NVIDIA is busy developing Riva Studio which will help in building applications such as chatbots, virtual assistants and multimodal virtual assistants that leverage Riva skills. This eludes to the fact that NVIDIA will have a skills approach, like Microsoft, IBM etc. Looking at the demo examples, it could stand NVIDIA in good stead to have a more disparate approach in dialog state management. I have written in the past how a Riva dialog state management environment might look. Points 1 & 2, and 3 & 4 are usually tightly coupled.

It is targeted to those looking for an out-of-the-box solution to test the Machine Learning and Artificial Intelligence capabilities of NVIDIA Jetson boards. It offers a portable and easy-to-use environment that can be executed on any NVIDIA Jetson board. The project includes a set of example applications that exercise ML and AI frameworks such as DeepStream, OpenCV, GStreamer, GstInference, among others. Each example comes with the source code and they are implemented in different languages and using different frameworks (bash scripts, python, C/C, GStreamer, etc). You can find the example that is closer to your use case, and use it as a starting point for your demos and exploratory tests.

AWS provides several Artificial Intelligence (AI) services. With AI services, you could implement some useful AI things: image and video analysis, document analysis, text to speech or speech to text translation, and so on. However, those AWS services can be used not only for enterprise applications but for your self-development applications. Applying these services we are able to implement an application to improve our foreign language skills. Let's map AWS AI services to language skills: It doesn't cover all skills but we could develop some of them this way.

With so many cloud applications infused with artificial intelligence (AI) and machine learning (ML) capabilities, AI/ML is being democratized by cloud services. The growth of AI in a wide range of applications demands more purpose-built processors to provide scalable levels of performance, flexibility, and efficiency. The LG AIoT board helps customers accelerate their computer vision and ML journey using Amazon Web Services (AWS). OEMs can now easily incorporate visual intelligence, voice intelligence, and control intelligence into their products. The LG Neural Engine (LNE) in the LG AIoT board offloads the compute requirements of deep learning algorithms to the specially designed processor, which delivers 1 TFLOPS of compute performance.

Over the last year or two there has been a flood of custom silicon intended to speed up machine learning on the edge. First to arrive was Intel with their Moividius-based hardware, and more recently we've seen the appearance of Google's Edge TPU-based hardware. However traditionally NVIDIA's offering in this space -- built as it was around their GPU-based hardware -- has been higher powered, and comparatively expensive. However, with everyone moving towards the edge it is perhaps unsurprising to see them introduce something a bit more affordable. So last month we saw the arrival of the Jetson Nano Module and Developer Kit. Still based around their existing GPU technology, the new Jetson Nano is therefore "upwards compatible" with the much more expensive Jetson TX and AGV Xavier boards. The Jetson Nano Developer Kit arrives in yet another unassuming box. Inside the box is the carrier board itself with the Jetson Nano module and a heatsink already fitted.

Machine learning and deep learning, in particular, are developing at amazing speeds. Today, machine learning can be used to solve ever more complex tasks that have been considered impractical just a few years ago. Examples include autonomous cars, AlphaGo's win against the world's Go champion, the photo-realistic transformation of pictures, and neural machine translation systems. In this blog post, we describe a simple system to recognize monetary amounts on Swiss payment slips. The user interface is implemented using Eclipse Scout and we build, train, and run the deep neural net using Deeplearning4j.

The createPairwiseMatches() function shown in Figure 7 above, extracts features in parallel mapping images to vertical and horizontal luminosity histograms. Furthermore, the histograms for each image are saved in a hash table for quick reference since each image's features will be repeatedly matched to other images. Once the match features are extracted, the match is immediately placed in a thread safe blocking collection for further downstream reduction processing. While the mapping functions shown in Figure 7 are executing in a background thread, parallel reduce functions simultaneously execute processing each completed match produced to calculate the similarity between the match images.