Will Machine Learning Yield Machine Intelligence?

This paper outlines the non-behavioral Algorithmic Similarity criterion for machine intelligence, and assesses the likelihood that it will eventually be satisfied by computers programmed using Machine Learning (ML). Making this assessment requires overcoming the Black Box Problem, which makes it difficult to characterize the algorithms that are actually acquired via ML. This paper therefore considers Explainable AI’s prospects for solving the Black Box Problem, and for thereby providing a posteriori answers to questions about the possibility of machine intelligence. In addition, it suggests that the real-world nurture and situatedness of ML-programmed computers constitute a priori reasons for thinking that they will not only learn to behave like humans, but that they will also eventually acquire algorithms similar to the ones that are implemented in human brains.