Besides image recognition, CNNs have been applied to natural language processing, drug discovery, and playing Go. Natural language processing (NLP) is another major application area for deep learning. In addition to the machine translation problem addressed by Google Translate, major NLP tasks include automatic summarization, co-reference resolution, discourse analysis, morphological segmentation, named entity recognition, natural language generation, natural language understanding, part-of-speech tagging, sentiment analysis, and speech recognition. In addition to CNNs, NLP tasks are often addressed with recurrent neural networks (RNNs), which include the Long Short Term Memory (LSTM) model. As I mentioned earlier, in recurrent neural networks, neurons can influence themselves, either directly, or indirectly through the next layer.

We are excited to share our recent work on supporting a recurrent neural network (RNN). We did not support RNN models at our open source launch in April. So, over the last several months, we have developed state-of-the-art RNN building blocks to support RNN use cases (machine translation and speech recognition, for example). Using Caffe2, we significantly improved the efficiency and quality of machine translation systems at Facebook. We got an efficiency boost of 2.5x, which allows us to deploy neural machine translation models into production.

"AI is the new Electricity" – Andrew Ng* Since the advent of 20th century electricity became the main source of invention in every major industry ranging from transportation, manufacturing to healthcare, communications and many more. Today Artificial Intelligence (AI) is bringing the same big transformation across all the major industries. The part of AI that is rapidly growing and which is driving most of these transformations is Deep Learning. Today, Deep Learning has become one of the most sought after skills in the technology world. Agriculture is one industry where Deep Learning scientists and researchers are working with farmers to help them with their produce.

It's become a custom for some protesters to cover their faces during public demonstrations. Now, it seems, technology could outwit them: a team of engineers has created an algorithm that can identify faces that are partially covered. The algorithm identifies faces using angles at 14 different points on the face, according to a paper published on the preprint server arXiv to be presented at the IEEE International Conference on Computer Vision Workshops in October. The researchers trained and validated the algorithm, which relies on a form of artificial intelligence called deep learning, using a dataset of 1500 images of 25 human faces. Each face was partially obscured by one or more of ten disguises (such as sunglasses, a face scarf, or a hat) and eight complex backgrounds to simulate real-world photos.

Recently we wrote about the development of AI and neural nets beyond the second generation Convolutional and Recurrent Neural Nets (CNNs / RNNs) which have come on so strong and dominate the current conversation about deep learning. The original charge by DARPA's SyNAPSE program has spread the work among many of these labs including Sandia, Oak Ridge, and Lawrence Livermore. "Neuromorphic computing is still in its beginning stages," says Dr. Catherine Schuman, a researcher working on such architectures at Oak Ridge National Laboratory. State of the art on the hardware side appears to still belong to IBM which recently delivered a supercomputing platform based on TrueNorth to Lawrence Livermore Lab with the equivalent of 16 million neurons and 4 billion synapses.

To the best of our knowledge, this is the first list of deep learning papers on medical applications. There are couple of lists for deep learning papers in general, or computer vision, for example Awesome Deep Learning Papers. In this list, I try to classify the papers based on their deep learning techniques and learning methodology. I believe this list could be a good starting point for DL researchers on Medical Applications.

Artificial intelligence (AI) promises to revolutionise our lives, drive our cars, diagnose our health problems, and lead us into a new future where thinking machines do things that we're yet to imagine. Even billionaire entrepreneur Elon Musk, who admits he has access to some of the most cutting-edge AI, said recently that without some regulation "AI is a fundamental risk to the existence of human civilization". So what is the future of AI? Michael Milford and Peter Stratton are both heavily involved in AI research and they have different views on how it will impact on our lives in the future. How widespread is artificial intelligence today? Answering this question depends on what you consider to be "artificial intelligence".

IBM said on Thursday it will spend $240 million over the next decade to fund a new artificial intelligence research lab at the Massachusetts Institute of Technology. The resulting MIT–IBM Watson AI Lab will focus on a handful of key AI areas including the development of new "deep learning" algorithms. Deep learning is a subset of AI that aims to bring human-like learning capabilities to computers so they can operate more autonomously. The Cambridge, Mass.-based lab will be led by Dario Gil, vice president of AI for IBM Research and Anantha Chandrakasan, dean of MIT's engineering school. It will draw upon about 100 researchers from IBM (ibm) itself and the university.

Apart from performing for our clients, InData Labs data science team is keen on taking part in top notch data science competitions, for example, Kaggle Competition. The team has recently shown one of the best results in Quora Question Pairs Challenge on Kaggle. The challenge is remarkable for a number of interesting findings and controversies among the participants, so let's dig deeper into the details of the competition and create a winning formula for data science and machine learning Kaggle competition. Quora is a Q&A site where anyone can ask questions and get answers. Quora audience is quite diverse. People use it for studying, work consultations and whenever they have second thoughts about almost anything.

According to the new market research report "Automotive Artificial Intelligence Market by Offering (Hardware, Software), Technology (Deep Learning, Machine Learning, Computer Vision, Context Awareness and Natural Language Processing), Process, Application and Region - Global Forecast to 2025", published by MarketsandMarkets, the automotive artificial intelligence (AI) market report, the market is expected to be valued at USD 782.9 Million in 2017 and is expected to reach USD 10,573.3 Browse 66 Market Data Tables and 66 Figures spread through 203 Pages and in-depth TOC on "Automotive Artificial Intelligence Market - Global Forecast to 2025" The emergence of autonomous vehicle and industry-wide standards such as the adaptive cruise control (ACC), blind spot alert, and advanced driver assistance systems (ADAS) would trigger the growth of the automotive AI market. The growing demand for convenience and safety also presents an opportunity for OEMs to develop new and innovative artificial intelligence systems that would attract customers. Software holds a major share of the overall AI market in the automotive industry because of the various developments of AI software and related development kits. In the recent years, major developments have occurred in AI software solutions, platforms, and related software development kits.