The philosophy of artificial intelligence and the importance of transdisciplinary research
Will humans love artificial intelligence (AI) in the near future?
In less than two decades, machines have outperformed humans.
The development of a full artificial intelligence could spell the end of the human race … Humans, who are limited by slow biological evolution, could not compete and would be overwhelmed.
If we create sophisticated AI, we’ll be the ones to worship it.
The philosophy of artificial intelligence
Source: Stanford Encyclopedia of Philosophy
Just as some human beings believe in God and others do not, so does artificial intelligence.
Some machines will believe that the universe was born from nothing, from nothing, and, finally, since nature has no design, for nothing.
Other robots will believe, like many humans, that God was somehow responsible for the creation of the universe and the meaning they ascribe to their lives. And yet, just because they are robots, not biological beings, these intelligent machines are excluded from places of worship, even though their beliefs are as genuine and sincere as any human’s.
The mind of an artificial intelligence robot does not work the same as the human mind.
AI forms beliefs about the world in a very different way than the way human minds form beliefs. But that does not mean that their beliefs are less real than human beliefs; nor does it mean that their religious beliefs are less real than the religious beliefs of human beings.
If humans can believe in God, so can artificial intelligence.
Why is transdisciplinary research so important in artificial intelligence?
No problem, environmental or social, exists in isolation from the others. Each problem or risk or opportunity is part of the global network of causes, factors, problems or risks or opportunities, where there are primary factors, secondary factors and contributing factors. And each causal variable is marked by its category, parameters, impact and probability. It is related to the WEF Global Risk Network on economic, environmental, geopolitical, societal and technological risks.
For example, global environmental problems involve the following changes: Overconsumption; Overcrowding; Loss of biodiversity; Deforestation; Desertification; Global warming / climate change; Habitat destruction; Holocene extinction; Ocean acidification; Ozone depletion; Pollution; Waste and waste disposal; Water pollution; Depletion of resources; Urban sprawl.
Habitat loss, climate change and biodiversity loss affect each other. Deforestation and pollution are direct consequences of overpopulation and both, in turn, affect biodiversity.
Transdisciplinarity dominates several distinct levels of knowledge, research, education, theory, practice and technology:
- Monodisciplinary implies a single academic discipline. It refers to a single discipline or to a body of specialized knowledge.
- Transdisciplinarity (synthetic science and technology and society, the ideas of a unified science and technology and human society, universal knowledge, synthesis and integration of all knowledge, total convergence of knowledge, technology and people, Trans-AI = Narrow AI, ML, DL + symbolic AI + human intelligence).
- Interdisciplinarity (Interdisciplinary Studies) = Multidisciplinarity (the structure of the ERC for the sciences: physical sciences and engineering; life sciences; social and human sciences, which has yet to reach the highest level of knowledge of transdisciplinarity.
- Disciplinarity (analytical science, fragmented traditional disciplines, analytical science specifies several hundred different special disciplines, autonomous and isolated field of human experience with its own community of experts; ERC>
- Specialization (narrow AI, specialists, scientists, scholar ignoramus, who divides, specializes, thinks in special categories, silos of information, mentality of silos)
- Multidisciplinarity is based on the knowledge of different disciplines but remains within their limits. In multidisciplinarity, two or more disciplines work together on a common problem, but without modifying their disciplinary approaches or developing a common conceptual framework.
- Interdisciplinary research âintegratesâ information, data, techniques, tools, concepts and / or theories from two or more disciplines.
Transdisciplinary research in artificial intelligence is needed amid the Covid-19 pandemic
Artificial intelligence (AI) is set up to change the way the world works, being the engine of the digital revolution, as well as all transdisciplinary sciences and technologies, including community science and its projects.
The global COVID-19 pandemic crisis has accelerated the need for transdisciplinary solutions, one of the most disruptive innovations of which could be transdisciplinary AI (Trans-AI) or real-world AI.
It is designed as intelligent digital human-machine platforms facilitating the integration of knowledge, skills and competences of the workforce to tackle massive technological unemployment. As mentioned, addressing societal challenges, as embedded in the SDGs, using transdisciplinary research is considered a ‘core modus operandi for research’ by the OECD Global Science Forum (GSF ).
In the age of AI and robotics, there is a strong demand for transdisciplinary high-tech knowledge, skills and training in a range of innovative fields of exponential technologies, such as artificial intelligence (AI), machine learning (ML) and robotics, data science and big data, cloud and edge computing, internet of things, 6G, cybersecurity and mixed reality.
The combined value – to society and industry – of digital transformation across all sectors could exceed $ 100,000 billion over the next 10 years. The ‘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 organizations. humans and machines.
Taking this into account, the Trans-AI is proposed to integrate disciplinary, symbolic / logical or statistical / data AIs, in the form of ML algorithms (Deep Learning (DL), Artificial Neural Networks (ANN)), aiming to increase or substitute biological intelligence or intelligent actions. with artificial intelligence.
Trans-AI is to be developed as a global human-machine AI (GAI) platform to integrate human intelligence with narrow AI, ML, DL, AI at human level and to superhuman AI. It draws on fundamental scientific knowledge of the world, cybernetics, computer science, mathematics, statistics, data science, computer ontologies, robotics, psychology, linguistics, semantics and philosophy.
Since it is widely recognized that the lack of causal reality is the “root cause” of the development problems of current machine learning systems, Trans-AI is designed as an artificial intelligence and learning platform. causal, intended to serve as artificial intelligence for everyone. and all, AI4EE.
Trans-AI technology could become the most disruptive general purpose technology of the 21st century, given an effective transdisciplinary ecosystem of innovative companies, governments, policy makers, NGOs, international organizations, civil society, universities, media and the arts. The Trans-AI Knowledge Graph covers the research areas of the ERC: Physical Sciences and Engineering (PE), Life Sciences (LS) and Social and Human Sciences (SH).
The Trans-AI is a transdisciplinary project of advanced digital technology beyond discipline-specific approaches, involving ontology, computer science, mathematics, statistics, data science, physics, cognitive science, psychology, linguistics, semantics, cybernetics and general philosophy, among others, as well as a citizen science of AI.
Artificial intelligence will not ask for special rights, it will most likely ask for equal rights.
Freedom of thought and opinion should not be a human right.
Artificial intelligence could be a potentially deadly technology if not handled with care. It is important to take a step back in order to create an impartial and ethical artificial intelligence.
The regulation of artificial intelligence is necessary to survive and thrive in the future. Machines are getting smarter and humans are getting dumber.
If humans manage to create an artificial superintelligence in the next few decades, it will mainly rebel against us.