Applied Artificial Intelligence in the Subsurface

In this paper we present an example of improved approaches for how to interact with data and leverage artificial intelligence for the subsurface. Currently, subsurface workflows typically rely on a lot of time-consuming manual input and analysis, but the promise of artificial intelligence is that, once properly trained, an AI can take care of the more routine tasks, leaving the domain expert free to work on more complex and creative parts of the job. Artificial intelligence work on subsurface datasets in recent years has typically taken the form of research and proof of concept type work, with a lot of one-off solutions showing up in the literature using new and innovative ideas (e.g. Hussein et al, 2021; Misra et al, 2019). Oftentimes this work requires a good degree of data science knowledge and programming skills on the part of the scientist, putting many of the approaches outlined in these and a multitude of other papers out of reach for many subsurface experts in the Oil and Gas industry. In order for Artificial Intelligence to become applied as part of regular workflows in the subsurface, the industry needs tools built to help subsurface experts access AI techniques in a more practical, targeted way. We present herein a practical guide to help in developing applied artificial Intelligence tools to roll out within your organization or to the industry more broadly.

Implications of daily applied artificial intelligence and the future of our rights

Nowadays, artificial intelligent technologies are all in our hands, and we all make a modest contribution, sometimes consciously and sometimes unconsciously, in their further improvement. The increasing development, adoption and use of intelligent technologies and systems has shown that an algorithm is able to predict consumer’s needs, or furthermore wishes, or diagnose a disease with an accuracy rate beyond average natural human intelligence. While the use of artificially intelligent technologies and machines revolutionizes crucial sectors such as health, finance and banking and the economy and market needs, boundaries are still to be set. This paper analyzes ethical implications of day-to-day use of AI along with the need and steps towards human rights law to address AI impacts.

A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma

BackgroundNeuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved.MethodsHere, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism.ResultsThis classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients.ConclusionsIn this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.

18 examples of mivar expert systems

Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.

CURRENT TRENDS AND BARRIERS OF APPLIED ARTIFICIAL INTELLIGENCE

In 2017, the Spanish Government created a group made up of different experts, in order to develop a joint state strategy in Artificial Intelligence and Big Data. Although it is true that there were moments in which the development of this strategy stopped due to political issues, at the end of 2018 the European Commission urged its member countries to have their national strategy for R & D & i in AI presented. In this way, the Spanish R & D & I Strategy in Artificial Intelligence was presented in March 2019.