Capabilities of Engineers to Build Machines with Human-like Intelligence Using Artificial Intelligence (AI) and Machine Learning (ML)

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).