAI 2017 -- in Quebec City, Canada, one of the best ways to understand how deep learning is improving the lives of people all around us. The conference brings together leading biomedical scientists, engineers and clinicians to talk about new technologies in medical imaging and computer-assisted intervention, providing an early look at trends poised to sweep through the $6.5 trillion healthcare industry. This will be the group's biggest conference yet, with 1,300 attendees. And deep learning -- which pairs vast quantities of data with sophisticated neural networks to give computers amazing new capabilities -- deserves a lot of the credit, organizers say.