Every single person who contributed is a true believer of creating a network of compute nodes that utilize idle compute power for the purposes of AI training where speed is the key. These are people who roll up their sleeves in the industry and see a clear path to user adoption when most believe Amazon, Google, or existing frameworks get the job done. Contrarian thinking is how massive opportunities are captured. We are honored to have on-board futurists who are aligned with our vision. While existing deep learning distribution methods and frameworks have come a long way, it still suffers from slow training speeds and expensive servers.

Fast charging will help electric cars such as the Mercedes-Benz EQ Silver Arrow electric concept vehicle. We are living in an age of massive technological change, but sometimes it is all so complicated it can be hard to work out what's really going to change the world and what will fall by the wayside. Researchers at Lux have looked at the key technology innovations that are going to change the world economy – and our lives over the next 10 years. Its 19 for 2019 report looks at the innovations that are facing market roadblocks and those that are more likely to succeed because they fit an unmet market need. The top 5 transformative technologies to watch in 2019, according to Lux, are:Machine learning and AI: These programs can quickly generate insight from vast datasets for uses in retail, transportation, medicine, and more – 10,000 AI patents were filed in the past year.Wearable electronics: Wearables are evolving beyond the smartphone to serve as connected, personalized sensors – venture capital funding over the past five years has totalled $2 billion.3D Enterprise uses for AR and VR are on the rise for training, quality, productivity, and more – $2 billion in VC funding pours in each year.

Editor's Note: The DigitalGlobe 2018 Australia Sustainability Hackathon aimed to address Australia's most conflicting issues surrounding mining, agriculture and environmental sustainability using machine learning and satellite imagery. This blog post is written by the winning team from the agriculture category. The forestry industry can benefit from multi-spectral, high-resolution satellite imagery in a number of ways, particularly for inventory components, such as tree stocking assessment, Leaf Area Index (LAI) estimation, volume survey and health analysis at stand and individual tree level. These could be measured in direct way through sampling. However, direct methods are very labour intensive, costly and subject to sampling error. Image-based remote sensing and advanced artificial intelligence (AI) technology offer an affordable solution to this problem.

There was a time when Zhang Lei worked in London's financial sector. While successful, he felt that dealing in derivatives was not a meaningful way to live his life. So, he quit his job in 2006 to pursue his passion for fighting climate change. In 2007, he founded Envision to design and manufacture wind turbines. As the CEO, Zhang envisions a better future and strives for the company to exist at the forefront of current technology.

Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distribution observations. Approximate Bayesian approaches are commonly used to estimate uncertainty for neural network predictions, but can struggle with out-of-distribution observations. Generative models can in principle detect out-of-distribution observations as those with a low estimated density. However, the mere presence of an out-of-distribution input does not by itself indicate an unsafe situation. In this paper, we present a method for uncertainty-aware robotic perception that combines generative modeling and model uncertainty to cope with uncertainty stemming from out-of-distribution states. Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit (generative) model of the observation distribution to handle out-of-distribution inputs. This is accomplished by probabilistically projecting observations onto the training distribution, such that out-of-distribution inputs map to uncertain in-distribution observations, which in turn produce uncertain task-related predictions, but only if task-relevant parts of the image change. We evaluate our method on an action-conditioned collision prediction task with both simulated and real data, and demonstrate that our method of projecting out-of-distribution observations improves the performance of four standard Bayesian and non-Bayesian neural network approaches, offering more favorable trade-offs between the proportion of time a robot can remain autonomous and the proportion of impending crashes successfully avoided.

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy, including estimation of relevant parameters can be time consuming and costly. In this paper we address this issue by proposing generic adaptation strategies based on approaches from earlier works. Experimental results after using the proposed strategies with three adaptive algorithms on 36 datasets confirm their viability. These strategies often achieve better or comparable performance with custom adaptation strategies and naive methods such as repeatedly using only one adaptive mechanism.

The popular approach is to constrain the reconstructions using compactness priors including sparsity. Several researchers have recently introduced off-the-grid continuous domain priors that are robust to discretization errors [1, 2], which provide significantly improved image quality in a range of applications. However, the main challenge is the significant increase in computational complexity. Recently, several researchers have introduced deep learning methodsas fast and efficient alternatives to compressed sensing algorithms. Current approaches can be categorized into direct and model based strategies. The direct approaches directly estimate the images from the undersampled measurements ortheir transforms/features [3, 4]. These methods learn to invert the forward operator over the space/manifold of images. Whilethis approach is more popular, a challenge with these schemes is the need to learn the inverse, which often requires large models (e.g.

Fast charging will help electric cars such as the Mercedes-Benz EQ Silver Arrow electric concept vehicle. We are living in an age of massive technological change, but sometimes it is all so complicated it can be hard to work out what's really going to change the world and what will fall by the wayside. Researchers at Lux have looked at the key technology innovations that are going to change the world economy – and our lives over the next 10 years. Its 19 for 2019 report looks at the innovations that are facing market roadblocks and those that are more likely to succeed because they fit an unmet market need. The top 5 transformative technologies to watch in 2019, according to Lux, are:Machine learning and AI: These programs can quickly generate insight from vast datasets for uses in retail, transportation, medicine, and more – 10,000 AI patents were filed in the past year.Wearable electronics: Wearables are evolving beyond the smartphone to serve as connected, personalized sensors – venture capital funding over the past five years has totalled $2 billion.3D Enterprise uses for AR and VR are on the rise for training, quality, productivity, and more – $2 billion in VC funding pours in each year.

Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process. The encoder summarizes the representation of input sentence from scratch, which is potentially a problem if the sentence is ambiguous. When translating a text, humans often create an initial understanding of the source sentence and then incrementally refine it along the translation on the target side. Starting from this intuition, we propose a novel encoder-refiner-decoder framework, which dynamically refines the source representations based on the generated target-side information at each decoding step. Since the refining operations are time-consuming, we propose a strategy, leveraging the power of reinforcement learning models, to decide when to refine at specific decoding steps. Experimental results on both Chinese-English and English-German translation tasks show that the proposed approach significantly and consistently improves translation performance over the standard encoder-decoder framework. Furthermore, when refining strategy is applied, results still show reasonable improvement over the baseline without much decrease in decoding speed.

Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Older techniques for such picking include interpolation of control points however, in recent years neural networks have been used for this task. Until now, most networks trained on small patches from larger images. This limits the networks ability to learn from large-scale geologic structures. Moreover, currently available networks and training strategies require label patches that have full and continuous annotations, which are also time-consuming to generate. We propose a projected loss-function for training convolutional networks with a multi-resolution structure, including variants of the U-net. Our networks learn from a small number of large seismic images without creating patches. The projected loss-function enables training on labels with just a few annotated pixels and has no issue with the other unknown label pixels. Training uses all data without reserving some for validation. Only the labels are split into training/testing. Contrary to other work on horizon tracking, we train the network to perform non-linear regression, and not classification. As such, we propose labels as the convolution of a Gaussian kernel and the known horizon locations that indicate uncertainty in the labels. The network output is the probability of the horizon location. We demonstrate the proposed computational ingredients on two different datasets, for horizon extrapolation and interpolation. We show that the predictions of our methodology are accurate even in areas far from known horizon locations because our learning strategy exploits all data in large seismic images.