On 10th of June, 2019, twenty-two AI researchers, including Andrew Ng and Yoshua Bengio, published a paper on how climate change can be tackled with machine learning. I really enjoyed reading it and I am convinced that the paper as well as the climatechange.ai For that reason i created a series of blog posts and videos which provide a dense summary, listing many of the proposed solutions and linking research work as well as ongoing projects. In the big picture, all solutions aim to reduce greenhouse gas emissions. As my contribution to the global #ClimateStrike week from September 20th to 27th, i will post one chapter (video and blog post) on every working day.

On 10th of June, 2019, twenty-two AI researchers, including Andrew Ng and Yoshua Bengio, published a paper on how climate change can be tackled with machine learning. I really enjoyed reading it and I am convinced that the paper as well as the climatechange.ai For that reason i created a series of blog posts and videos which provide a dense summary, listing many of the proposed solutions and linking research work as well as ongoing projects. In the big picture, all solutions aim to reduce greenhouse gas emissions. As my contribution to the global #ClimateStrike week from September 20th to 27th, i will post one chapter (video and blog post) on every working day.

According to IDC, over half of the world's data was created in the last two years, yet less than two percent has been analyzed.1 This untapped data represents a potential treasure trove for enterprises which seek more comprehensive business intelligence, improved business process, and innovative ways to remain ahead of their competitors. While high performance data analytics (HPDA), simulation and modeling, visualization, and other HPC workloads offer substantial business value on their own, augmenting those workloads with artificial intelligence (AI) derives even more benefit for a corporation. Financial institutions use AI to detect fraud real-time. Energy companies can more easily interpret ground-penetrating scans to pinpoint underground fossil fuel reserves, minimizing the impact on the surrounding environment.

A week ago today, millions of students took to the streets to protest the lack of action governments are taking to combat climate change. On Monday, 16-year-old Swedish campaigner Greta Thunberg made an intense and emotionally charged speech at the United Nations, begging world leaders to step up their commitment to protecting the planet's future. Headlines around the world echo her rallying cry, accusing our leaders of failing us. I wholeheartedly agree that governments can, and should, do more to confront climate change. But I have been equally curious about how cutting-edge technology is being used to fight and shelter against the effects of global warming.

AI is a continuum of technology innovation, says Yonatan Hagos, chief product officer at Beyond Limits. If you go beyond the fiction and beyond the hype, you see that the evolution of AI has been prompted by the quest for better answers to business problems. This drive forward is the foundation of the powerful AI solutions we see in action today -- and should also be the catalyst for companies to push beyond the limits of traditional AI. The ultimate goal of AI was always to find out if we could get machines to think like a human, with the understanding that machines should be able handle the analysis of huge data sets far more quickly and effectively than humans ever could. Today, forms of AI have proven to be effective as powerful neural networks and deep learning tools took over the heavy lifting for data processing.

This article outlines how data visualisation enables effective conversations between business stakeholders, data scientists and data engineers when solving complex Machine Learning (ML) workflows. We will introduce Kedro-Viz, an open-source data pipeline visualisation tool, exploring its functionality and detailing how QuantumBlack deployed Front-End Engineering to create our latest tool. You have spent weeks trying to locate and access to the data sources required to solve your ML use case, and are now beginning to preprocess these raw datasets. You now face the time consuming prospect of converting column types, cleansing, transformation and wrangling before you even begin to consider feature engineering. Your business stakeholders have already queried why the prototype ML model was not completed in Week One.

An NETL-sponsored project is leveraging artificial intelligence in a manner that will lead to more efficient, long-lasting and reliable gas turbines to meet America's growing energy needs. As advanced energy systems move toward higher temperatures to boost efficiency and reduce emissions, monitoring their performance under such harsh conditions becomes a challenge. Existing monitoring tools for gas turbines are costly, time-consuming and complicated, involving wires and risks for damage. With funding and guidance from NETL, North Carolina-based Siemens Corp. and its partners are developing smart sensor systems that provide real-time monitoring of gas turbine components, thereby enabling condition-based maintenance and prediction of each component's remaining useful life. Siemens is achieving this by incorporating advanced sensors directly onto gas turbine blades that feature an ultra-high-temperature wireless monitoring and data transfer capability that provides accurate, real-time information up to 1,400 degrees Celsius.

American leadership in Artificial Intelligence (AI) is critical to driving innovation and maintaining our nation's economic competitiveness. Today, U.S. Department of Energy (DOE) laboratories house four of the ten fastest and most powerful supercomputers in the world, uniquely positioning DOE to redefine what is possible in AI. DOE established its Artificial Intelligence Program (DOE AI) to harness and accelerate the Department's world-class leadership in high-performance computing, facilities, and team science in applying AI across the entire National Laboratory system to increase the pace of discovery in energy, materials science, health care, transportation, and beyond. InnovationXLab: Artificial Intelligence, hosted by Argonne National Laboratory, is the fourth in DOE's InnovationXLab series: a showcase of the remarkable assets and capabilities of the Department's National Laboratories. These summits facilitate a two-way exchange of information and ideas between industry, universities, investors, and end-use customers with Lab innovators and experts.

Machine Intelligence (MI) has emerged as the next breakthrough technology. The computing ability to recognize data patterns, accurately identify and classify languages, images, and data types including anomalies such as security breaches are getting stronger every day. Organizations in India are beginning to leverage mature Machine Learning (ML) platforms to improve productivity. We are seeing an immediate impact in embedded devices that are often located at the edge of the network - think cell towers, robotic arms, and smart city traffic sensors. With MI, embedded smart devices at the edge of the network are rapidly becoming the front line of analysis and decision-making.

Friday 20 September saw over 4 million people participate in climate change strikes worldwide, with 300,000 people in the UK taking to the streets to show their anger around the lack of action from global leaders. Another strike is due to take place on 27 September, bookending the newly dubbed Global Week for Future. Technology and climate change have a difficult relationship. As technology has become more pervasive throughout our homes, workplaces and society, the amount of data we create has spiralled, causing data centres across the globe to now have the same size carbon footprint as the aviation industry. Although some might argue that the only way to decarbonise is to decomputerise, like most of these things, the situation is not quite that black and white.