Announcing our new Foundation for Deep Learning acceleration MIOpen 1.0 which introduces support for Convolution Neural Network acceleration -- built to run on top of the ROCm software stack! Install the ROCm MIOpen implementation (assuming you already have the'rocm' and'rocm-opencl-dev" package installed): For just OpenCL development

As machine learning makes its way into more applications--leveraging everything from sensor data to consumer information repositories--pressure for hardware and software engineers to familiarize themselves with the technology grows. Because this type of control algorithm differs in key ways from those based on traditional logic, the learning curve may be steeper for some designers. Nevertheless, it's time for all engineers to understand how this technology changes the design process and what tools and practices help with its implementation. One of the best ways to understand machine learning is to consider how it differs from conventional control mechanisms. Traditional programming uses Boolean logic's true and false rules to define a program's behavior, building the application via a series of defined steps, where the rules making up the program ensure what action happens next. Machine learning takes a different approach, built on inductive reasoning.

We understand that in convex problems it is much easier to find the global optimum. We appreciate the opportunity to participate in this discussion. KD, MF: No, the convexified problem can have a minimum that is quite different from the original problem. The motivation for our paper comes from the fact that in many problems (like control and reinforcement learning) one is interested in a "robust" minimum (a minimum such that the cost does not increase much when you perturb the parameters). Our method destroys non-robust minima and preserves a single robust minimum of the problem.

Used as a part of artificial intelligence systems, applications of NLP technologies are being deployed for predictive analysis and clinical decision support systems,

Jiro's pick this week is CNN for Old Japanese Character Classification by one of my colleagues Akira Agata. Nowadays, I probably go many days without seeing a handwritten document. From computers, to smartphones, to TVs, to books, almost every character I see is a printed character. So it's refreshing to see a handwritten document from time to time. This demo by Akira uses deep learning (convolutional neural networks) to classify various handwritten Japanese characters.

At the GTC technology conference this year, NVIDIA launched their latest and most advanced GPU called Volta. At the center of this chip is Tensor Core, an Artificial Intelligence accelerator that that is poised to usher in the next phase of AI applications. However, our current AI algorithms are not fully utilizing this accelerator, and for us to achieve another major breakthrough in AI, we need to change our software. The realization of this computing resource will advance and even create AI applications that might otherwise not exist. For example, by utilizing this resource, AI algorithms could better understand and synthesize human speech.

Of all the fields in the chatbot-crazed world, customer service is one of the prime targeting areas for automation. Virtual Customer Agents (customer service focused bots or VCAs for short) are intelligent systems that are able to understand what users ask via chat and provide them with adequate answers to solve users' issues. In the context of this article when we talk about VCAs we mean systems that are able to understand natural language and texting and do not just operate in a rule based multiple-choice environment. In short, these VCAs compete directly with humans to resolve customer service issues. The current reality of chatbots nicely counterbalances all the hype that AI is getting and also offers guidance as to where things need to develop.

This paper studies the generalization error of invariant classifiers. In particular, we consider the common scenario where the classification task is invariant to certain transformations of the input, and that the classifier is constructed (or learned) to be invariant to these transformations. Our approach relies on factoring the input space into a product of a base space and a set of transformations. We show that whereas the generalization error of a non-invariant classifier is proportional to the complexity of the input space, the generalization error of an invariant classifier is proportional to the complexity of the base space. We also derive a set of sufficient conditions on the geometry of the base space and the set of transformations that ensure that the complexity of the base space is much smaller than the complexity of the input space. Our analysis applies to general classifiers such as convolutional neural networks. We demonstrate the implications of the developed theory for such classifiers with experiments on the MNIST and CIFAR-10 datasets.

Jürgen, it's a privilege to have you here as one of the pioneers of artificial intelligence and, more specifically, deep learning--its hottest field right now. Before you go into all of those fields, we would like to understand the person Jurgen Schmidhuber better. Perhaps you can tell us a few things that you're particularly proud of in your career. One of the things I'm proud of: I think I understand what it means to be curious and how to implement curiosity, which I think is essential to build agents that learn from experience through their own self-generated experiments. Agents who are motivated to invent, in a directed way, action sequences or experiments that lead to data that tell them something about how the world works that they didn't know yet.

Summary: Deep learning and Big Data are being adopted in law enforcement and criminal justice at an unprecedented rate. Does this scare you or make you feel safe? When you read the title, whether your mind immediately went for the upstairs "H" or the downstairs "H" probably says something about whether the new applications of Big Data in law enforcement let you sleep like a baby or keep you up at night. You might have thought your choice of "H" related to whether you've been on the receiving end of Big Data in law enforcement but the fact is that practically all of us have, and for those who haven't it won't take much longer to reach you. There is an absolute explosion in the use of Big Data and predictive analytics in our legal system today driven by the latest innovations in data science and by some obvious applications.