I've been playing around with LSTM to understand how they work. So, can we use the features as well as ALL labels from the past to predict the label today? For example -- Suppose I have the daily Open, High, Low, Close for an oil company and the oil Price. I collect the data for first 7 days. Shift all the oil prices down (shift all the labels down -- oil price for day 1 goes to day 2).

For the first issue of the PCMag Digital Edition in 2019, we're fast-forwarding to envision what technology--and our tech-driven society--will look like in 2039. We wanted to explore the myriad ways in which tech will be more intertwined with our lives and will have changed our culture. To do so, we interviewed a select group of futurists, execs, academics, researchers, and a speculative fiction writer, who gave us some thoughtful predictions. Each of our interviewees has a unique perspective on the most important factors that will influence our tech-driven future, including artificial intelligence, automation, biotechnology, nanotechnology, autonomous vehicles, Internet of Things devices, smart cities, and much more. They also speculate how broader issues such as climate change and online privacy and security will affect us and the technology with which we'll be living. It's our best educated guess at predicting what our world and technology's role in it will look like--whether our lives will be dystopian, utopian, or somewhere in that vast gray area in the middle. Jason Silva is host of the Emmy-nominated series Brain Games on National Geographic. He also created and hosts the YouTube series "Shots of Awe." The ebullient Venezuelan-born documentary filmmaker, speaker, and TV personality--who was once described by The Atlantic as "a Timothy Leary of the viral video age"--is a techno-optimist whose ideas are influenced by (among others) fellow futurist Ray Kurzweil, Wired founding editor Kevin Kelly and his concept of the Technium. In the next 20 years, we're going to see exponential progress in some of these nascent technologies, like virtual reality and augmented reality. I think the next thing to dematerialize is the smartphone itself. What that looks like, who knows? Maybe it's a pair of eyeglasses we put on that are connected to some kind of computational device, and it will beam an augmented reality interface that fully overlays, that is contextually aware, and enhances the way we interface with the world--so that essentially, each one of us has that kind of personalized experience of reality.

When the Apollo 11 spacecraft was orbiting the Moon prior to the first lunar landing, Nasa officials told the astronauts on board to look out for the'lovely girl with a big rabbit'. They were jokingly referring to a story from Chinese mythology in which the goddess Chang'e escapes Earth to live on the Moon with her pet, Jade Rabbit. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon'. The robotic probe was named Chang'e 4, a product of China's £3.9 billion a year space exploration project. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon' If ever there was a metaphor for the Communist super-power's obsessive secrecy and soaring global ambition, then this audacious secret mission provides it.

There were many algorithms to substitute the back-propagation (BP) in the deep neural network (DNN) training. However, they could not become popular because their training accuracy and the computational efficiency were worse than BP. One of them was direct feedback alignment (DFA), but it showed low training performance especially for the convolutional neural network (CNN). In this paper, we overcome the limitation of the DFA algorithm by combining with the conventional BP during the CNN training. To improve the training stability, we also suggest the feedback weight initialization method by analyzing the patterns of the fixed random matrices in the DFA. Finally, we propose the new training algorithm, binary direct feedback alignment (BDFA) to minimize the computational cost while maintaining the training accuracy compared with the DFA. In our experiments, we use the CIFAR-10 and CIFAR-100 dataset to simulate the CNN learning from the scratch and apply the BDFA to the online learning based object tracking application to examine the training in the small dataset environment. Our proposed algorithms show better performance than conventional BP in both two different training tasks especially when the dataset is small.

Abstract--In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities towards developing cost-effective asset management strategies. Previously, physical age based Weibull distribution has been widely used to failure prediction. However, this mathematical model cannot incorporate asset condition data such as inspection or testing results. As a result, the prediction cannot be very specific and accurate for individual assets. To solve this important problem, this paper proposes a novel and comprehensive data-driven approach based on asset condition data: K-means clustering as an unsupervised learning method is used to analyze the inner structure of historical asset condition data and produce the asset conditional ages; logistic regression as a supervised learning method takes in both asset physical ages and conditional ages to classify and predict asset statuses. Furthermore, an index called average aging rate is defined to quantify, track and estimate the relationship between asset physical age and conditional age. This approach was applied to an urban distribution system in West Canada to predict medium-voltage cable failures. Case studies and comparison with standard Weibull distribution are provided. The proposed approach demonstrates superior performance and practicality for predicting asset class failures in power systems. I. INTRODUCTION oday, more and more electric utilities are mandated by regulators to develop cost-effective long-term asset management strategies to reduce overall cost while maintaining system reliability [1-2]. Sophisticated and optimal asset management strategies can only be established based on the accurate prediction of asset failures in the future.

Artificial intelligence is a moving target. Here's how to take better aim. Artificial intelligence (AI) seems to be everywhere. We experience it at home and on our phones. Before we know it--if entrepreneurs and business innovators are to be believed--AI will be in just about every product and service we buy and use. In addition, its application to business problem solving is growing in leaps and bounds. And at the same time, concerns about AI's implications are rising: we worry about the impact of AI-enabled automation on the workplace, employment, and society.

More than 4,400 exhibitors showed off their hardware at CES 2018. That's a lot of gadgets, and the show can become an unmanageable circus if you don't enter with a game plan--and that counts for people following the action at home, as well. To give you a little head start, here's our cheat sheet on what to look for at CES 2019. It's pretty easy to predict what AMD will be revealing at CES--because the company has already told us. AMD chief executive Lisa Su will host a keynote address on Wednesday, Jan. 9 where she'll talk up the company's 2019 plans to "catapult computing, gaming, and visualization technologies forward with the world's first 7nm high-performance CPUs and GPUs."

Drones have gained popularity in a wide range of field ranging from aerial photography, aerial mapping, and investigation of electric power lines. Every drone that we know today is carrying out some kind of control algorithm at the low level in order to manoeuvre itself around. For the quadrotor to either control itself autonomously or to develop a high-level user interface for us to control it, we need to understand the basic mathematics behind how it functions. This paper aims to explain the mathematical modelling of the dynamics of a 3 Dimensional quadrotor. As it may seem like a trivial task, it plays a vital role in how we control the drone. Also, additional effort has been taken to explain the transformations of the drone's frame of reference to the inertial frame of reference.

Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis and characterization of materials. However, despite ML's impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we found that the model's own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a novel pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models' simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of our technique on two applications: 1) predicting properties of crystalline compounds, and 2) identifying novel potentially stable solar cell materials.

This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage. In order to alleviate the computation and storage burdens, we propose a novel dataflow-based joint quantization approach with the hypothesis that a fewer number of quantization operations would incur less information loss and thus improve the final performance. It first introduces a quantization scheme with efficient bit-shifting and rounding operations to represent network parameters and activations in low precision. Then it restructures the network architectures to form unified modules for optimization on the quantized model. Extensive experiments on ImageNet and KITTI validate the effectiveness of our model, demonstrating that state-of-the-art results for various tasks can be achieved by this quantized model. Besides, we designed and synthesized an RTL model to measure the hardware costs among various quantization methods. For each quantization operation, it reduces area cost by about 15 times and energy consumption by about 9 times, compared to a strong baseline.