Take Luck! Preface 5

I could not help but touch on one of my favorite subjects, and that is the English language. Hofstadter titled preface 5: "Conceptual Halos and Slippability", and it really made sit back and laugh a little bit when I read it. One of my favorite things in life is catching little slip ups in the English language.

In detail he goes about saying how he believes there are conceptual halos in languages so that words have multiple meanings. Some languages have more or less meanings for different words, and this is very unique. There aren't too many words in the world that are universal across languages, and this needs to be taken into account when traversing across multiple languages.

One of my favorite examples of this is, and it also dabbles into how many AI programs do not have a full grasp on our world, are language translators that you can access on the internet. I know many people who have attempted to do foreign language homework multiple times and have failed. Hofstadter gave a great example in Italian where the sentence Lei ha fratelli? - is "Do you have brothers?", but he could answer Si due sorelle - "Yes, two sisters.", where fratelli can float between "brothers" and "brothers and sisters".

On another quick note, Hofstadter spoke of one my favorite mishaps in the English language and that is when there are word substitutions. I couldn't help myself to find a clip of my favorite comedian making a joke on this because I know I have done this many times while speaking the English language.

http://www.youtube.com/watch?v=9do7HRaGd00

Perception and AI Chapter 5

Chapter 5 of Hofstadter's book is a critique of Artificial-intelligence methodology. I found this critique very interesting and I found Hofstadter and his fellow colleagues who made this article, David Chalmers and Robert French, hit the nail right on the head for me.

When I read about and see these artificial-intelligence programs doing "magnificent" things such as making analogies, making conversation with people, and problem solving programs. When first reading about programs like these I thought that they were doing something truly unique. Thinking that maybe these programs had a real sense of "knowledge" and "concept". After reading Hofstadter's critique it made me really question many of these types of programs.

Hofstadter writes about a problem of relevance that I find interesting. Which information does the program decide to use at a specific time or for a specific situation is interesting. I went and read examples from the ELIZA program and this program seemed to impress me less and less. It felt like an empty conversation, and it felt as though there was no knowledge being put forth. There seemed to be an emptiness that wasn't fulfilled when reading different examples of the ELIZA code.

http://library.thinkquest.org/2705/Programs.html

I found that the ELIZA program had some relevance when conversing with people, but some of it felt as though Koko the gorilla was sitting there signing conversation to Penny Patterson.

http://www.koko.org/world/talk_aol.html

I feel as though many of the AI programs such as ELIZA, or ACME seem to lack a specific feel to it. Like they actually have some sort of grasp on the world, and do not necessarily see it as a world full of patterns, strings, or numbers.

Eliza Effect Preface 4

The more I read into Hofstadter's book the more I tend to agree with almost every word this man has put onto paper. In Preface 4, Hofstadter takes a closer look at some of the noticeable "AI" accomplishments and he then proceeds to carry out a sort of critique of these accomplishments.

One of the more notable programs that Hofstadter critiques is a program called "ACME" that was developed by Keith Holyoak and Paul Thagard. This program was supposed to be able to draw analogies between Socrates' remarks about himself being a "midwife of ideas". The program "ACME" seemed to be given a knowledge base of what midwifery is. It could then switch out strings of information for one another and easily make an "analogy" between Socrates' and what a midwife really was.

Hofstadter immediately shoots this idea down and shows that "ACME" does not "know" the analogy, but merely hides it behind the switching of strings and patterns within the remark. I found this quite interesting and it again brought my attention to Searle's Chinese room argument. It is so that a computer cannot really "know" what specific things are in the world without a seemingly infinite knowledge base that it has acquired through humans? Is it possible for a computer to actually "know" what certain items or objects are, and be able to analogously compare different items in the world to gain its own "knowledge". This to me seems like an interesting topic, and I know this has been discussed previously in many discussions about AI.

