Consider this post to be the spiritual successor of my previous post, “Probabilities Play an Important Role to Us.”
Probabilities and statistics are both increasingly prevalent and increasingly useful in computing as an undercarriage in artificial decision making (especially in the sub-field of robotics (see LAGR)). Like computers, the human mind makes decisions based on statistical information all the time. Hofstadter realized this a long time ago, so many of his studies are based around a statistical decision-making process. He designed a program called JUMBO which emulates the unconscious cognitive process of “deep perception”; which includes the unconscious act of simultaneously analyzing different outcomes to a scenario and weeding out irrelevant paths one might take. Rather than simply engineering programs which exhaustively, computationally search for possible solutions, Hofstadter preferred strong artificial intelligence: machines which solve problems in a human-like manner.
Probabilities are at the heart of his program JUMBO, which attempts to mimic cognition in order to solve seemingly trivial word puzzles. It “glues” together letters that make sense together in an attempt to make up possible English-like words. However, it has no prior knowledge of which words actually exist. Intentionally not including a dictionary of all the possible English words, he instead supplied JUMBO with a chart of how likely two (or three) letters are to being found next to each other in the English language, along with a strength value. So, the letter combination “sh” might have a strength of 8, while the combination “sq” might have a strength of 3.
Based on this stat chart, JUMBO glues together letters and forms new bundles of letters. These bundles also contain a probability of how likely they are to producing a viable word. Based on these probabilities it will continue to glue letters to other letters or letters to bundles until all the letters are used. Using these hypothetical words it proceeds to fill in the word puzzle (Jumble).
Hofstadter coins this concept of weeding out unlikely candidates a terraced scan. It operates much like how we weed out all the books we aren’t interested in when we visit the bookstore. First we start with a quick surface scan, then take those which seem interesting and perform a deeper scan. Hofstadter intentionally built his system to solve the Jumble word problems in this manner as an attempt to emulate human cognition. Hofstadter urges uncovering our unconscious processing which he coins “deep perception.” This processing happens so quickly we can’t notice it. He argues uncovering it can be a major breakthrough for artificial intelligence, despite some claims from the scientific community that it is a waste of time to explore.
-James
