One of the advantages of machine intelligence over human intelligence is that machines are not driven toward poetry. To me, poetry captures the scientific appreciation for the simplest explanations with the fewest number of terms. Humans are innately poets by nature, and even the objectivity of science can not escape the human delight in well-crafted poetry, or human disdain for inelegance in descriptions.
Behind this messy argument is a deeper concern I have that we are doing a disservice to young people by presuming that they really do need more than a decade to learn advanced skills. We can subject young people to more intense education than we are now, and that they could have college-graduate level skills before they become 18 years old. Yet, we think that such an expectation is unwise as if it risks losing something more valuable. Perhaps we fear the young person’s loss to easy access to the presumption of innocence.
The modern era of machine learning, though, presents us an example where we can begin to suspect a separate level of intelligence, and one that feeds on our intelligence. As we sense this happening, we realize that we’ll get no sympathy from the machine for the exact same reason we don’t recognize naturally occurring intelligence in non-humans.
Big data is often characterized as having three V-words: volume, variety, and velocity. While all three present technology problems for storage, the latter words of variety and velocity implies something more than storage. The technologies of predictive analytics is often described as a supplementary technology to big data, but it is more specifically…