Disclaimer: Our opinions are our own. Let me quote authors of the framework. In this article we will be using it to train a chatbot. More precisely we will be using the following tutorial for neural machine translation (NMT). If you wonder how an NMT model could be used for a chatbot, please see my previous article ("Own ChatBot Based on Recurrent Neural Network for 6$/6 hours and 100 lines of code.").

NetApp and Nvidia have introduced a combined AI reference architecture system to rival the Pure Storage-Nvidia AIRI system. It is aimed at deep learning and, unlike FlexPod (Cisco and NetApp's converge infrastructure), has no brand name. Unlike AIRI, neither does it have its own enclosure. A NetApp and Nvidia technical whitepaper – Scalable AI Infrastructure Designing For Real-World Deep Learning Use Cases (PDF) – defines a reference architecture (RA) for a NetApp A800 all-flash storage array and Nvidia DGX-1 GPU server system. There is a slower and less expensive A700 array-based RA.

"Before we work on artificial intelligence, why don't we do something about natural stupidity?" The latter might be a tall order. But AI, it appears, just took one small step for robotkind. New research published June 14 in Science reports that for the first time scientists have developed a machine-learning system that can observe a particular scene from multiple angles and predict what it will look like from a new, never-before-observed angle. With further development the technology could lead to more autonomous robots in industrial and manufacturing settings.

This video will show you how to infer dimensions while reshaping a PyTorch tensor by using the PyTorch view operation. Then we print the PyTorch version we are using. Let's now create a PyTorch tensor for our example. We use torch.Tensor, we pass in our data structure which is 2x3x6, and we're going to assign this PyTorch tensor to the Python variable pt_initial_tensor_ex. Let's print the pt_initial_tensor_ex Python variable to see what we have. We see that it's a PyTorch FloatTensor of size 2x3x6, we have all our numbers from 1 to 36, inclusive, and we're going to use this tensor now to reshape it in a variety of ways and infer the shape.

We propose a very simple modification of gradient descent and stochastic gradient descent. We show that when applied to a variety of machine learning models including softmax regression, convolutional neural nets, generative adversarial nets, and deep reinforcement learning, this very simple surrogate can dramatically reduce the variance and improve the accuracy of the generalization. The new algorithm, (which depends on one nonnegative parameter) when applied to non-convex minimization, tends to avoid sharp local minima. Instead it seeks somewhat flatter local (and often global) minima. The method only involves preconditioning the gradient by the inverse of a tri-diagonal matrix that is positive definite. The motivation comes from the theory of Hamilton-Jacobi partial differential equations. This theory demonstrates that the new algorithm is almost the same as doing gradient descent on a new function which (a) has the same global minima as the original function and (b) is "more convex". Again, the programming effort in doing this is minimal, in cost, complexity and effort. We implement our algorithm into both PyTorch and Tensorflow platforms, which will be made publicly available.

To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is 'accumulative' and 'online' without interruption. However, despite the recent success, artificial neural networks (ANNs) must be trained offline, and they suffer catastrophic interference between old and new learning, indicating that ANNs' conventional learning algorithms may not be suitable for building intelligent agents comparable to our brain. In this study, we propose a novel neural network architecture (DynMat) consisting of dual learning systems, inspired by the complementary learning system (CLS) theory suggesting that the brain relies on short- and long-term learning systems to learn continuously. Our experiments show that 1) DynMat can learn a new class without catastrophic interference and 2) it does not strictly require offline training.

Regional language extraction from a natural scene image is always a challenging proposition due to its dependence on the text information extracted from Image. Text Extraction on the other hand varies on different lighting condition, arbitrary orientation, inadequate text information, heavy background influence over text and change of text appearance. This paper presents a novel unified method for tackling the above challenges. The proposed work uses an image correction and segmentation technique on the existing Text Detection Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses standard PVAnet architecture to select features and non maximal suppression to detect text from image. Text recognition is done using combined architecture of MaxOut convolution neural network (CNN) and Bidirectional long short term memory (LSTM) network. After recognizing text using the Deep Learning based approach, the native Languages are translated to English and tokenized using standard Text Tokenizers. The tokens that very likely represent a location is used to find the Global Positioning System (GPS) coordinates of the location and subsequently the regional languages spoken in that location is extracted. The proposed method is tested on a self generated dataset collected from Government of India dataset and experimented on Standard Dataset to evaluate the performance of the proposed technique. Comparative study with a few state-of-the-art methods on text detection, recognition and extraction of regional language from images shows that the proposed method outperforms the existing methods.

A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We show that well-known reinforcement learning (RL) methods can be adapted to learn robust control policies capable of imitating a broad range of example motion clips, while also learning complex recoveries, adapting to changes in morphology, and accomplishing user-specified goals. Our method handles keyframed motions, highly-dynamic actions such as motion-captured flips and spins, and retargeted motions. By combining a motion-imitation objective with a task objective, we can train characters that react intelligently in interactive settings, e.g., by walking in a desired direction or throwing a ball at a user-specified target. This approach thus combines the convenience and motion quality of using motion clips to define the desired style and appearance, with the flexibility and generality afforded by RL methods and physics-based animation. We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills. We demonstrate results using multiple characters (human, Atlas robot, bipedal dinosaur, dragon) and a large variety of skills, including locomotion, acrobatics, and martial arts.

An AI winter is a time when funding and support for artificial intelligence research and commercial ventures dries up. AI went through various winters in the 20th century, when early promise turned to disillusion, and the best technologists turned their minds to other problems. Though many definitions of AI are overly vague, it is an important technology that receives a lot of media attention. That means a lot of people have opinions about it even if they are unfamiliar with how it works and what its limits are. This post gives you a framework by which to interpret opinions about AI, to situate someone who holds an AI opinion stands and understand who their allies and opponents are.

Image inpainting, the task of filling in holes in an image, can be used in various applications. For instance, it can be used in image editing to delete unwanted content, while backfilling in the remaining space with related imagery. Researchers from NVIDIA expanded upon this by introducing a deep learning method inside a photo editing software that can handle gaps of any shape, size location, or distance from the image borders. Previous deep learning approaches have focused on rectangular areas located around the center of the image, and require expensive post-processing. With this new model, this team was the first to demonstrate the efficacy of deep learning image inpainting on irregularly shaped holes.