So, I compared the model with ARIMA and a few interesting findings. Firstly, there doesn't appear to be any seasonal component in the data - when decomposed with statsmodels, the series simply shows a straight line. Also, ARIMA showed a mean percentage error of 23%, whereas for LSTM it was just over 8%. The daily fluctuations in electricity consumption is quite volatile, so it looks like LSTM has an advantage over ARIMA here in that it is accounting for the inherent volatility in the series. While ARIMA would usually need to be combined with a model such as GARCH to estimate this volatility, the inherent nature of LSTM allows it to handle sequential data and in this case it looks like it's handling the volatility quite well.

This author's solar punk novel involves the team from Clean Coalition using their power grid maps, guiding business areas with strategic solar storage placement on a city level, taking into account Tesla's 1,600 superchargers, and everyone having solar storage in their homes. At some percentage, within this super distributed network we will gain resiliency. To get there will take patience, and smart tools. Researchers at the University of Massachusetts, Amherst campus, have built a software tool, called DeepRoof, which they say has achieved a "true positive rate" of 91.1% in identifying a roof's solar power potential, while using widely available (and cheap) satellite data from tools like Google Earth. Their goal in Deep Roof: a Data-Driven Approach For Solar Potential Estimation Using Rooftop Imagery, is to take a list of address (or GPS coordinates) from a contractor and hand back the solar power potential of those sites.

Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustor behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustor anomaly detection performance.

For machine learning perception problems, human-level classification performance is used as an estimate of top algorithm performance. Thus, it is important to understand as precisely as possible the factors that impact human-level performance. Knowing this 1) provides a benchmark for model performance, 2) tells a project manager what type of data to obtain for human labelers in order to get accurate labels, and 3) enables ground-truth analysis--largely conducted by humans--to be carried out smoothly. In this empirical study, we explored the relationship between resolution and human classification performance using the MNIST data set down-sampled to various resolutions. The quantitative heuristic we derived could prove useful for predicting machine model performance, predicting data storage requirements, and saving valuable resources in the deployment of machine learning projects. It also has the potential to be used in a wide variety of fields such as remote sensing, medical imaging, scientific imaging, and astronomy.

Advances in technology can allow you to order food by voice or unlock your phone with your face, but those new capabilities could take a toll on the environment. Enhanced tech capabilities are being developed through the use of artificial-intelligence approaches like neural networks, which detect patterns in speech and images by training programs across countless data points. That process constantly crunches reams of information on power-hungry servers in data centers that use a substantial amount of energy to power, cool and monitor the servers. The result: Training a neural network can emit 17 times more carbon dioxide than an average American does in a year, and five times the lifetime emissions of an average car. Those are the findings of a recent paper by researchers at the University of Massachusetts, Amherst, which highlighted the substantial power generated by AI technologies.

Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited. This work gives an interpretable belief propagation rule that is actually minimization of a localized $\alpha$-divergence. We term this algorithm as $\alpha$ belief propagation ($\alpha$-BP). The performance of $\alpha$-BP is tested in MAP (maximum a posterior) inference problems, where $\alpha$-BP can outperform (loopy) BP by a significant margin even in fully-connected graphs.

The ability to forecast events at scale, given a set of variables, is something most companies would find useful. So Amazon is aiming to make prediction more accessible with a fully managed service called Forecast that uses AI and machine learning to deliver highly accurate forecasts. As Amazon explained in a press release, Forecast -- which is based on the same technology the Seattle company uses to anticipate demand for hundreds of millions of products every day -- can be used to build precise forecasts for virtually any business condition, including product demand and sales, infrastructure requirements, energy needs, and staffing levels. It automatically provisions the necessary cloud infrastructure and processes data, building custom AI models hosted on AWS without requiring an ounce of machine learning experience on the part of developers. Amazon says the API or a console allows the average person to build custom machine learning models in less than five clicks and achieve accuracy levels that would normally take months in as little as a few hours.

These are exciting times for computational sciences with the digital revolution permeating a variety of areas and radically transforming business, science, and our daily lives. The Internet and the World Wide Web, GPS, satellite communications, remote sensing, and smartphones are dramatically accelerating the pace of discovery, engendering globally connected networks of people and devices. The rise of practically relevant artificial intelligence (AI) is also playing an increasing part in this revolution, fostering e-commerce, social networks, personalized medicine, IBM Watson and AlphaGo, self-driving cars, and other groundbreaking transformations. Unfortunately, humanity is also facing tremendous challenges. Nearly a billion people still live below the international poverty line and human activities and climate change are threatening our planet and the livelihood of current and future generations. Moreover, the impact of computing and information technology has been uneven, mainly benefiting profitable sectors, with fewer societal and environmental benefits, further exacerbating inequalities and the destruction of our planet. Our vision is that computer scientists can and should play a key role in helping address societal and environmental challenges in pursuit of a sustainable future, while also advancing computer science as a discipline. For over a decade, we have been deeply engaged in computational research to address societal and environmental challenges, while nurturing the new field of Computational Sustainability.

Looking at the way we live today, it's easy to think that relatively recent discoveries and innovations in science and technology are responsible for our modern lifestyle. But even the newest devices and equipment today have their foundations in technology developed centuries ago. The technology used for information exchange, communication, transportation and many other essential aspects of our lives are all a result of a series of inventions and innovations that go back well into the past. Let's take a look at some of the most crucial technological advancements in history. Using glass to refract light is a simple idea, but it took humanity a long time to discover it.

As a subfield of machine learning, \emph{reinforcement learning} (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey will discuss the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.