A Perspective on the Future of Machine Learning: Moving Away from ‘Business as Usual’ and Towards a Holistic Approach of Global Conservation

2018 ◽  
pp. 411-430
Author(s):  
Grant R. W. Humphries ◽  
Falk Huettmann
Keyword(s):  
Machine Learning ◽  
Holistic Approach ◽  
Global Conservation ◽  
The Future ◽  
Business As Usual

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

Human, AI, Robot Teaming and the Future of Work: Barriers and Opportunities for Advancement

2020 ◽  
Vol 64 (1) ◽  
pp. 62-66
Author(s):  
Erin K. Chiou ◽  
Eric Holder ◽  
Igor Dolgov ◽  
Kaleb McDowell ◽  
Lance Menthe ◽  
...  
Keyword(s):  
Economic Value ◽  
Panel Discussion ◽  
Well Being ◽  
Future Of Work ◽  
Global Workforce ◽  
Big Ideas ◽  
The Future ◽  
Barriers And Opportunities ◽  
Business As Usual ◽  
And Robotics

Global investments in artificial intelligence (AI) and robotics are on the rise, with the results to impact global economies, security, safety, and human well-being. The most heralded advances in this space are more often about the technologies that are capable of disrupting business-as-usual than they are about innovation that advances or supports a global workforce. The Future of Work at the Human-Technology Frontier is one of NSF’s 10 Big Ideas for research advancement. This panel discussion focuses on the barriers and opportunities for a future of human and AI/robot teaming, with people at the center of complex systems that provide social, ethical, and economic value.

scholarly journals Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Dhruvil Shah ◽  
Devarsh Patel ◽  
Jainish Adesara ◽  
Pruthvi Hingu ◽  
Manan Shah
Keyword(s):  
Machine Learning ◽  
Education Sector ◽  
Student Records ◽  
Job Roles ◽  
Student Data ◽  
Disruptive Technologies ◽  
Valid Data ◽  
The Future ◽  
Student Achievements ◽  
The University

AbstractAlthough the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

scholarly journals A Review of Reductionist versus Systems Perspectives towards ‘Doing the Right Strategies Right’ for Circular Economy Implementation

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 38
Author(s):  
Raquel Balanay ◽  
Anthony Halog
Keyword(s):  
Circular Economy ◽  
Systems Approach ◽  
Holistic Approach ◽  
Comprehensive Analysis ◽  
Unintended Consequences ◽  
Review Reports ◽  
The Right ◽  
Business As Usual ◽  
Analytical Frameworks ◽  
Reductionist Approach

This systematic review examines the importance of a systems/holistic approach in analyzing and addressing the footprints/impacts of business-as-usual activities regarding the development of a circular economy (CE). Recent works on why current CE approaches have to be examined in terms of reductionist vs. systems perspectives are reviewed to tackle questions pertaining to the right or the wrong way of CE implementation. ‘Doing the right thing right’ is essential for sustainability—the ultimate goal of a CE, which must be viewed as a system to begin with. The limited reductionist approach overlooks and thus cannot prognosticate on the formidable unintended consequences that emerge from ‘doing the right things wrong’, consequences that become too costly to undo. The systems approach, being holistic, is complicated and difficult to pursue but open to exciting opportunities to integrate innovations in CE analysis and implementation. Complexity is an inherent downside of the systems approach. However, both approaches are complementary, as reductionist models can be combined to create a system of comprehensive analysis to correct the approach towards implementation of current CE initiatives. This review reports that advancements in systems analytical frameworks and tools are highly important for creating general guidelines on CE analysis and implementation.

Can machine learning be used to forecast the future uncertainty of military teams?

2021 ◽  
pp. 154851292199911
Author(s):  
Ronald H Stevens ◽  
Trysha L Galloway
Keyword(s):  
Machine Learning ◽  
Sensory Information ◽  
Fundamental Property ◽  
Transition Matrices ◽  
Team Members ◽  
Student Teams ◽  
The Future ◽  
Network States ◽  
Uncertainty Models ◽  
Military Teams

Uncertainty is a fundamental property of neural computation that becomes amplified when sensory information does not match a person’s expectations of the world. Uncertainty and hesitation are often early indicators of potential disruption, and the ability to rapidly measure uncertainty would have implications for future educational and training efforts by targeting reflective discussions about past actions, supporting in-progress corrections, and generating forecasts about future disruptions. An approach is described combining neurodynamics and machine learning to provide quantitative measures of uncertainty. Models of neurodynamic information derived from electroencephalogram (EEG) brainwaves have provided detailed neurodynamic histories of US Navy submarine navigation team members. Persistent periods (25–30 s) of neurodynamic information were seen as discrete peaks when establishing the submarine’s position and were identified as periods of uncertainty by an artificial intelligence (AI) system previously trained to recognize the frequency, magnitude, and duration of different patterns of uncertainty in healthcare and student teams. Transition matrices of neural network states closely predicted the future uncertainty of the navigation team during the three minutes prior to a grounding event. These studies suggest that the dynamics of uncertainty may have common characteristics across teams and tasks and that forecasts of their short-term evolution can be estimated.

Invited review: Importance of animal health and welfare for the stability of the three pillars of sustainability of livestock systems

2016 ◽  
Vol 7 (2) ◽  
pp. 208-214
Author(s):  
P. Chemineau
Keyword(s):  
Animal Welfare ◽  
Production Systems ◽  
Animal Health ◽  
Holistic Approach ◽  
Population Increase ◽  
Strong Interactions ◽  
Essential Components ◽  
The Future ◽  
The Stability ◽  
Livestock Systems

The future livestock systems at the world level will have to produce more in the perspective of the population increase in the next 30 years, whereas reducing their environmental footprint and addressing societal concerns. In that perspective, we may wonder if animal health and animal welfare, which are two essential components of production systems, may play an important role in the stability of the three pillars of sustainability of the livestock systems. We already know that objectives driven by economy, environment and society may modify animal welfare and animal health, but is the reverse true? The answer is yes and in 11 cases out of 12 of the matrix health-welfare×3 pillars of sustainability×positive or negative change, we have many examples indicating that animal health and animal welfare are able to modify, positively or negatively, the three pillars of sustainability. Moreover, we also have good examples of strong interactions between health and welfare. These elements play in favour of an holistic approach at the farm level and of a multicriterial definition of what could be the sustainable systems of animal production in the future which will respect animal welfare and maintain a good animal health.

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