Data-driven research is currently revolutionizing how to model, predict, and control complex systems. This because many of the most pressing open issues in the literature do not adapt well to the traditional analytical approaches based only on scientific principles. For instance, modern dynamical systems theory is currently undergoing a renaissance, with analytical derivations and first-principles models giving way to data-driven approaches. It is clear that data science will not replace physical mathematics but will complement it to solve modern problems. The traditional engineering approach consists of manually deriving the system governing equations and plugin the measured physical parameters to it. It is then required advanced expertise in applied mathematics, dynamic systems theory, computational methods, mathematical modeling, optimization frameworks, and operator-assisted algorithmic tuning of control parameters. However, this approach is restrictive for systems with a high degree of complexity or uncertainty, where the reality gap caused by the model’s bias yields an unfeasible system. This, usually, due to uncertainties in physical parameter values or mathematical modeling assumptions. The pattern reproduction capability of machine learning (ML) models has attracted the attention of engineers. In particular, Neural networks (NNs) are universal function approximators, which means that they can be used as a black-box estimator applicable to the systems with parametric uncertainties and nonlinearities. Researchers are applying ML models to analyze and control complex dynamical systems with the potential to revolutionize our ability to interact and manipulate such systems. Data-driven control applications include the Model Predictive Control (MPC) with a surrogate NN plant model and reinforcement learning (RL), which does not require obtaining surrogate models of the system to be controlled, in contrast to MPC or feed-forward approaches.

Statement of the problem: In Corporate transactions, Attorneys spend hours searching online data rooms, reviewing company documents, contracts and other vital information which is time consuming and defeats the tasks of meeting deadlines. There has been difficulty, in speed-reading, vetting for errors, issue spotting based on particular words and phrases in contract agreements. The purpose of this study is to examine the relevance of artificial intelligence in delivery of legal services by lawyers, law firms, as well as its relevance in the administration of justice in international courts. Methodology and Theoretical orientation: both doctrinal methodology approach and also a geographical study of the legal market was utilized to focus on the rate and quality of legal service delivery and professionalism across firms all over major parts of the globe. Findings: In a recent US study carried out in 2018,it was discovered that human lawyers achieved an average accuracy of 85% in an average time of 92 minutes. However, in comparison to the AI, the success rate was at astonishing 92% with a record-breaking time of just 26 seconds. Conclusion and significance: artificial intelligence is changing quickly the legal industry and practice of law all over the world on how lawyers practice will operate in the future, given that employment and application of AI is an indispensable aid in technology, which every lawyer cannot ignore to be relevant in this 21^st century. Recommendations are made for corporate and commercial law firms, international court of justice and international arbitration centers to employ artificial intelligence lawyer (software) called ROSS to help them analyze legal issues and make corrections that ordinarily would be invisible. 

Background In the last few years internet technology has played a very important role in reinventing various medical procedures and allowing quick access to medical services, particularly in the remote areas of China. Use of (AI) and cloud computing in clinical laboratories for slide analysis has potential to compensate for a country-wide lack of pathologists. We used an automated scanner as well as mobile devices for screening and pathology diagnosis in a cervical cancer screening for rural women in Hubei province in China. Design In 2017 we launched a prospective cohort study where 703 103 women were enrolled in cervical cancer screening program using a validated AI-assisted cytology system. Each woman with slides classified as abnormal by either AI-assisted or manual reading and 10% negative randomly selected (AI selected) was referred for colposcopy and subsequent biopsy. We compared cytologists’ reports with AI reports for subsets of all slides. The outcomes were histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+). We measured economic value of this approach and compared it with the current standard of practice. In addition, we used mobile device to scan biopsy slides and applied a different algorithm to generate diagnosis. Those were compared with manual reading. Results AI-assisted cytology was 5.8% (3.0%-8.6%) more sensitive for detection of CIN2+ than manual reading with a slight reduction in specificity. We concluded that combining AI and cloud computing is the ideal approach for cervical cancer screening programs, providing the best service delivery and standardization across the entire program while maintaining high standards of quality control. Conclusion Our data showed that AI, cloud computing combined with mobile phone units can assist pathologists at every step of their work, bringing important laboratory services across geographic regions where pathology expertise is not available or there is a lack of it.

Statement of the Problem: Data systems and AI-based algorithms can be deployed at unprecedented scale and speed. Unintended consequences will affect people with the same scale and speed. Developers of AI-based models, as well as data analysts and scientists, have an ethical responsibility for the systems they create and their unintended consequences.

Methodology & Theoretical Orientation: There is an urgent need to identify methodologies to design such AI- based systems that will drive technological innovation and not slow down the development of large-scale AI while meeting the innate ethical requirements applicable to individuals and demanded by today's society. We need more than our own values and these guidelines (be fair, provide explainability and transparency, be safe and secure) to get there. In the current state of the market, it is necessary to teach all stakeholders in AI-based systems to design with methodologies that ensure ethical and autonomous behavior of such systems.

Findings. A methodology is developed to ensure the ethical design of intelligent systems, based on simple and up-to-date technological design principles, as well as specific activities with an integral vision of the IA-project. It includes continuous improvement cycle with feedback from unexpected outcomes and newly discovered consequences as opportunities.

Conclusion & Significance: Own values, good human-centered practices and guidelines are no more enough to mitigate potentially harmful consequences and bias. They need to be implemented in each specific IA initiative and this is not obvious. This proposed methodology provides an advanced and integrative methodological framework for design IA projects.

Statement of the Problem: Data systems and AI-based algorithms can be deployed at unprecedented scale and speed. Unintended consequences will affect people with the same scale and speed. Developers of AI-based models, as well as data analysts and scientists, have an ethical responsibility for the systems they create and their unintended consequences.

Methodology & Theoretical Orientation: There is an urgent need to identify methodologies to design such AI- based systems that will drive technological innovation and not slow down the development of large-scale AI while meeting the innate ethical requirements applicable to individuals and demanded by today's society. We need more than our own values and these guidelines (be fair, provide explainability and transparency, be safe and secure) to get there. In the current state of the market, it is necessary to teach all stakeholders in AI-based systems to design with methodologies that ensure ethical and autonomous behavior of such systems.

Findings. A methodology is developed to ensure the ethical design of intelligent systems, based on simple and up-to-date technological design principles, as well as specific activities with an integral vision of the IA-project. It includes continuous improvement cycle with feedback from unexpected outcomes and newly discovered consequences as opportunities.

Conclusion & Significance: Own values, good human-centered practices and guidelines are no more enough to mitigate potentially harmful consequences and bias. They need to be implemented in each specific IA initiative and this is not obvious. This proposed methodology provides an advanced and integrative methodological framework for design IA projects