Transdisciplinary Science and Technology: the matter of life and death during the COVID-19 crisis and beyond

Transdisciplinary research (TDR) is ranked as a mainstream modus operandi for research” by the OECD Global Science Forum (GSF). Addressing global risks and societal challenges, as embedded in SDGs, is the main goal of transdisciplinary science and technology (TST). Integrating knowledge from different science and technology disciplines and (non-academic) stakeholder communities, the TST projects should have a priority in sustainable public funding and socially responsible private investments.  For only the TST solutions are applied to global issues as the COVID-19 pandemic spreading across the World affecting human health, the socio-economic wellbeing, employment, inequality, economy, policy, and everyday human life.

 No global issues, or critical threat to the world, from global economic, social, and political issues to global environmental issues, that potentially impact or affect all persons and places, could be solved out of the TST.

TST is the source of the all-sustainable smart discoveries, innovations, technologies, and investments, as the Trans-AI to be developed and deployed as a global human-AI Internet/Web Platform.

Europe is in the urgent need of TST and TDR projects.  

Having a strong industrial base and huge resources, human and economic, Europe could be a global socio-technological model in the transdisciplinary solutions of trans-national and global change problems. It needs to overcome to the fragmentation of the EU’s research space and digital market, difficulties in attracting human capital and external investment, the lack of commercial competitiveness, and internal geopolitical inequalities, or Big Europe mentality.

[Why the EU lags behind in AI, Science and Technology: https://futurium.ec.europa.eu/en/european-ai-alliance/open-discussion/w…]

Why is Transdisciplinary Research so Critical for Critical Threats to the World?

No single issue, environmental or social, exists as isolated from the others. Each issue or risk or opportunity is part of the global network of causes, factors, issues, or risks, or opportunities, where there are primary factors, subordinary factors and contributary factors. And each causal variable is marked by its category, parameters, impact, and likelihood. It is like the WEF’s Global Risks Network of Economic, Environmental, Geopolitical, Societal and Technological Risks. The [Global Risks Report 2021 16th Edition; http://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2021.pdf]

For example, global environmental issues involve the following changes: Overconsumption; Overpopulation; Biodiversity loss; Deforestation; Desertification; Global warming/climate change; Habitat destruction; Holocene extinction; Ocean acidification; Ozone depletion; Pollution; Waste and waste disposal; Water pollution; Resource depletion; Urban sprawl.

Habitat loss and climate change and biodiversity loss are mutually adversely affecting each other. Deforestation and pollution are direct consequences of overpopulation and both, in turn, affect biodiversity. https://www.weforum.org/global-risks

Why are Transdisciplinarity, Transdisciplinary Research, or Transdisciplinary Science and Technology?

In all, transdisciplinarity tops several distinct levels of knowledge, research, education, theory, practice, and technology:

Specialization (Narrow AI, Specialists, Scientists, Learned Ignoramus, who divides, specializes, thinks in special categories, Information Silos, Silos Mentality) >

Disciplinarity (analytic science, traditional fragmented disciplines, analytic science specifies several hundred different special disciplines, self-contained and isolated domain of human experience with its own community of experts; ERC >

Interdisciplinarity (Interdisciplinary Studies) = Multi-disciplinarity (the ERC's structure for Science: Physical Sciences and Engineering; Life Sciences; Social Sciences and Humanities, still needs to reach transdisciplinarity; https://erc.europa.eu/https://erc.europa.eu/sites/default/files/document/file/ERC_Panel_struc…  ) >

Transdisciplinarity (synthetic science and technology and society, the ideas of a unified science and technology and human societyuniversal knowledge, synthesis and the integration of all knowledge, total convergence of knowledge, technology and people, Trans-AI = Narrow AI, ML, DL + Symbolic AI + Human Intelligence).

Monodisciplinary involves a single academic discipline. It refers to a single discipline or body of specialized knowledge.

Multidisciplinarity draws on knowledge from different disciplines but stays within their boundaries. In multidisciplinarity, two or more disciplines work together on a common problem, but without altering their disciplinary approaches or developing a common conceptual framework. 

Interdisciplinary research “integrates” information, data, techniques, tools, concepts, and/or theories from within two or more disciplines.

Interdisciplinarity is about the interactions between specialised fields and cooperation among special disciplines to solve a specific problem. It concerns the transfer of methods and concepts from one discipline to another, allowing research to spill over disciplinary boundaries, still staying within the framework of disciplinary research.

In the context of the unprecedented worldwide pandemic-enhanced crises, the transdisciplinarity appears as an all-sustainable way of solving complex real-world problems pursuing a general search for a “unity of knowledge” or Real-World AI.

Transdisciplinarity is radically distinct from interdisciplinarity, multi-disciplinarity and mono-disciplinarity. 

