Our previous article'Why Deep Learning is a Design Challenge' is the preface to this article. Every experienced engineer and designer will tell you that process is everything. Instead of expecting a Eureka moment to develop the best possible product, projects require a lot of methodologies, experiments and rigorous problem solving. Design tools offer solutions to these challenges, by building an understanding of processes and users. For deep learning specifically, design methodologies can lead to enhanced explainability of processes and overall improvement of their performance.

A decade ago, artificial intelligence was little more than a Hollywood idea popularised (and demonised) only in movies, but the concept of using machines to solve business problems is fast becoming a reality. As such, it came as no surprise that discussions around AI littered around most conferences today. With the dizzying hype, it's difficult to make sense of AI and truly understand the difference it can make to business. Drew Perez, managing director of Adatos, a company at the frontier of using AI for enterprise applications said during a recent conference that the fear plaguing some western tech thinkers is: "What if a machine consumed the fruit of knowledge, discerned the difference between good and evil and made decisions from it, irreversibly replacing humans?" The reality is AI, is constantly evolving, and instead of holding onto perceptions of us (humans) vs them (machines) dichotomy, adopting a view that man and machine having a symbiotic relationship can make the benefits more tangible and the consequences a little less biblical.

LUCKNOW: Keeping in mind the high burden of heart patients, authorities at King George's Medical University are looking at installation of artificial intelligence-based patient triage technology (AIPTT) kiosks in the out-patient department. This intelligent touch-screen interface, comparable to the online check-in kiosks at airports, will help patients record the nature of problem faced by them, thereby saving about 40% of doctors' time. A team of doctors and representatives from technology companies including one from California visited KGMU on Saturday to study the needs of the institution. The system is being successfully used in some developed countries. In the cardiology department, a single doctor is expected to attend to 400-500 patients everyday.

What would you think if I told you IBM was the biggest ad agency in the world in 2015. Perhaps roll your eyes and ask what the punchline is? IBM was the largest global ad agency in 2015. They had US $1.9 billion dollars in revenue. Now I imagine you are asking yourself, how the heck?

During our May Applied AI Insiders Series event, we will explore practical ways AI is being applied to different Human/Computer interfaces, moving us beyond touch and closer to how we interact with other humans – using speech, gestures and even body language. This event is INVITE ONLY. If you did not receive an invitation you can request one below. A long-standing goal of human-computer interaction has been to enable people to have a natural conversation with computers, as they would with each other. In recent years, we have witnessed a revolution in the ability of computers to understand and to generate natural speech, especially with the application of deep neural networks.

The U.S. military has been looking to incorporate elements of artificial intelligence and machine learning into its drone program. Project Maven, as the effort is known, aims to provide some relief to military analysts who are part of the war against Islamic State. These analysts currently spend long hours staring at big screens reviewing video feeds from drones as part of the hunt for insurgents in places like Iraq and Afghanistan. The Pentagon is trying to develop algorithms that would sort through the material and alert analysts to important finds, according to Air Force Lieutenant General John N.T. 'Jack' Shanahan, director for defense intelligence for warfighting support. Military bosses say intelligence analysts are'overwhelmed' by the amount of video being recorded over the battlefield by drones with high resolution cameras'A lot of times these things are flying around(and)... there's nothing in the scene that's of interest,' he told Reuters.

Recently, I gave a talk at the O'Reilly AI conference in Beijing about some of the interesting lessons we've learned in the world of NLP. While there, I was lucky enough to attend a tutorial on Deep Reinforcement Learning (Deep RL) from scratch by Unity Technologies. I thought that the session, led by Arthur Juliani, was extremely informative and wanted to share some big takeaways below. In our conversations with companies, we've seen a rise of interesting Deep RL applications, tools and results. In parallel, the inner workings and applications of Deep RL, such as AlphaGo pictured above, can often seem esoteric and hard to understand.

This study targets to automatically annotate on arrhythmia by deep network. The investigated types include sinus rhythm, asystole (Asys), supraventricular tachycardia (Tachy), ventricular flutter or fibrillation (VF/VFL), ventricular tachycardia (VT). Methods: 13s limb lead ECG chunks from MIT malignant ventricular arrhythmia database (VFDB) and MIT normal sinus rhythm database were partitioned into subsets for 5-fold cross validation. These signals were resampled to 200Hz, filtered to remove baseline wandering, projected to 2D gray spectrum and then fed into a deep network with brand-new structure. In this network, a feature vector for a single time point was retrieved by residual layers, from which latent representation was extracted by variational autoencoder (VAE). These front portions were trained to meet a certain threshold in loss function, then fixed while training procedure switched to remaining bidirectional recurrent neural network (RNN), the very portions to predict an arrhythmia category. Attention windows were polynomial lumped on RNN outputs for learning from details to outlines. And over sampling was employed for imbalanced data. The trained model was wrapped into docker image for deployment in edge or cloud. Conclusion: Promising sensitivities were achieved in four arrhythmias and good precision rates in two ventricular arrhythmias were also observed. Moreover, it was proven that latent representation by VAE, can significantly boost the speed of convergence and accuracy.

This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neonatal brain health to all neonatal healthcare professionals, particularly those without EEG interpretation expertise. The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG. The proposed system uses a low-cost and low-power EEG acquisition system. An Android app provides single-channel EEG visualization, traffic-light indication of the presence of neonatal seizures provided by a trained, deep convolutional neural network and an algorithm for EEG sonification, designed to facilitate the perception of changes in EEG morphology specific to neonatal seizures. The multifaceted EEG interpretation framework is presented and the implemented mobile platform architecture is analyzed with respect to its power consumption and accuracy.

An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper. The solution of this ridge regression with stretchable parameters is given in primal and dual spaces and in closed-form. Essentially, the proposed solution stretches the covariance computation by a power term, thereby compressing or amplifying the estimation parameters. To maintain the computation of power root terms within the real space, an input transformation is proposed. The results of an empirical evaluation in both synthetic and real-world data illustrate that the proposed method is effective for compressive learning with high-dimensional data.