The preface to chapter 5 of Hofstadter’s book reveals an introduction to the concept of slippage and the program that Hofstadter worked on that deals with making analogies, called Copycat. Hofstadter does a great job in describing slippage, that is slippage from a cognitive stand point and not to be confused with a lack of mechanical traction. Hofstadter uses the following examples to explain the concept of cognitive slippage (when dealing with oration):
“No one could get tickets on that flane.” This is a slippage of combing flight and plane.
“Don’t shell so loud.” This is a slippage of the words shout and yell.
Question: “Plastic bag alright?”
Reply: “I prefer a wood one… uhh, a… a paper one please.”
The question and reply slippage from above is a different kind of slippage than the combination of two words that mean the same thing within the given context. But the question and reply slippage which occurs demonstrates that slippage seems to combine like minded concepts. That is to say that in the above examples that the combination or replacement of one word for another, involves aspect of likeness. Flane, which is a combination of flight and plane, both deal with locomotion through the air, shell which is a combination of shout and yell, both deal with someone talking in a loud, maybe even aggressive manner. The wood for paper replacement for the third example, deals with the idea that paper is made from wood and that grocery bags (contextually based for this example) have a brown color like wood, unlike writing paper which tends to be white.
Thursday, October 29, 2009
Bacon and Kepler
“One of the deepest problems in cognitive science is that of understanding how people make sense of the vast amount of raw data constantly bombarding them from their environment. The essence of human perception lies in the ability of the mind to hew order from this chaos, whether this means simply detecting movement in the visual field, recognizing sadness in a tone of voice, perceiving a threat on a chessboard, or coming to understand the Iran-Contra affair in terms of Watergate.”(169,Chambers,French,Hofstadter)
The above quote is exactly what I was referring too in my previous blog, that is, the ability of the computer, machine, or etc, to recognize, perceive, or otherwise ‘know’ what to make of all the data that floods it. One of the more interesting concepts that Hofstadter mentions is for a program or a computer to distinguish between truth and falsehood and also the ability to discern what is important and what is not important to a given problem. This is best illustrated when Hofstadter .et al describe the example of Bacon solving Kepler’s Third Law of Planetary Motion. This example explains in a nutshell what I believe to be one of the most important concepts within the field of AI. If a computer has the ability to discern between truth and fallacy and the ability to discern between the relevant and irrelevant, then the computer or machine could begin to solve problems in a more human like way.
I also liked reading about the different programs such as Bacon and SME. Hofstadter does a great job in detailing the reasons that these programs lack in the way of human perception and the ability to solve problems, mainly as a result of the programs being fed only the relative data that it needs, such as is the case with Bacon and ‘its’ discovery of Kepler’s Third Law of Planetary Motion. But having said this I also believe that at times it felt that Hofstadter was doing more than just critiquing these programs, by not giving the programs the proper respect that they deserve for the job that they do and the advances that the give to the field of artificial intelligence and cognitive science.
The above quote is exactly what I was referring too in my previous blog, that is, the ability of the computer, machine, or etc, to recognize, perceive, or otherwise ‘know’ what to make of all the data that floods it. One of the more interesting concepts that Hofstadter mentions is for a program or a computer to distinguish between truth and falsehood and also the ability to discern what is important and what is not important to a given problem. This is best illustrated when Hofstadter .et al describe the example of Bacon solving Kepler’s Third Law of Planetary Motion. This example explains in a nutshell what I believe to be one of the most important concepts within the field of AI. If a computer has the ability to discern between truth and fallacy and the ability to discern between the relevant and irrelevant, then the computer or machine could begin to solve problems in a more human like way.
I also liked reading about the different programs such as Bacon and SME. Hofstadter does a great job in detailing the reasons that these programs lack in the way of human perception and the ability to solve problems, mainly as a result of the programs being fed only the relative data that it needs, such as is the case with Bacon and ‘its’ discovery of Kepler’s Third Law of Planetary Motion. But having said this I also believe that at times it felt that Hofstadter was doing more than just critiquing these programs, by not giving the programs the proper respect that they deserve for the job that they do and the advances that the give to the field of artificial intelligence and cognitive science.
Wednesday, October 28, 2009
The Eliza Effect
The Preface for chapter 4 in Hofstadter’s book deals with the anthropomorphizing of computer programs that deal in some way with artificial intelligence. Although I had up until this point never heard of the Eliza Effect, it appears to imply that human tendency is to give human though characteristics and the ability for computers to take real world information (with knowing nothing of the real world) and for the purpose of creation. Anthropomorphism on the other hand is within the same conceptual sphere as the Eliza Effect, but includes giving human qualities and characteristics to things that are not human including machines.