Transdisciplinarity analyzes, synthesizes and harmonizes links between disciplines into a coordinated and coherent whole, a global system where all interdisciplinary boundaries dissolve.

It is about addressing the world’s most pressing issues and seeing the world in a systemic, consistent, and holistic way at three levels:

(1) theoretical, (2) phenomenological, and (3) experimental (which is based on existing data in a diversity of fields, such as experimental science and technology, business, education, art, and literature).

Transdisciplinarity is a way of being radically distinct from interdisciplinarity, as well as multi-disciplinarity and mono-disciplinarity.

Transdisciplinarity integrates the natural, social, and engineering sciences in a unifying context, a whole that is greater than the sum of its parts and transcends their traditional boundaries.

Transdisciplinarity connotes a research strategy that crosses many disciplinary boundaries to create a holistic approach.

Transdisciplinary research integrates information, data, concepts, theories, techniques, tools, technologies, people, organizations, policies, and environments, as all sides of the real-world problems.

Transdisciplinarity takes this integration of disciplines on the highest level. It is a holistic approach, placing these interactions in an integral system. It thus builds a total network of individual disciplines, with a view to understand the world in terms of integrity and unity and discovery.

As noted, “Addressing societal challenges, as embedded in SDGs, using transdisciplinary research” considered a mainstream modus operandi for research” by the OECD Global Science Forum (GSF). The Recommendations for Governments, research agencies, research institutions and international bodies follow below.

https://www.oecd.org/science/addressing-societal-challenges-using-transdisciplinary-research-0ca0ca45-en.htm

TDR Recommendations from the OECD Global Science Forum

Governments

Governments need to recognise and promote transdisciplinary research, as an essential complement to other more traditional research approaches, in addressing complex societal challenges. Governments have a critical role to play in establishing the overall framework that enables and supports effective TDR. This includes: 1. Providing dedicated and sustainable resources for TDR, in particular in relation to STI for societal challenges and the Sustainable Development Goals; 2. Facilitating and supporting the engagement of public sector actors – including policy makers – in TDR activities and making the relevant public sector data available for use in these activities; 3. Incentivising other actors, including from the private sector, to support and participate in TDR to address societal challenges; 4. Promoting cooperation across ministries and responsible public authorities, including pooling of resources where appropriate, e.g. for research, innovation and overseas development, for TDR that addresses complex societal challenges.

Research Funders

Research funding agencies have a critical role to play by directly supporting and incentivising TDR research. This affects both prioritisation of research areas and changes to funding processes, including funding criteria, peer review and evaluation. Specific actions that can be taken by funders include:

1. provision of dedicated, long-term funding for TDR to address societal challenges, e.g. challenge-based funding, at local, national and international scales 2. support to establish centres of expertise and national and international networks in inter- and trans-disciplinary research domains 3. experimentation with different mechanisms to support the development of rigorous TDR projects, including sand-pit processes and training workshops for researchers 4. implementation of proactive management and monitoring of TDR programmes, recognising that flexibility is required to accommodate the evolving goals that are inherent in TDR projects. The management overhead, at both project and programme level, is likely be higher than for more traditional research. 5. changes to peer review and evaluation processes, including the use of multi-disciplinary and multi-stakeholder review processes and selection of peer-reviewers with prior experience in doing TDR 6. emphasising the evaluation of societal as well as scientific outputs and impacts in both ex ante and ex poste assessment of projects 7. extension of funding, and/or collaboration with other donors, to support capacity building and the participation of non-academic stakeholders in TDR projects 8. individual support, e.g. Fellowships, for outstanding individuals, who can develop and lead TDR projects.

Universities and Public Research Institutions (PRIs)

Universities and PRIs are the principle organisations through which TDR is carried out and their long-term strategic commitment and support is essential if TDR is to be expanded to the scale that is necessary to address complex societal challenges. This has implications for education and training, as well as research. It also cuts across the so-called 3rd mission activities (societal engagement and innovation) of universities and PRIs. Specific actions that can be taken by universities and PRIs include: 1. introduction of challenge-based approaches in research strategies and organisational structures 2. development of sustainable institutional structures and mechanisms (e.g., cross-department committees and meetings, shared infrastructure, flexible schedules, pump-priming funds) to foster cooperation across disciplines and to support TDR 3. establishment of structures and mechanisms to build long-term trusted relations with external stakeholder communities, including creation of formal, high-profile interfaces with civil society and private and public sector entities 4. allocation of core resources, including personnel, to build long-term expertise in TDR methodologies and practice 5. introduction of TDR learning modules into science education and postgraduate training courses; 6. support for early career researchers to engage in TDR projects, e.g. jointly supervised PhDs, and development of more flexible career paths 7. changes to evaluation and promotion criteria for individuals who engage in TDR, so that they are judged not only on scientific publications and citations but also on their contribution to collective research outputs that are of value to stakeholders outside of science 8. establishment of local, national and international networks of institutions that cooperate and exchange best practices in relation to TDR. These might be focused on local challenges, selected domains, such as sustainability research or global health, or more generic aspects of TDR. The academic community and science association

