Is Machine Learning Real Learning?
Abstract
The question of whether machine learning is real learning is ambiguous, because the term “real learning” can be understood in two different ways. Firstly, it can be understood as learning that actually exists and is, as such, opposed to something that only appears to be learning, or is misleadingly called learning despite being something else, something that is different from learning. Secondly, it can be understood as the highest form of human learning, which presupposes that an agent understands what is learned and acquires new knowledge as a justified true belief. As a result, there are also two opposite answers to the question of whether machine learning is real learning. Some experts in the field of machine learning, which is a subset of artificial intelligence, claim that machine learning is in fact learning and not something else, while some others – including philosophers – reject the claim that machine learning is real learning. For them, real learning means the highest form of human learning. The main purpose of this paper is to present and discuss, very briefly and in a simplifying manner, certain interpretations of human and machine learning, on the one hand, and the problem of real learning, on the other, in order to make it clearer that the answer to the question of whether machine learning is real learning depends on the definition of learning.
Downloads
References
Bhatnagar, S., et al. (2018). Mapping intelligence: Requirements and possibilities. In V. C. Müller (Ed.), Philosophy and theory of artificial intelligence 2017. Berlin: Springer.
Bostrom, N. (2017). Superintelligence. Oxford, UK: Oxford University Press.
Bringsjord, S., & Govindarajulu, N. S. (2019). Learning Ex Nihilo. arXiv:1903.03515v2 [cs.AI].
Bringsjord, S., et al. (2018). Do machine-learning machines learn? In V. C. Müller (Ed.), Philosophy and theory of artificial intelligence 2017. Berlin: Springer.
Danziger, S. (2018). Where intelligence lies: Externalist and sociolinguistic perspectives on the Turing Test and AI. In V. C. Müller (Ed.), Philosophy and theory of artificial intelligence 2017. Berlin: Springer.
Floridi, L. (2015). The ethics of information. Oxford, UK: Oxford University Press.
Floridi, L. (2016). The fourth revolution. Oxford, UK: Oxford University Press.
Gettier, E. (1963). Is justified true belief knowledge? Analysis, 23, 1963/6.
Hamlyn, D. W. (1987). Human learning. In R. S. Peters (Ed.), Philosophy of education. Oxford, UK: Oxford University Press.
Hamlyn, D. W. (1987b). Logical and psychological aspects of learning. In R. S. Peters (Ed.), Philosophy of education. Oxford, UK: Oxford University Press.
Mitchell, T. (1997). Machine learning. New York, NY: McGraw Hill.
Mitchell, T. (2017). Machine learning (Draft of the chapter 14). Retrieved from http://www.cs.cmu.edu/~tom/mlbook/keyIdeas.pdf
Nath, R. (2009). Philosophy of artificial intelligence. Boca Raton, FL: Universal Publishers.
Peters, R. S. (1987). Introduction. In R. S. Peters (Ed.), Philosophy of education. Oxford, UK: Oxford University Press.
Reboul, O. (1980). Qu'est-ce qu'apprendre? Pour une philosophie de l'enseignement [What is learning? For a philosophy of teaching?]. Paris: PUF.
Reboul, O. (1995). La philosophie de l’éducation [Philosophy of education]. Paris: PUF.
Ryle, G. (1949). The concept of mind. Chicago, IL: University of Chicago Press.
Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 44(1), 211-229.
Scheffler, I. (1965). Conditions of knowledge. Glenview, IL: Scott, Foresman and Company.
In order to ensure both the widest dissemination and protection of material published in CEPS Journal, we ask Authors to transfer to the Publisher (Faculty of Education, University of Ljubljana) the rights of copyright in the Articles they contribute. This enables the Publisher to ensure protection against infringement.