Numbo and Myself (Pg 138-154)

The more I read about Daniel Defay's program Numbo the more I use introspection to try and decipher how I solve problems of this sort. Defay speaks of Pnet and Coderack and it makes me think of this is how human processing really does occur. I myself sometimes struggle with problems that are in the mathematical realm, and I keep looking into my own mind to try and see how I solve these problems.

Defay easily puts on paper how Numbo works and uses a great description and also great diagram from a trace run of Numbo. The main question that I have in regards to Numbo is if it really shows some sort of intelligence when solving these problems. When first reading about Numbo it seemed to me that it merely took one path and tried it until it failed and disregarded other paths that could be correct. In this section of the reading Defay put that unsettling feeling to rest in my eyes. He states that Numbo, unlike much of the previous artificial intelligence programs out there, keeps goal competition going on without disregarding other paths. Some paths will however be "stronger" than others and I thought this was key to his Numbo program.

Numbo to me shows great promise and it surprised me in a way. The architecture that Defay uses is interesting and to me shows "intelligent" like qualities which can be applied to more areas of AI.

So Defayntastic (127-138)

In this section of our assigned reading Hofstader strays away from his own personal work and looks at the work of one of his colleagues, Daniel Defay, that was on sabbatical from the University of Liège in Belgium. Defay was interested in learning more about Artificial Intelligence and the like. Defay had contacted Hofstadter with a great interest in a specific type of project that he wanted to pursue. Defay had a great interest in creating a program that was very similar to Hofstadter's Jumbo program. Defay however did not want to solve problems dealing with letters and words, but wanted to solve problems with numbers.

Defay's program, called Number, was based on a television show called Le compte est bon. This program would take a set of 5 numbers and would be given a goal number to try and achieve from the set of 5. Not all of the numbers need to be used as long as the goal is reached. I immediately thought of the crypto problems that we had been solving in class (where we have a set of 5 numbers and are given a goal number to reach. We can use the basic operators, +,-,x,/, to solve the problem and all numbers must be used). In Defay's program however only addition, subtraction, and multiplication may be used. I thought this would make it much easier for me, but it didn't.

Defay goes through his program and it made me think about how I actually solved the problems that he had given within the reading. He had given different examples for readers to try and solve and I found a few of them quite challenging when in fact they were really quite simple. It makes me wonder how we really do look at these problems in our minds. Defay states that there are "bricks" of numbers that are quite salient that I overlooked completely. This shot down my confidence more with math and made me appreciate Jumbles just a little more. It makes me feel as thought our brains are sometimes more open to solve language problems due to the use of language more than mathematics, but that is just my opinion ( a very biased one at that since I do not like Math as much as I really should). Defay explains this in his book L'esprit en friche, which I hope to read quite soon. His Numbo program is an interesting program that tries to look at human level problem solving with mathematics. I hope to find a copy of his book sometime in the near future.

Jumbo and Trees

When reading about Hofstadter's program Jumbo there were a few questions that came to my mind in regards to its abilities to solve Jumbles. I looked into my own mind when solving Jumbles and so many things came forward. How I first off don't like to solve Jumbles, secondly, that when I look at a word I just randomly throw letters together when I can't solve a word to hopefully bring forth a coherent word.

It made me think of how Jumbo solved the Jumbles that it was given. Hofstadter clearly states in the assigned reading, from pages 111 to 126, that there is a clear way in which he achieved this. Hofstadter explains that Jumbo uses a "tree" like hierarchical structure to solve the problem at hand. It randomly chooses different paths to add letters and if it does not pan out, Jumbo uses backtracking to randomly select another path to take. I find that Hofstadter's thought process on this is right on the mark. Selecting specific pathways with rules and other things of the like. There are however biases put in so that the Jumbo has gloms that are of higher urgency and also of lower urgency. This can separate out gloms that are not important to Jumbo.

I enjoyed Hofstadter's segment on Jumbo and how he explained how his program really worked. It's a very interesting idea that definitely struck my interest, and I'm excited to see where Hofstadter goes from this point on.

