Climate change has emerged as the biggest threat to humanity, with devastating consequences such as extreme weather events, climate migration, and a sharp decline in biodiversity. While the brunt of climate action is shouldered by green parties and public activists like the young Greta Thunberg, in recent years many industries have stepped up innovation to try and do their bit. The informatics industry in particular has been flexing its R&D muscle to propose bleeding-edge solutions. A recent paper published by a group of high-profile AI experts and IT professionals explores the potential that can be found "at the nexus of climate change and machine learning". Headed by David Rolnick, a Postdoctoral Research Fellow at the University of Pennsylvania, the paper puts a spotlight on "high-impact opportunities for real-world change" present in such ML fields as artificial intelligence, computer vision, unsupervised learning, and more.

Climate change has emerged as the biggest threat to humanity, with devastating consequences such as extreme weather events, climate migration, and a sharp decline in biodiversity. While the brunt of climate action is shouldered by green parties and public activists like the young Greta Thunberg, in recent years many industries have stepped up innovation to try and do their bit. The informatics industry in particular has been flexing its R&D muscle to propose bleeding-edge solutions. A recent paper published by a group of high-profile AI experts and IT professionals explores the potential that can be found "at the nexus of climate change and machine learning". Headed by David Rolnick, a Postdoctoral Research Fellow at the University of Pennsylvania, the paper puts a spotlight on "high-impact opportunities for real-world change" present in such ML fields as artificial intelligence, computer vision, unsupervised learning, and more.

With successful adoption of AntWorks' IAP solution, businesses will stand to save millions and realise increased performance and efficiency by automating and processing business data, including unstructured data, which will make up 80% of the world's data by 2025. The partnership will help the GCC become a blueprint for the AI economy in the rest of the Middle East, Turkey and Africa, especially as governments look to diversify and drive revenue from non-oil and gas sectors. "We are deeply honored to partner with The Private Office of Sheikh Saeed bin Ahmed Al Maktoum and SEED Group expanding our reach into the Middle East," said Asheesh Mehra, AntWorks Co-Founder and Group CEO. "We see our partnership with SEED Group as an incredible opportunity to bring AntWorks' leading expertise in artificial intelligence to the GCC - helping the UAE's Ministry of AI realise its 2031 Artificial Intelligence Strategy. This is a market that thrives on innovation and has taken some of the most ambitious steps in the world in adopting the use of AI across government and business as they seek to create new economic, social, and educational opportunities for citizens. We look forward to a powerful and productive relationship that will make straight-through processing a reality across the GCC."

Day three of ADIPEC 2019 has just concluded here in Abu Dhabi, UAE and much was said about oil demand concerns. Morning discourse was coloured by the International Energy Agency's take that demand is set to plateau by 2030 due to a pick up in the use of electric vehicles around the world. In its latest market projections, the IEA said overall demand for energy is set to increase by 1% every year until 2040, however headline demand will plateau ten years earlier than it had previously forecast. Elsewhere in its World Energy Outlook report, the IEA said US shale output, which has made the country the world's biggest oil producer, is likely to stay higher for longer than previously projected, with the country accounting for 85% of the increase in global oil production by 2030, and for 30% of the increase in natural gas. Meanwhile, switching tack to the coming 12 months, OPEC Secretary General Mohammed Barkindo said an uptick in demand for 2020 may be on the cards should the US-China trade stand-off end.

Hello, In this article I am going to make an experiment on a tool called mlflow that come out last year to help data scientist to better manage their machine learning model. The idea of this article is not to build the perfect model for the use case where I am going to build a machine learning model, but more to dive on the functionalities of mlflow and see how it can be integrated in a ML pipeline to bring efficiency in the daily basis for a data scientist/ machine learning engineer. There are three pillars around mlflow (). Their documentation is really great and they have a nice tutorial to explain the component of mlflow. For this article I am going to focus my test on the Tracking and Models parts of mlflow because I will be honest with you I didn't see the point on the Project part (looks like a conda export and a config file to run python script in a specific order) but I am sure it can help some people on the reproductive aspect of an ml pipeline.

And we focus really on the athletic part of it. I think, though, that if you do a good job on the athletic part, which is also kind of the low-level part, you can make it easier for high-level AI to interact with you." In other words, it's much easier to direct a robot to take care of a task for you if you've already taught the robot how to stand, walk, navigate, and so on.

CNR-IAC, Rome, Italy Abstract Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically nontrivial fluctuations of the velocity field, over a wide range of length-and timescales, and it can be quantitatively described only in terms of statistical averages. Strong non-stationarities hinder the possibility to achieve statistical convergence, making it impossible to define the turbulence intensity and, in particular, its basic dimensionless estimator, the Reynolds number. Here we show that by employing Deep Neural Networks (DNN) we can accurately estimate the Reynolds number within 15% accuracy, from a statistical sample as small as two large-scale eddy-turnover times. In contrast, physics-based statistical estimators are limited by the rate of convergence of the central limit theorem, and provide, for the same statistical sample, an error at least 100 times larger. Our findings open up new perspectives in the possibility to quantitatively define and, therefore, study highly non-stationary turbulent flows as ordinarily found in nature as well as in industrial processes. Turbulence is characterized by complex statistics of velocity fluctuations correlated over a wide range of temporal-and spatial-scales.

Geophysics Department, Stanford University, Stanford, California, USA In this study we develop a single-station deep-learning approach for fast and reliable estimation of earthquake magnitude directly from raw waveforms. We design a regressor composed of convolutional and recurrent neural networks that is not sensitive to the data normalization, hence waveform amplitude information can be utilized during the training. Our network can predict earthquake magnitudes with an average error close to zero and standard deviation of 0.2 based on single-station waveforms without instrument response correction. We test the network for both local and duration magnitude scales and show a station-based learning can be an effective approach for improving the performance. The proposed approach has a variety of potential applications from routine earthquake monitoring to early warning systems.

As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation. In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users' different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our proposed portfolio strategy has marked superiority across representative real-world market datasets in terms of extensive evaluation criteria.

We present a simulation framework for spunbond processes and use a design of experiments to investigate the cause-and-effect-relations of process and material parameters onto the fiber laydown on a conveyor belt. The virtual experiments are analyzed by a blocked neural network. This forms the basis for the prediction of the fiber laydown characteristics and enables a quick ranking of the significance of the influencing effects. We conclude our research by an analysis of the nonlinear cause-and-effect relations.