KUKA's Austin, Texas-based software development team for web, mobile and cloud technology was recently at the internationally-renowned festival of arts and technology, SXSW, with a focus on what it means to connect robots to the cloud and harness the power in their data to make them "smarter" at what they do. In particular, for industrial robots working in factory environments, using this data to optimize operation, spot potential service issues before they arise, and feed deep learning processes is one of the foundations for the smart factories of the future.

In 2001, a group of Physicists were awarded the Nobel prize in Physics for creating an experiment that produced the Bose Einstein Condensate(BEC). The BEC is a state of Matter in an extremely cold state, close to absolute zero(that is, very near 0 K or 273.15 C), first theorized by Satyendra Nath Bose and Albert Einstein in 1925. In the 2001 Noble Prize winning experiment, the physicists created the first BEC in a lab by shooting multiple lasers at Gas particles from different directions. After meticulous calculations and planning, they carefully calibrated a series of lasers to achieve this.

Samsung's ambitions to be a player in the self-driving car space aren't new, but today the company hit a big milestone. The company's home country of South Korea has approved Samsung's plans to test self-driving cars on real roads, not just test courses. Instead, the company is working on developing sensors and computer parts that are backed up by its artificial intelligence and deep learning software. The car itself that'll be hitting the road is a Hyundai customized with Samsung's own gear, but little else is known about exactly what sort of technology Samsung is providing. It sounds like a similar situation to Apple's own self-driving car that hit the road in Silicon Valley recently -- though Apple's Lexus is using plenty of off-the-shelf parts right now, it also likely contains some custom hardware of software on board.

The self-driving car market is set to reach $42 billion by 2025 and a new report has revealed that Samsung wants a piece of the action. The South Korean electronics maker has recently been approved to test it deep-learning based autonomous vehicles on public roads. Although the firm has been very quiet about the project, it has developed a'commercialized Hyundai vehicle equipped with the latest cameras and sensors' that will be used during testing. Samsung received approved to test it deep-learning based autonomous vehicles on public roads. It plans to use a'commercialized Hyundai vehicle (stock image) equipped with the latest cameras and sensors' that will be used during testing The news was first shared online by The Korea Hearald, which said Samsung received its approval from the South Korean Ministry of Land, Infrastructure and Transport.

Compared with traditional machine learning models, neural networks are computationally more complex and introduce many additional parameters. This often prevents machine learning engineers and data scientists from getting the best performance from their models. In some cases, it might even dissuade data scientists from using neural networks. In this post, we show how to tune a Convolutional Neural Network (CNN) for a Natural Language Processing (NLP) task 400 times faster than with traditional random search on a CPU. Additionally, this method also achieves greater accuracy.

"AI teaches us who we are," says Richard Socher. The recent rapid progress in the field of artificial intelligence is the result of successfully processing "a large amount of known training data, doing things [the computer] has seen before," he says. Unlike humans, computers cannot create something new and unique. Human creativity has been the driving force behind scientific and engineering advances, including making computers do more human-like activities such as identifying objects or words. Socher's creativity, his ability to come up with new approaches to solving computers' language and visual processing challenges, has made him a rising star of the deep learning movement that has spawned exciting new applications of artificial intelligence.

Sure, there are lots of tutorials and overviews on gaining the insight you need into picking up machine learning, but many (most?) of them take the long view: get a foundation first, learn the basics next, then learn a bit of complementary theory before getting too far ahead of yourself in practical terms, take a step back, try your hand at a few examples, undertake a project on your own... This is all great advice, and a great approach to learning... well, almost anything. But let's say you're not starting from scratch. Or you don't have the patience to go through all of the motions. Let's say you want to hit the ground running and scramble under pressure to learn everything right now.

Though further developments are underway, Google said on April 27 that it has successfully developed new deep learning algorithms that can detect and diagnose diabetic retinopathy, an eye disease which can lead to blindness, as well as locate breast cancer. Lily Peng, product manager of the medical imaging team at Google Research, shared how the US tech giant is using deep learning to train machines to analyze medical images and automatically detect pathological cues, be it swollen blood vessels in the eye or cancerous tumors, during a video conference with the South Korean media hosted by Google Korea. Based on the workings of the human brain, deep learning uses large artificial neural networks -- layers of interconnected nodes -- that rearrange themselves as new information comes in, allowing computers to self-learn without the need for human programming. "Artificial neural networks have been around since the 1960s. But now with more powerful computing power, we can build more layers into the system to handle more complicated tasks with high accuracy," Peng said. "In deep learning, the feature engineering is handled by the computer itself.

As buzzwords become ubiquitous they become easier to tune out. We've finely honed this defense mechanism, for good purpose. It's better to focus on what's in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn't help you. VR could eat all media, but it's hardware requirements keep it many years away from common use. Yes, machine learning will help us build wonderful applications.

If there's ever a time you want to spend less time under the knife, it's during brain surgery. Artificial intelligence could help doctors diagnose brain tumors more quickly and more accurately, according to a new study by researchers at the University of Michigan Medical School and Harvard University. "Our goal is to develop an algorithm that approaches the performance of a neuropathologist at diagnosis during an operation," said Dr. Daniel Orringer, first author of the study in Nature Biomedical Engineering and an assistant professor of neurosurgery at Michigan Medicine. In their experiments on more than 100 brain tissue samples, the researchers used deep learning to detect the presence of a tumor and classify it into one of several broad categories. The algorithm analyzes tissue from a laser imaging technique the researchers developed called stimulated Raman histology, or SRH.