To Arthur!

First off, Happy To Arthur Guinness day! In this weeks assigned reading, Douglas Hofstadter talks about his artificial-intelligence research project called Jumbo. Jumbo’s purpose is to act like a human who is trying to solve a Jumble problem by taking a group of letters, then trying to take the letters and make it so that it can seek an English word out of the given letters.

Hofstadter did not give Jumbo a dictionary, because he thought that it defeated the purpose of what he was trying to replicate, in essence, human thought processes. What he did was give Jumbo instructions on how the English language makes its constructions, such as how consonant and vowel clusters are formed out of letters, syllables out of clusters, and words out of syllables. This immediately made me think of John Searle’s Chinese room argument, and made me think if Jumbo could really show some sort of human like intelligence, or an understanding of what it actually was doing.

I think Hofstadter was going in the correct direction when he was designing Jumbo and his thoughts and ideas that are there are very good. However, there is always the slight bit of doubt that an artificial-intelligence program is merely doing the work, but still does not have an understanding of what it is doing. I look forward to reading more about Jumbo to see the outcome of Hofstadter’s program.

Jumble

Word jumbles have been my arch enemy for quite some time now, along with crossword puzzles and sudoku. In the reading from pages 87-95, Hofstadter talks a great deal about anagrams and his attempt at writing a computer program that attempts to make "...English-like words out of a set of letters by rearranging them and putting them into plausible order."

Hofstadter talks about how he has enjoyed solving Jumbles for quite some time. I on the other hand have not had great luck in anagrams even though I do enjoy them. His Jumbo program seems like an interesting program, but I feel as though it'd be a very difficult program to write. After reading about how there were brute-force anagram programs that used abridged dictionaries, I was more interested on Hofstadter's take on how he would accomplish this goal. It seems to me that there would be a plethora of answers for a particular set of letters when doing an anagram with one of these brute-force programs, or even with his Jumbo program. He does have a great interest in human cognition and he does state that this is a problem and that it this is the reason why he does not like the brute-force programs.

After reading this section I thought about when I was taking Linguistics 100 a few years ago. How there are so many difficult rules in the English language and how his Jumbo program could decipher all of the different rules that the English language carried. It only makes me think that there could be mistakes somewhere down the line just like the human language.

Ambiguous

I found that the section that we were assigned to read by Hofstadter was a little confusing at some points, but this was only due to the nature of his writing style. Like a normal conversation Hofstadter jumps from point to point and also goes on a miniature rant about conceptual spheres. Where people take a primary concept and they then take this concept and spread it to a conceptual space that is shared by many other individuals. I thought this was a very interesting topic that Hofstadter points out, and he only makes that even better when he expands his idea into the realm of grammar and logic.

Hofstadter talks about generalizations and how they spread outwards from a conceptual centerpiece. When we read or hear something we tend to take this and apply it and relate it in some way or form to things that have happened in our own lives. I find this interesting and he example of the “Me-Too” phenomenon and his examples. The one example that I really enjoyed was when he stated an exchange of words between people named Shelley and Tim:

Shelley: I’m going to pay for my beer now.

Tim: Me, Too.

Tim’s reply in itself is ambiguous. There could be many interpretations that could steer someone off course and they might get the wrong impression. The great part about something like this is that we as humans use phrases like this everyday and do not think anything of it. We have taken our language and have used it to a new degree, where we can say things such as ambiguous statements and people can draw correct inferences from them and we can be on our merry way.

This also made me think about how this could make for a tough time in Artificial Intelligence programming. Human languages are so complex and have so many different phrases, uses, and tricks that it would be very difficult for a machine to pick up on some of these. I feel as though a phrase such as “get a hold of yourself”, or “get a grip” would cause a machine to sort of problems. It also made me think of Kim Peek and how he, one of the most unique and gifted individuals in the world, cannot understand the use of the phrases such as “get a hold of yourself.”

