Most autonomous vehicle tech ventures such as Waymo, GM Cruise and Nvidia rack up miles of deep learning experience to build reliably safe systems for self-driving cars. Cortica and Renesas Electronics' entirely different approach focuses on helping cars learn on their own. Cortica, an Israeli company with roots in predictive artificial intelligence based on visual perception, is embedding its latest "Autonomous A.I." solution on the Renesas R-Car V3H system-on-chip (SoC) solution for self-driving cars. Referred to by the companies as "unsupervised learning," Cortica's autonomous A.I. enables a vehicle to make predictions based on visual data received from forward-facing cameras. According to Cortica, the system uses "'unsupervised learning' methodology to mimic the way humans experience and incorporate the world around them."

Human-robot interaction is easy to do badly, and very difficult to do well. One approach that has worked well for robots from R2-D2 to Kuri is to avoid the problem of language--rather than use real words to communicate with humans, you can do pretty well (on an emotional level, at least) with a variety of bleeps and bloops. But as anyone who's watched Star Wars knows, R2-D2 really has a lot going on with the noises that it makes, and those noises were carefully designed to be both expressive and responsive. Most actual robots don't have the luxury of a professional sound team (and as much post-production editing as you need), so the question becomes how to teach a robot to make the right noises at the right times. At Georgia Tech's Center for Music Technology (GTCMT), Gil Weinberg and his students have a lot of experience with robots that make noise of various sorts, and they've used a new deep learning-based technique to teach their musical robot Shimi a basic understanding of human emotions, and how to communicate back to those humans in just the right way, using music.

Machine learning has brought an old idea -- neural networks -- to bear on a range of previously difficult problems such as handwriting and speech recognition. Better software and hardware has made it feasible to apply sophisticated machine learning algorithms that would have previously been only possible on giant supercomputers. However, there's still a learning curve for developing both models and software to use these trained models. Uber -- you know, the guys that drive you home when you've had a bit too much -- have what they are calling a "code-free deep learning toolbox" named Ludwig. The promise is you can create, train, and use models to extract features from data without writing any code.

Our flagship product, Viz LVO, leverages advanced deep learning to communicate time-sensitive information about stroke patients straight to a specialist who can intervene and treat. In February 2018, the U.S. Food and Drug Administration (FDA) granted a De Novo clearance for Viz LVO, the first-ever computer-aided triage and notification platform to identify LVO strokes in CTA imaging. Most recently, Viz.ai announced its second FDA clearance for Viz CTP through the 510(k) pathway, offering healthcare providers an important tool for automated cerebral image analysis.

Today the Apache MXNet community is excited to announce the 1.4.0 release of the Apache MXNet deep learning framework. We would like to thank the Apache MXNet community for all their contributions towards this power packed v1.4 release. This release brings Java bindings for inference, Julia bindings, experimental control flow operators, JVM memory management, Horovod integration for distributed training, graph optimization and quantizations, and many more features and under-the-hood performance enhancements and usability improvements. Check out the full release highlights on MXNet v1.4 release notes. This release includes new Java Inference APIs which offer easy to use, idiomatic high level APIs for performing predictions in Java with deep learning models trained using MXNet.

News: Straight Dope is growing up. Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/. This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code.

The inputs, outputs, and transformations within neural networks are all represented using tensors, and as a result, neural network programming utilizes tensors heavily. The concept of a tensor is a mathematical generalization of other more specific concepts. Let's look at some specific instances of tensors. Let's organize the above list of example tensors into two groups: The first group of three terms (number, array, 2d-array) are terms that are typically used in computer science, while the second group (scalar, vector, matrix) are terms that are typically used in mathematics. We often see this kind of thing where different areas of study use different words for the same concept.

In this tutorial, you will learn how to apply Holistically-Nested Edge Detection (HED) with OpenCV and Deep Learning. We'll apply Holistically-Nested Edge Detection to both images and video streams, followed by comparing the results to OpenCV's standard Canny edge detector. Edge detection enables us to find the boundaries of objects in images and was one of the first applied use cases of image processing and computer vision. When it comes to edge detection with OpenCV you'll most likely utilize the Canny edge detector; however, there are a few problems with the Canny edge detector, namely: Holistically-Nested Edge Detection (HED) attempts to address the limitations of the Canny edge detector through an end-to-end deep neural network. This network accepts an RGB image as an input and then produces an edge map as an output.

Artificial intelligence that's beastly at World of Warcraft might not lie too far into the distant future, if OpenAI has its way. The San Francisco research nonprofit today released Neural MMO, a "massively multiagent" virtual training ground that plops agents in the middle of an RPG-like world -- one complete with a resource collection mechanic and player versus player combat. "The game genre of Massively Multiplayer Online Games (MMOs) simulates a large ecosystem of a variable number of players competing in persistent and extensive environments," OpenAI wrote in a blog post. "The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence." AI agents spawn randomly in Neural MMO environments, which contain automatically generated tile maps of a prespecified size. Some tiles are traversable, like "forest" (which bears food) and "grass," while others aren't (such as water and stone).

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