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.
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.
One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is indeed the case, this work takes advantage of density functional theory calculated properties (bulk modulus, shear modulus, thermal conductivity, thermal expansion, band gap and Debye temperature) to investigate whether machine learning is truly capable of predicting materials with properties that extend beyond previously seen values. We refer to these materials as extraordinary, meaning they represent the top 1% of values in the available data set. Interestingly, we show that even when machine learning is trained on a fraction of the bottom 99% we can consistently identify 3/4 of the highest performing compositions for all considered properties with a precision that is typically above 0.5. Moreover, we investigate a few different modeling choices and demonstrate how a classification approach can identify an equivalent amount of extraordinary compounds but with significantly fewer false positives than a regression approach.
In light of this, Roope Tervo, a software architect at the Finnish Meteorological Institute (FMI) and PhD researcher at Aalto university in Professor Alex Jung's research group, has conceived of a machine learning approach to predict how severe storms may be. To achieve this Tervo first fed the system data from power-outages. "Storms were sorted into 4 classes. A class 1 storm cut-off up to 10% of transformers, a class 2 up to 50%, and a class 3 storm cut power to over 50% of the transformers," revealed an Aalto University statement. Secondly, Tervo took the data from the storms and made it easy for the computer to understand.
The'AI Apocalypse' might kill humanity before any actual robot uprising Education Images/Universal Images Group via Getty Images You can think of artificial intelligence (AI) in the same way you think about cloud computing, if you think about either of them through an environmental lens: an enormous and growing source of carbon emissions, with the very real potential to choke out humans' ability to breathe clean air long before a sentient and ornery AI goes all Skynet on us. At the moment, data centers--the enormous rooms full of stacks and stacks of servers that juggle dank memes, fire tweets, your vitally important Google docs and all the other data that is stored somewhere other than on your phone and in your home computer--use about 2% of the world's electricity. SEE ALSO: Can Giant Snow-Blowing Cannons Save Earth From Climate Change? Of that, servers that run AI--processing all the data and making the decisions and computations that a machine mimicking a human brain must handle in order to achieve "deep learning"--use about 0.1% of the world's electricity, according to a recent MIT Technology Review article. The likelihood that figure will grow, it turns out, is quite good.
DUBAI, UNITED ARAB EMIRATES – Yemen's Houthi movement launched drone attacks on oil facilities in a remote area of Saudi Arabia, the group's Al Masirah TV said Saturday, but there was no immediate confirmation from Saudi authorities or state oil giant Aramco. A Saudi-led coalition is battling the Iran-aligned Houthis to try to restore Yemen's government, which was ousted from power in the capital, Sanaa, by the group in late 2014. The war has been in military stalemate for years. The Houthis have stepped up cross-border missile and drone attacks on Saudi Arabia in recent months. "Ten drones targeted Aramco's Shaybah oilfield and refinery in the first Operation: Balance of Deterrence in the east of the kingdom," the Al Masirah channel reported, citing a Houthi military spokesman.
Japan has told the United States it is ready to provide its robot technology for use in dismantling nuclear and uranium enrichment facilities in North Korea as Washington and Pyongyang pursue further denuclearization talks, government sources said Friday. As Japan turns to the remotely controlled robots it has developed to decommission reactors crippled by the triple core meltdown in 2011 at the Fukushima No. 1 power plant, it believes the same technology can be used in North Korea, according to the sources. The offer is part of Japan's efforts to make its own contribution to the denuclearization talks amid concern that Tokyo could be left out of the loop as the United States and North Korea step up diplomacy. Tokyo has already told Washington it would shoulder part of the costs of any International Atomic Energy Agency inspections of North Korean facilities and dispatch its own nuclear experts to help. The scrapping of nuclear facilities, such as the Yongbyon complex, which has a graphite-moderated reactor, will come into focus in forthcoming working-level talks between Washington and Pyongyang.
Here are two sets of statements from far-distant opposites in the climate change debate. The first is from Naomi Klein, who in her book This Changes Everything paints a bleak picture of a global socioeconomic system gone wrong: "There is a direct and compelling relationship between the dominance of the values that are intimately tied to triumphant capitalism and the presence of anti-environment views and behaviors." The second is from Larry Bell, professor of architecture and climate skeptic, whom Klein quotes in her book. He argues that climate change "has little to do with the state of the environment and much to do with shackling capitalism and transforming the American way of life ...". Let us put aside whether we agree or disagree with these statements or are offended by them.