Besides the ideas that Hofstadter explained in the preface, I feel that for in the realm of artificial intelligence, for a computer or a machine to really be able to think, to be able to create a work of fiction, or to create analogies and metaphors, like the examples that Hofstadter spoke of two very important concepts have to be made real for computers. First computers or machines have to have the ability to learn, not just have the ability to store and recall information that is supplied by its creator, programmer, or user, but to be able to learn from and extract further information from the data that it is supplied with. Lastly I think that a computer has to be self aware, if a computer can become aware, that it is has to know what something is for what it is not just a string of zeros and ones that represents a real idea, concept, or physical object. It has to be able to distinguish between what is abstract and what is physical.
Besides the ideas that Hofstadter explained in the preface, I feel that for in the realm of artificial intelligence, for a computer or a machine to really be able to think, to be able to create a work of fiction, or to create analogies and metaphors, like the examples that Hofstadter spoke of two very important concepts have to be made real for computers. First computers or machines have to have the ability to learn, not just have the ability to store and recall information that is supplied by its creator, programmer, or user, but to be able to learn from and extract further information from the data that it is supplied with. Lastly I think that a computer has to be self aware, if a computer can become aware, that it is has to know what something is for what it is not just a string of zeros and ones that represents a real idea, concept, or physical object. It has to be able to distinguish between what is abstract and what is physical.
Wednesday, October 7, 2009
Solving A Problem
Daniel Defay’s paper on Numbo presented in Hofstadter’s book provides an interesting look into some of the logic that is used in creating a computer representation of what the human brain might try to do when solving problems using mathematics. In Numbo, Defay explains the use of codelets, the pnet, and the cyto-net in trying to solve a given mathematical problem based on some random big target number and only using a random amount of smaller numbers with addition, subtraction, and multiplication.
Although Defay readily explains the basics of the architecture, the reasoning behind his methods used, and the fact that in many respects Numbo is similar in design to Hofstadter’s Jumbo, Seek-Whence, and Copycat programs, I thought that the best part of Defay’s paper was in the concluding sections, more importantly the section that describes his human test subjects and the methods that the test subjects used to solve the a stated Numble problem and how they compared to Numbo’s solving of a Numble problem. I believe that this is important because it shows the parallels to the human brain and the Numbo computer program as well as showing the difficulties a programmer, or cognitive scientist might have in recreating brain function in a computer program.
“Another factor that would certainly complicate any comparison between Numbo and people is the highly questionable nature of human protocols. I was repeatedly told by solvers that it is difficult, if not impossible, to keep track of everything going on when one is solving a problem.” (Hofstadter, 151)
I think that this statement by Defay sums up the main problem of analyzing the way the human brain solves problems, nicely.
Although Defay readily explains the basics of the architecture, the reasoning behind his methods used, and the fact that in many respects Numbo is similar in design to Hofstadter’s Jumbo, Seek-Whence, and Copycat programs, I thought that the best part of Defay’s paper was in the concluding sections, more importantly the section that describes his human test subjects and the methods that the test subjects used to solve the a stated Numble problem and how they compared to Numbo’s solving of a Numble problem. I believe that this is important because it shows the parallels to the human brain and the Numbo computer program as well as showing the difficulties a programmer, or cognitive scientist might have in recreating brain function in a computer program.
“Another factor that would certainly complicate any comparison between Numbo and people is the highly questionable nature of human protocols. I was repeatedly told by solvers that it is difficult, if not impossible, to keep track of everything going on when one is solving a problem.” (Hofstadter, 151)
I think that this statement by Defay sums up the main problem of analyzing the way the human brain solves problems, nicely.
Sunday, October 4, 2009
Introducing Numbo
In this section of the Hofstadter’s book “Fluid Concepts and Creative Analogies”, Hofstadter includes Daniels Defay’s “Numbo: A Study in Cognition and Recognition”. In the first section of Defay’s article on Numbo, he summarizes as set of observations made during the Numble examples as: “The amount of knowledge available determines the nature of the strategy followed”. I believe that the statement is true enough, for almost everything that humans do in day to day life. But I think what really draws me to the statement is the fact that a statement as simple as this unless directly brought attention to is something that humans do without realizing that they are doing it. By bring attention to the common place and everyday it makes analyzing problems easier and the process by which the solutions are formed less complicated to note and understand. And may implore one to think about the processes that he or she takes in thinking and problem solving.
The problem solving process and thinking about the methods in which humans solve problems I think is an area that will greatly help the future of artificial intelligence. It reminds me of Hofstadter’s explanation about why he designed Jumbo the way he did. I believe he made comment of something like it wasn’t whether or not Jumbo could come up with a real word but the process in which Jumbo took to form the word. Which in hindsight is why Jumbo wasn’t encoded with a dictionary but used letters, gloms, and codelets. It will be interesting to see how Numbo and Jumbo compare on a process basis.
