Wordology, Crocodility

I love the way that word rolls off the tongue. Crocodility is an ancient word for fallacious reasoning

See if you can follow this paradox. A crocodile snatches a young boy from a riverbank. His mother pleads with the crocodile to return him, to which the crocodile replies that he will only return the boy safely if the mother can guess correctly whether or not he will return the boy.

There is no problem if the mother guesses that the crocodile will return him. If she is right, he is returned; if she is wrong, the crocodile keeps him. If she answers that the crocodile will not return him, however, we end up with a paradox: if she is right and the crocodile never intended to return her child, then the crocodile has to return him, but in doing so breaks his word and contradicts the mother’s answer. On the other hand, if she is wrong and the crocodile actually did intend to return the boy, the crocodile must then keep him even though he intended not to, thereby also breaking his word.

The paradox is such an enduring logic problem that in the Middle Ages the word 'crocodilite' came to be used to refer to any similarly brain-twisting dilemma where you admit something that is later used against you.

Moravec's Paradox

Hans Moravec, adjunct faculty member at the Robotics Institute of Carnegie Mellon University, pointed out that machine technology mimicked a savant infant. Machines can do long math equations instantly and beat humans in chess, but they can't answer a simple question or walk up a flight of stairs (until recently). He, along with many others has been working to solve that paradox and help computers evolve on their own.

Early artificial intelligence (AI) researchers believed intelligence was characterized as the things that highly educated scientists found challenging, such as chess, symbolic integration, and solving complicated word algebra problems. They thought, if those could be done so easily by computers, things that children of four or five years could do effortlessly, such as visually distinguishing between a coffee cup and a chair, or walking around on two legs, or responding to words would be infinitely easier for computers to learn.

Computers/robots are finally beginning to move and think like people. Narrative Science can write earnings summaries that are indistinguishable from wire reports. We can ask our phones, 'I'm lost, help.' and our phones can tell us how to get home. (The smartphone was introduced in 2007, just seven years ago.)

Computers that can drive cars were never supposed to happen and ten years ago, many engineers said it was impossible. Navigating a crowded street requires a combination of spacial awareness, soft focus, and constant anticipation. Yet, today we have Google's self-driving cars and they have been approved by some states as allowable on city streets. Ten years from impossible to common.

IBM, working with Memorial Sloan-Kettering cancer information is using its computers to diagnose diseases and the Cleveland Clinic to help train aspiring physicians. It just invested a billion dollars to set up 'Watson' into a separate business unit for medical and other complex decision making activities.

Bottom line, we are experiencing solutions to the paradox and it is very exciting, although I am not sure machines will ever replace the following or that we will ever want to.