The academic community and science associations

  1. development and recognition of new inter-and trans-disciplinary research fields, such as sustainability research and planetary health, including the promotion of relevant scientific journals 2. support for, and participation in, new research management approaches, including innovative peer review and evaluation processes, that would promote TDR 3. support, including mentorship, for early career researchers who wish to engage in TDR 4. development of strategies and assessments, e.g. by National Academies or international science bodies, of the needs and potential for TDR to address societal challenges 5. development of international frameworks or programmes for TDR that addresses complex societal challenges 6. contributing to the development of new STI indicators and measures that value the combination of multiple research outputs.

Intergovernmental Organisations

One of the major policy drivers for more TDR is the UN Sustainable Development Goals. Inherent in these goals is the recognition that no single country can fully address them on its own and there is a need for more effective international cooperation and exchange. Whilst the UN and other international bodies, including the OECD do not have the resources or the authority to implement TDR at the scale required to address societal challenges, these international bodies can play an important role in building consensus and catalysing action.

Specific roles that international bodies can play include: 1. building awareness of TDR into existing policy frameworks (e.g. the SDGs or Responsible Research Innovation); 2. fostering capacity-building, e.g. by convening meetings of development donors and research funders; 3. promulgating guidelines/best practices/case studies of TDR; 4. promoting international alliances/networks and forums that bring together scientists and other stakeholders.

In addition to these 5 groups of actors, who have the major responsibility for promoting and enabling TDR within scientific research systems, there are a number of other stakeholders that need to embrace TDR if we are to effectively address that complex challenges that society is currently confronted with. Principle among these are many actors from the private sector and civic society/NGOs and civil science citizens.

Citizen Science, Community Science, Crowd Science, Civic Science, or Volunteer Monitoring

Civic Science is a non-academic, scientific research conducted, in whole or in part, by amateur (or nonprofessional) scientists. Citizen science is sometimes described as "public participation in scientific research," participatory monitoring, and participatory action research whose outcomes are often advancements in scientific research by improving the scientific communities capacity, as well as increasing the public's understanding of science. https://en.wikipedia.org/wiki/Citizen_science

In September 2015, the European Citizen Science Association (ECSA) published its Ten Principles of Citizen Science, which have been developed by the "Sharing best practice and building capacity" working group of the ECSAhttps://eu-citizen.science/resource/88

Some ECSA projects are as follows:

  • Cos4Cloud: This project is developing 11 technological services to improve citizen observatories, helping them to increase the quantity and the quality of observations
  • D-Noses: Empowering citizens through ​responsible research and innovation, citizen science and co-creation tools to design measures to control odour pollution
  • EU-Citizen.Science: The platform for sharing citizen science projects, resources, tools and training
  • INCENTIVE: Establishing citizen science hubs in European research performing and funding organisations, to drive institutional change and ground responsible research and innovation in society
  • PANELFIT: Participatory approaches to a new ethical and legal framework for ICTs
  • ROSiE: Identifies emerging ethical, social and legal challenges related to open science and citizen science
  • SEEDS: A project to empower teenagers in their own health and in STEM
  • StepChange: Exploring the potential of citizen science by developing five CSIs in the fields of energy, health and environment. https://ecsa.citizen-science.net/

The Trans-AI as the Most Sustainable Transdisciplinary Solutions of Global Threats

Artificial Intelligence (AI) is set up to change how the world works, being the engine of digital revolution, as well all transdisciplinary science and technology, including the community science and its projects.

The COVID-19 pandemic global crisis has accelerated the need for transdisciplinary solutions, of which one of the most disruptive innovations could be a Transdisciplinary AI (Trans-AI) or Real-World AI.

 It is designed as the human-machine digital intelligent platforms facilitating integrated knowledge, competences, and workforce skills to meet massive technological unemployment. As mentioned, addressing societal challenges, as embedded in SDGs, using transdisciplinary research considered a mainstream modus operandi for research” by the OECD Global Science Forum (GSF).

In the AI and Robotics era, there is a high demand for the trans-disciplinary knowledge, competence, and high-technology training in a range of innovative areas of exponential technologies, such as artificial intelligence (AI), machine learning (ML) and robotics, data science and big data, cloud and edge computing, the Internet of Thing, 6G, cybersecurity and mixed reality.

The combined value – to society and industry – of digital transformation across industries could be greater than $100 trillion over the next 10 years. “Combinatorial” effects of AI and Robotics with mobile, cloud, sensors, and analytics among others – are accelerating progress exponentially, but the full potential will not be achieved without collaboration between humans and machines.

