Deep learning and Artificial Intelligence (AI) have moved well beyond science fiction into the cutting edge of internet and enterprise computing. Access to more computational power in the cloud, advancement of sophisticated algorithms, and the availability of funding are unlocking new possibilities unimaginable just five years ago. But it's the availability of new, rich data sources that is making deep learning real. If you want to get started right now, download the complete Deep Learning and Artificial Intelligence white paper. We are living in an era where artificial intelligence (AI) has started to scratch the surface of its true potential.

You've probably already seen one or more object recognition demos, where a system equipped with a camera detects the type of object using deep learning algorithms either locally or in the cloud. It's for example used in autonomous cars to detect pedestrian, pets, other cars and so on. Kochi Nakamura and his team have developed software based on GoogleNet deep neural network with a a 1000-class image classification model running on Raspberry Pi Zero and Raspberry Pi 3 and leveraging the VideoCore IV GPU found in Broadcom BCM283x processor in order to detect objects faster than with the CPU, more exactly about 3 times faster than using the four Cortex A53 cores in RPi 3. They just connected a battery, a display, and the official Raspberry Pi camera to the Raspberry Pi boards to be able to recognize various objects and animals. Not yet, but he is thinking about it, and when/if it is released it will probably be found on his github account, where there is already py-videocore Python library for GPGPU on Raspberry Pi, which was very likely used in the demos above.

You have to take the stairs to shatter the glass ceiling.

When Bitfusion launched at Disrupt NY 2015, its focus was on helping developers speed up their applications by giving them pre-compiled libraries that made better use of GPUs, FPGAs and other co-processing technologies. That was two years ago. Today, the hottest market for these technologies is in training deep learning models, something that was barely on the radar when the company launched. Unsurprisingly, though, that's exactly what Bitfusion is focusing on now. As the company announced today, it has raised a $5 million Series A round led by Vanedge Capital, with participation from new investor Sierra Ventures and existing investors Data Collective, Resonant VC and Geekdom.

This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the different sub-tasks. The algorithm reached the 3rd place on the 5-label classification task (sub-task C).

In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees. Do I need some Skynet to run it?

Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. However, large-scale clusters are being asked to operate in different ways, namely by chewing on large-scale deep learning workloads--and this requires a specialized approach to get high utilization, efficiency, and performance. Nearly all of the cluster management tools from the high performance computing community are being bent in the machine learning direction, but for production deep learning shops, there appears to be a DIY tendency. This is not as complicated as it might sound, given the range of container-based open source tools, and such a homegrown approach can bake in tunings for specific frameworks and internal applications. The lack of a sufficiently robust cluster manager for a large-scale cluster handling large machine learning workloads pushed researchers at the Chinese machine learning giant, Sensetime, to build their own.

Samsung was granted permission by South Korea's Ministry of Land, Infrastructure and Transport to test run self-driving cars, Yonhap News reported. Samsung's autonomous vehicle is apparently under development and based on deep-learning technology, the report said. The approval Monday by officials will allow the company to test the self-driving vehicle on the roads. Samsung wants to develop top-of-the-line sensors and computer modules backed by artificial intelligence and deep-learning technologies. That technology would improve vehicles' self-driving capabilities, even in bad weather conditions, the report said.

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains. Background Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. Also, after this list comes out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap, has been created and loved by many deep learning researchers. Although the Roadmap List includes lots of important deep learning papers, it feels overwhelming for me to read them all.

Donnelly Centre researchers have developed a deep learning algorithm that can track proteins, to help reveal what makes cells healthy and what goes wrong in disease. "We can learn so much by looking at images of cells: how does the protein look under normal conditions and do they look different in cells that carry genetic mutations or when we expose cells to drugs or other chemical reagents? People have tried to manually assess what's going on with their data but that takes a lot of time," says Benjamin Grys, a graduate student in molecular genetics and a co-author on the study. Dubbed DeepLoc, the algorithm can recognize patterns in the cell made by proteins better and much faster than the human eye or previous computer vision-based approaches. In the cover story of the latest issue of Molecular Systems Biology, teams led by Professors Brenda Andrews and Charles Boone of the Donnelly Centre and the Department of Molecular Genetics, also describe DeepLoc's ability to process images from other labs, illustrating its potential for wider use.