A trio of researchers from Chonnam National University, Nanjing University of Information Science and Technology and the Chinese Academy of Sciences has found that a deep learning convolutional neural network was able to accurately predict El Niño events up to 18 months in advance. In their paper published in the journal Nature, Yoo-Geun Ham, Jeong-Hwan Kim and Jing-Jia Luo, describe their deep learning application, how it was trained and how well it worked in predicting El Niño events. El Niño-Southern Oscillation events are periods during which water warms above normal temperatures in tropical parts of the Pacific. When that warm water moves east, it leads to more rainfall and other weather events, such as hurricanes, in the Americas, and less rain in Australia and Indonesia. Current models can accurately predict such events using data from water temperature gauges spread across the globe up to a year in advance.

Having first appeared on the scene of machine learning in 1986 and artificial neural networks in 2000, the study of deep learning continues to explode with new research, advanced techniques, higher benchmarks, and broader applications. Keeping pace in such an active field with an average of 30 new deep learning papers uploaded to arXiv per day over the previous month is daunting, to say the least. While there are many key deep learning scientists and engineers active today, the following list of 12 researchers and innovators in the field are among the most important – and they so happen to actively share on social media, making their progress and insights much easier to keep up with. So, start paying attention to these 12 top deep learning individuals, and be prepared to expand your understanding and awareness of the incredible advancements deep learning is bringing to science, industry, and society. While it in no way correlates to everyone's contribution to the field, the list is sorted by the number of Twitter followers so you can see who appears to have the most reach today.

Almost two years ago, Dennis Degray sent an unusual text message to his friend. "You are holding in your hand the very first text message ever sent from the neurons of one mind to the mobile device of another," he recalls it read. Degray, 66, has been paralysed from the collarbones down since an unlucky fall over a decade ago. He was able to send the message because in 2016 he had two tiny squares of silicon with protruding metal electrodes surgically implanted in his motor cortex, the part of the brain that controls movement. By imagining moving a joystick with his hand, he is able to move a cursor to select letters on a screen.

Researchers at the Queensland University of Technology (QUT) have applied artificial intelligence (AI) to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases. In the study, the group of researchers explored a range of deep learning techniques to analyse Optical Coherence Tomography (OCT) images, said David Alonso-Caneiro, QUT senior research fellow and study lead author. OCT, which takes cross-sectional images of the eye to show different tissue layers, is a common instrument used by optometrists and ophthalmologists. These images are around four microns in size and can help clinicians detect eye diseases such as glaucoma and age-related macular degeneration. The team collected OCT chorio-retinal eye scans from an 18-month longitudinal study of 101 children with good vision and healthy eyes, and used these images to train the AI program to detect patterns and define the choroid boundaries.

Researchers at the Queensland University of Technology (QUT) have applied artificial intelligence (AI) to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases. In the study, the group of researchers explored a range of deep learning techniques to analyse Optical Coherence Tomography (OCT) images, said David Alonso-Caneiro, QUT senior research fellow and study lead author. OCT, which takes cross-sectional images of the eye to show different tissue layers, is a common instrument used by optometrists and ophthalmologists. These images are around four microns in size and can help clinicians detect eye diseases such as glaucoma and age-related macular degeneration. The team collected OCT chorio-retinal eye scans from an 18-month longitudinal study of 101 children with good vision and healthy eyes, and used these images to train the AI program to detect patterns and define the choroid boundaries.

Brisbane startup FloodMapp has raised $1.3 million, as it looks to take its flood-prediction tech to the rest of Australia, and into hurricane-prone areas of the US. The funding comes from several VC firms, including Allectus Capital, Transition Level Investments, Jelix Ventures and Mercurian, as well as from a number of individual investors. Founded by Juliette Murphy and Ryan Prosser, FloodMapp combines big data analytics and machine learning techniques with traditional hydrology and hydraulic modelling approaches. The tech measures river height and rainfall data in real-time, and uses underlying elevation and topography to predict how and where water will flow over the land, Murphy tells StartupSmart. This allows the team to "predict a map of the inundated areas" and share that data with third parties.

Companies around the world are expecting to apply artificial intelligence (AI) within their companies in the next few years but are lagging in discussions of the ethics around it, research from Genesys finds. More than half of the employers questioned in a multi-country opinion survey say their companies do not currently have a written policy on the ethical use of AI or bots, although 21% expressed a definite concern that their companies could use AI in an unethical manner. "As a company delivering numerous customer experience solutions enabled by AI, we understand this technology has great potential that also comes with tremendous responsibility," said Steve Leeson, VP UK & Ireland, Genesys. "This research gives us important insight into how businesses and their employees are really thinking about the implications of AI – and where we as a technology community can help them steer an ethical path forward in its use." The research findings stem from opinion surveys sponsored by Genesys (www.genesys.com),

Current cameras are capable of recording high resolution video. While viewing on a mobile device, a user can manually zoom into this high resolution video to get more detailed view of objects and activities. However, manual zooming is not suitable for surveillance and monitoring. It is tiring to continuously keep zooming into various regions of the video. Also, while viewing one region, the operator may miss activities in other regions. In this paper, we propose sZoom, a framework to automatically zoom into a high resolution surveillance video. The proposed framework selectively zooms into the sensitive regions of the video to present details of the scene, while still preserving the overall context required for situation assessment. A multi-variate Gaussian penalty is introduced to ensure full coverage of the scene. The method achieves near real-time performance through a number of timing optimizations. An extensive user study shows that, while watching a full HD video on a mobile device, the system enhances the security operator's efficiency in understanding the details of the scene by 99% on the average compared to a scaled version of the original high resolution video. The produced video achieved 46% higher ratings for usefulness in a surveillance task.

Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as ETS and ARIMA gain their popularity not only from their high accuracy, but they are also suitable for non-expert users as they are robust, efficient, and automatic. In these areas, RNNs have still a long way to go. We present an extensive empirical study and an open-source software framework of existing RNN architectures for forecasting, that allow us to develop guidelines and best practices for their use. For example, we conclude that RNNs are capable of modelling seasonality directly if the series in the dataset possess homogeneous seasonal patterns, otherwise we recommend a deseasonalization step. Comparisons against ETS and ARIMA demonstrate that the implemented (semi-)automatic RNN models are no silver bullets, but they are competitive alternatives in many situations.

A framework to boost efficiency of Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation, which we call the refined variational approximation. Its strength lies both in ease of implementation and in automatically tuning the sampler parameters to speed up mixing time. Several strategies to approximate the \emph{evidence lower bound} (ELBO) computation are introduced, including a rewriting of the ELBO objective. A specialization towards state-space models is proposed. Experimental evidence of its efficient performance is shown by solving an influence diagram in a high-dimensional space using a conditional variational autoencoder (cVAE) as a deep Bayes classifier; an unconditional VAE on density estimation tasks; and state-space models for time-series data.