Webinar on

AI & Robotics

August 30, 2021

Scientific Program

Keynote Session:

Meetings International -  Conference Keynote Speaker Camilo Andres Manrique Escobar photo

Camilo Andres Manrique Escobar

MSc, University of Salerno, Italy

Title: Machine Learning Applications in Mechanical Engineering

Biography:

Camilo Manrique is a mechanical engineer with a master's degree in multibody dynamics from the University of Salerno. He is currently a researcher at the academic spin-off MEID4 Srl of the University of Salerno, where he investigates artificial intelligence algorithms' application for the robust control of non-linear systems.

 

Abstract:

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.

 

Meetings International -  Conference Keynote Speaker Thyword C. Nnadi photo

Thyword C. Nnadi

LL.B, Abia State University, Nigeria, B.L (Nigerian Law School)

Title: Legal profession, artificial intelligence and robotics: an ease of delivering legal services across the globe.

Biography:

We will update Soon

Abstract:

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 21st 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. 

 

Meetings International -  Conference Keynote Speaker Bojana Turic photo

Bojana Turic

Wuhan Landing Intelligence Medical Co., Ltd Wuhan, China

Title: The use of Artificial Intelligence (AI) in Clinical Laboratory for Population Based Cervical Cancer Screening and Diagnosis

Biography:

Dr. Bojana Turic was a part of the executive team at PMI labs, a Vancouver-based private spin off company from BC Cancer Agency that focused on early cancer detection. She led the company through all steps from a concept through significant clinical and regulatory milestones including positioning the company as a leader in early cancer diagnostic technologies, the development of strategic commercialization alliances, and execution of the company’s first commercial agreements. She was also instrumental for expanding private pathology services in BC. At present she is a part of Landing Medical High Tech Co. team where she supports the use of AI application for cervical cancer screening. Dr. Turic completed her MD at the University of Sarajevo, Bosnia and Herzegovina where she held the position of Assistant Professor of Medical Microbiology. She finished her fellowship in Medical microbiology at the University of Zagreb, Croatia.

 

Abstract:

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.

Meetings International -  Conference Keynote Speaker Dr. Ricardo Reier Forradellas photo

Dr. Ricardo Reier Forradellas

PhD, Catholic University of Avila, SPAIN

Title: Design of an advanced methodology and integration of the ethical factor for the design of AI-based models

Biography:

PhD Ricardo Reier Forradellas is Director of the Business School of the University. Coordinator Official Master's Degree MBA, Coordinator Official Master's Degree  in Direction and Management of Spots Institutions. Coordinator Official Master's Degree in CIbersecurity. Coordinator Master's Degree ECommerce, Coordinator Master's Degree Digital Transformation, Coordinator Master's Degree Big Data, Coordinator Master´s Degree Artificial Intelligence, Coordinator Master´s Degree Industry 4.0., Coordinator Master´s Degree Internet of Things, Coordinator Master´s Degree Agriculture 4.0.Speaker and organizer of numerous conferences on entrepreneurship, digital transformation and business management.

 

Abstract:

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.

 

 

Meetings International -  Conference Keynote Speaker Luis Miguel Garay Gallastegui photo

Luis Miguel Garay Gallastegui

Catholic University of Avila, SPAIN

Title: Design of an advanced methodology and integration of the ethical factor for the design of AI-based models.

Biography:

Customers and Technology. This exciting combination represents the evolution of his career, which in the last 20 years has been linked to business development, Internet and digital transformation. Passion and extensive experience as a teacher (> 20years) in postgraduate programs, masters and degrees, both in universities and business schools. Management experience as director of customer experience and digital technology and director of digital transformation at Telefonica. Author of several books about technological business models. Research areas based on Artificial Intelligence, its impact on Digital Marketing and ethics.

 

 

Abstract:

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