Given that, the Trans-AI is proposed to integrate disciplinary AIs, symbolic/logical or statistic/data, as ML Algorithms (Deep Learning (DL), Artificial Neural Networks (ANNs)), aiming to augment or substitute biological intelligence or intelligent actions with machine intelligence.

 The Trans-AI is to be developed as a Man-Machine Global AI (GAI) Platform to integrate Human Intelligence with Narrow AI, ML, DL, Human-level AI, and Superhuman AI. It relies on fundamental scientific world’s knowledge, cybernetics, computer science, mathematics, statistics, data science, computing ontologies, robotics, psychology, linguistics, semantics, and philosophy.

Since it is widely recognized that the lack of reality with causality is the “root cause” of development problems of current machine learning systems, the Trans-AI is designed as a Causal Machine Intelligence and Learning Platform, to be served as Artificial Intelligence for Everybody and Everything, AI4EE.

The Trans-AI technology could make the most disruptive general-purpose technology of the 21st Century, given an effective transdisciplinary ecosystem of innovative business, government, policy-makers, NGOs, international organizations, civil society, academia, media and the arts. The Trans-AI Knowledge Graph covers ERC’s fields of research: Physical Sciences and Engineering (PE), Life Sciences (LS), and Social Sciences and Humanities (SH).

The Trans-AI is an advanced digital technology transdisciplinary project beyond discipline-specific approaches, involving ontology, computer science, mathematics, statistics, data science, physics, cognitive sciences, psychology, linguistics, semantics, cybernetics, and general philosophy, among others, as well as an AI citizen science.

Articles

  1. Keynote: "A Smart World: A Development Model for Intelligent Cities"; The 11th IEEE International Conference on Computer and Information Technology (CIT-2011) The 11th IEEE International Conference on Scalable Computing and Communications (ScalCom-2011) http://www.cs.ucy.ac.cy/CIT2011/; https://www.cs.ucy.ac.cy/CIT2011/files/SMARTWORLD.pdf
  2. Trans-AI: meet the disruptive discovery, innovation, and technology of all time

https://futurium.ec.europa.eu/en/european-ai-alliance/posts/trans-ai-meet-disruptive-discovery-innovation-and-technology-all-time

  1. EIS has Created the First Trans-AI Model for Narrow AI, ML, DL, and Human Intelligence

https://futurium.ec.europa.eu/en/european-ai-alliance/posts/eis-has-created-first-trans-ai-model-narrow-ai-ml-dl-and-human-intelligence

  1. Global AI Academy: AI4EE

https://futurium.ec.europa.eu/en/european-ai-alliance/posts/global-ai-academy-ai4ee?language=el 

  1. AI4EE: Real World AI as the Human-Machine General Purpose Technology: the best investment into the common future

https://futurium.ec.europa.eu/en/european-ai-alliance/posts/ai4ee-real-world-ai-human-machine-general-purpose-technology-best-investment-common-future

  1. Why AI is not AI: Everything You Know about Machine Intelligence is Wrong

https://futurium.ec.europa.eu/en/european-ai-alliance/posts/why-ai-not-ai-everything-you-know-about-machine-intelligence-wrong

  1. Causal Learning vs. "Deep Learning"​: on a fatal flaw in human knowledge

https://www.linkedin.com/pulse/causal-learning-vs-deep-fatal-flaw-machine-azamat-abdoullaev/?published=t

  1. HUMANS AS INTELLIGENT MACHINES OR REAL AI: A NEW HUMAN-MACHINE WORLD

https://www.bbntimes.com/society/humans-as-intelligent-machines-or-real-ai-a-new-human-machine-world?fbclid=IwAR39sTzBGEQmsufWEfgcYJ99KYUkZQ9xWIJ0JKiIPzspB6KsetD-tJx2LFo

  1. If any real artificial intelligence in reality and who over-profits from the AI mythology...

https://futurium.ec.europa.eu/en/european-ai-alliance/posts/if-any-real-artificial-intelligence-reality-and-who-over-profits-ai-mythology

  1. Human-Machine Superintelligence (HMSI) vs. Artificial Intelligence (AI) and Technological Unemployment: a paradigm shift​ or constructive disruption

https://www.linkedin.com/pulse/human-machine-superintelligence-hmsi-vs-artificial-ai-abdoullaev/?published=t

  1. Real Artificial Intelligence vs. Fake Artificial Intelligence

https://www.emergingtechnologiesnews.com/index.php/2021/07/02/real-arti…

  1. $1 Trillion by 2025: the AI4EE: On the Most Disruptive GPT of the 21st Century
  2. Causal Learning vs. "Deep Learning": on a fatal flaw in human knowledge and machine learning
  3. Engineering a Symbiotic Superintelligence by 2025: meeting Musk's concerns

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Author: Dr. Azamat Sh. Abdoullaev

https://www.igi-global.com/affiliate/azamat-abdoullaev/1192