The problem solving process and thinking about the methods in which humans solve problems I think is an area that will greatly help the future of artificial intelligence. It reminds me of Hofstadter’s explanation about why he designed Jumbo the way he did. I believe he made comment of something like it wasn’t whether or not Jumbo could come up with a real word but the process in which Jumbo took to form the word. Which in hindsight is why Jumbo wasn’t encoded with a dictionary but used letters, gloms, and codelets. It will be interesting to see how Numbo and Jumbo compare on a process basis.
Wednesday, September 30, 2009
Its In The Context
One of the more important themes in this section of Hofstadter’s book is the theme of backtracking. I think that the idea of backtracking relates particularly well to the way that humans might solve a particular problem. For example a person driving from city A to city B, would start all the way back at city A if he or she gets lost along the way, he or she will back track to the most recent spot where they weren’t lost and from there proceed on to city B, this time hopefully on the right path.
However I do believe that there are times when starting from the beginning is the easier choice to make. I thought about this when Hofstadter was talking about unhappy gloms. Suppose, instead of gloms being composed of a grouping of letters that form words, the gloms are a grouping of words that form sentences. An author knows that the first sentence in any body of work is one of the most important sentences for that work. It is the sentence that hooks the reader to read further, so the author wants to get that first sentence right. During the editing process the author decides that the first sentence needs some work, now it could be that the gloms need to be rearranged or the order of the gloms needs to be changed. Or it could be that the first sentence is entirely removed and re-glommed to form a sentence that is much more effective.
I agree with Hofstadter in the context of Jumbo that the idea of backtracking may be the more intuitive approach to reform a word from gloms, and in fact may very well be the more intuitive approach to many problems solving situations. I do think that it is in the context of the problem to be solved whether or not backtracking or starting over is the easiest and best approach.
However I do believe that there are times when starting from the beginning is the easier choice to make. I thought about this when Hofstadter was talking about unhappy gloms. Suppose, instead of gloms being composed of a grouping of letters that form words, the gloms are a grouping of words that form sentences. An author knows that the first sentence in any body of work is one of the most important sentences for that work. It is the sentence that hooks the reader to read further, so the author wants to get that first sentence right. During the editing process the author decides that the first sentence needs some work, now it could be that the gloms need to be rearranged or the order of the gloms needs to be changed. Or it could be that the first sentence is entirely removed and re-glommed to form a sentence that is much more effective.
I agree with Hofstadter in the context of Jumbo that the idea of backtracking may be the more intuitive approach to reform a word from gloms, and in fact may very well be the more intuitive approach to many problems solving situations. I do think that it is in the context of the problem to be solved whether or not backtracking or starting over is the easiest and best approach.
Wednesday, September 23, 2009
Bonds, Chains, And Gloms Oh My
What I found particularly interesting about Hofstadter’s brief explanation of Jumbo is the process that Jumbo goes through to form its possible word candidates for the “Jumble” puzzle. As Hofstadter explains it isn’t necessarily if the word formed is correct, but the process by which the word is formed. This suggests to me that Jumbo doesn’t actually solve the “Jumble” puzzle but tries to form words using the anagrams presented.
What really peaked my interest was the partial rule base that Hofstadter provided in this section. In this he states that his rule base wasn’t based on any kind of letter to letter frequency but only on priorities that he himself believes viable in his own process for forming words in the “Jumble”.
I believe that Hofstadter has thought deeply about his own process for forming words using anagrams. I however don’t know if that is exactly how I form words trying to solve anagrams. Maybe on some level, it is very similar to the process that I use. I will certainly have to think more about it to come to any kind of conclusion on the differences between his process and my own.
After reading this section it reminded me about something that I had seen on the internet a few years ago. Research at Cambridge had discovered that it doesn’t matter the order of the letters as long as the first and last letter of a word are correct, people would have a tendency to figure the word out with almost no effort. This is not the original article but here is a link to an example of this.
http://www.mylittleportal.com/mixed-letters-still-readable
What really peaked my interest was the partial rule base that Hofstadter provided in this section. In this he states that his rule base wasn’t based on any kind of letter to letter frequency but only on priorities that he himself believes viable in his own process for forming words in the “Jumble”.
I believe that Hofstadter has thought deeply about his own process for forming words using anagrams. I however don’t know if that is exactly how I form words trying to solve anagrams. Maybe on some level, it is very similar to the process that I use. I will certainly have to think more about it to come to any kind of conclusion on the differences between his process and my own.
After reading this section it reminded me about something that I had seen on the internet a few years ago. Research at Cambridge had discovered that it doesn’t matter the order of the letters as long as the first and last letter of a word are correct, people would have a tendency to figure the word out with almost no effort. This is not the original article but here is a link to an example of this.
http://www.mylittleportal.com/mixed-letters-still-readable
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