Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves. In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.

The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters.

Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions. Mostly, the existing methods are based on machine learning with knowledge-based learning. This working note presents the Recurrent neural network (RNN) and Long short-term memory (LSTM) based embedding for automatic health text classification in the social media mining. For each task, two systems are built and that classify the tweet at the tweet level. RNN and LSTM are used for extracting features and non-linear activation function at the last layer facilitates to distinguish the tweets of different categories. The experiments are conducted on 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017. The experiment results are considerable; however the proposed method is appropriate for the health text classification. This is primarily due to the reason that, it doesn't rely on any feature engineering mechanisms.

We develop an algorithm for systematic design of a large artificial neural network using a progression property. We find that some non-linear functions, such as the rectifier linear unit and its derivatives, hold the property. The systematic design addresses the choice of network size and regularization of parameters. The number of nodes and layers in network increases in progression with the objective of consistently reducing an appropriate cost. Each layer is optimized at a time, where appropriate parameters are learned using convex optimization. Regularization parameters for convex optimization do not need a significant manual effort for tuning. We also use random instances for some weight matrices, and that helps to reduce the number of parameters we learn. The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers. This expectation is verified by extensive experiments for classification and regression problems, using standard databases.

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the `raw' clinical time series data is used as input features to the models.

Diagnosing disease is one of the more labor-intensive aspects of the healthcare system. It also happens to be one that is particularly well-suited to being performed by machine learning algorithms. While work in this area is in its early stages, the technology is evolving rapidly and appears poised to transform diagnostic medicine. Thanks largely to the huge volumes of data collected from patients, medical diagnostics is an ideal domain for machine learning. Much of the diagnostic data is image-based, such as X-rays, MRI scans, and ultrasound imagery, but can also include things like genomic profiles, epidemiological data, blood tests, biopsy results, and even medical research papers. As a result, there is a wealth of data available for training neural networks and for other machine learning techniques.

Yang Fan, managing director of SenseTime said the company is dedicated to spearheading research and development in deep learning in face recognition systems. As well as human face recognition, SenseTime is developing security technology focused on text and characters, body shapes and vehicles. "Human face recognition is the most mature part of this type of technology," said Yang. "We can provide complete image and video analysis, too, and content extraction technology. Using those, we are heavily involved in working with the security and surveillance, finance, education and robotics industries". The two-year-old Beijing startup already has a numerous highly trained scientists onboard, including graduates from Massachusetts Institute of Technology and Stanford University in the United States, the University of Hong Kong and the Chinese University of Hong Kong, as well those who have worked for international technology leaders such as Google, Microsoft, Baidu and Lenovo.

Security firm McAfee has announced new endpoint and cloud offerings at the MPOWER Cybersecurity Summit in Las Vegas. The move is part of McAfee's drive to go beyond machine learning to take advantage of the speed and accuracy of advanced analytics, deep learning and artificial intelligence (AI), and increase the efficiency of security operations. From a hacker perspective, many organisations are still leaving the front door open and the windows unlocked. Failure to protect and handle data correctly can also result in punitive actions for companies participating in the digital economy. Wake up and get the knowledge to get protected.

You should probably be thrilled to be alive these days. Every week brilliant researchers present new breakthroughs in scientific papers that extend our understanding of who we are (groundbreaking accuracy of CRISPR/Cas9 genome editing), how the universe began (the recent discovery of gravitational waves), and what computers can learn (phenomenal advances in deep-learning techniques). Yet, as a regular consumer of scientific research, I'm frustrated that the current knowledge comes in puzzle pieces: a research paper presented in a conference here and another recommended by a friend there. Existing tools aren't particularly helpful when it comes to solving this problem. Finding relevant academic studies can take days, sometimes even weeks as Google Scholar, Microsoft Academic, and other existing tools give millions of results, the vast majority of which are not valuable to your search.

Text classification describes a general class of problems such as predicting the sentiment of tweets and movie reviews, as well as classifying email as spam or not. Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems. In this post, you will discover some best practices to consider when developing deep learning models for text classification. Best Practices for Document Classification with Deep Learning Photo by storebukkebruse, some rights reserved. The modus operandi for text classification involves the use of a word embedding for representing words and a Convolutional Neural Network (CNN) for learning how to discriminate documents on classification problems.