The study addresses the ongoing
debate surrounding the capabilities of engineers to build machines with
human-like intelligence using Artificial Intelligence (AI) and Machine Learning
(ML). By highlighting the existing obstacles and proposing an alternative
approach based on complexity theory and non-linear adaptive systems, the
manuscript offers valuable insights and potential solutions to the challenges
faced by engineers in the field of AI and ML research. Additionally, it aims to
clarify the confusion and misuse of terminology surrounding AI and ML,
contributing to greater clarity and understanding within the scientific
community. AI and ML are attracting a lot of scientific and engineering
attention nowadays, nothing up to now has been achieved to reach the level of
building machines that possess human-like intelligence. However, the
engineering community continuously claims that several engineering problems are
solved using AI or ML. Here, it is argued that engineers are not able to build
intelligent machines, implying that the systems claimed to have AI/ML belong to
different engineering domains. The base of the syllogism is the existence of
four main obstacles on which extensive elucidation is performed. These are (i)
lack of precise definition of AI (and ML), (ii) impossible generation of requirements
and verification and validation procedures for designing and fabricating
machines with intelligence, (iii) no scientific consensus, (iv) philosophical
fundamental issues with AI/ML which impose conceptual and assimilation problems
in order not to be able making progress if not deal with them. In addition, an
attempt to clear out the developed confusion, misuse and abuse of the phrases
“Artificial Intelligence” and “Machine Learning” by scientists and engineers is
carried out. The confusion is a result of the previous obstacles scientists and
engineers are facing and avoid to face, hence creating and growing a kind of
“Lusus Naturae” of this scientific field with socio-political impacts as
well. Furthermore, mathematical, and
philosophical approaches are also mentioned that strengthen the argument
against AI implementability as part of the whole syllogism. Finally, an
alternative approach (not being unique) is suggested and discussed for
performing research on AI and ML by the engineers. It is based on complexity
theory and non-linear adaptive systems and provides the benefit of eliminating
the before mentioned pragmatic and philosophical obstacles that engineers are
facing and ignoring, without creating confusion on this scientific endeavor. This
approach is based on the emergent properties of complex systems. So instead of
trying to make the apple (as a symbol of AI), we build the apple tree which
through complexity the apple will be grown (symbolically AI will be emanated).
Author(s)
Details :-
Nikolaos
Panagiotopoulos
On Board Computers & Data Handling Systems, European Space Research
& Technology Centre (ESTEC), Noordwijk, Netherlands.
Please see the book
here :- https://doi.org/10.9734/bpi/rraass/v9/322