Analogy-Making Lies At The Heart Of Intelligence

The part of Hofstadter's book that I am going to be focusing on today came from his section entitled "The Key Role of Analogies" on page 62 of his book Fluid Concepts and Creative Analogies. In this section, Hofstadter begins to talk about how analogy-making is used in seeking out "Islands of Order" in pattern sequences (read page 58 for more details on Islands of Order).

This seemed to me like a very important concept in the daily lives of humans. When we look at everyday objects, numbers, or things of the like we either relate or differentiate between these things to make note of them within our minds.

This is a quote that I’d like to take from Hofstadter’s passage that I had enjoyed: “…pattern-finding is the core of intelligence, the implication is clear: analogy-making lies at the heart of intelligence. Yet these extremely simple ideas have seldom been stated in cognitive science, let alone explored in detail.” This reminded me of the research that I had done 2 summers ago when I was attending the URSI program at Vassar College with Professor Jan Andrews and Ken Livingston.

The research that I took part in was on Category Learning with the use of 3-D objects in a virtual world. The virtual world we used was Second Life. Hofstadter talked about how “Analogies vary not only in their degrees of salience but also in their degrees of strength.” I could not help but reminisce of doing research and how his ideas about analogies closely correlated with the ideas put forth in some of the research.

In the research that we did we had 2 sets of objects, a gex and a zof, that varied slightly from one another within their respected groups. We had extreme forms in both of the objects and we also had forms of each object that looked as though they crossed the threshold to where they looked the exact same when in actuality they did not. The participants would then go through a random order of these objects and try to categorize and choose which form the object was that they were looking at (either a gex or zof). This is where I saw the connection between Hofstadters looks on analogies and the research that I took part in.

Though the research did not include mathematical sequences I do believe that there is some sort of connect in a broader sense. I could keep going on this topic, but I know that I only have a set number of words that I am allowed to use in my blog entry for this class and I am really cutting it close to the amount of words that I’m using. I can post more on this topic later at some point if need be.

Music and Mathematical Patterns

In this blog entry I will basically be continuing what I was talking about from my first blog entry. Without reading further in the book until Wednesday night I stated that " It makes me think about other patterns that may be occurring within other aspects of our natural world." It made me happy to know that I was on the correct path and also on the same page as Hofstadter.

In the most recent section we were assigned to read Hofstadter talked about how he had stretched his love of patterns into the realm of music. This struck me by surprise. I've been involved with music since a young age and have known that many patterns had existed in music, but I had never even thought to look there for patterns. When I was thinking about the natural world and patterns that could be involved I seemed to have skipped over music all together. To add even more to this I was even listening to music while doing some of my reading and thinking.

Music is made up of patterns, which really makes music unique. Hofstadter points out that music can be put into a hierarchical system, where the notes are split into chunks. Reading about this made me really think about music in its entirety. How amazing and mysterious music can be, and how it too can be just as complicated as mathematical patterns.

I look forward to reading more of this book. Not only because we are merely "told" to read, but because Hofstadter takes a difficult and interesting topic and makes it so you can really understand what he is talking about in such a simple way.

Mathematical Patterns and Relationships

I'd like to first start off by saying how I really enjoy the style of writing that is used by Douglas Hofstadter in his book Fluid Concepts and Creative Analogies. It feels as though you're not reading a book, but rather sitting down and having a real talk with Hofstadter. I look forward to reading more of this book not only because of the topic, but also because of his very unique and down to earth writing style.

Hofstadter has a real love and knack for finding patterns within mathematics. His search for finding a pattern for the triangular numbers between the squares was very interesting. I never would have thought that there were such patterns that existed within the world of mathematics. I am not very keen at math, and I usually have little interest in mathematical problems. This problem however caught my attention. It's amazing how unique and mysterious math can be.

I find it incredible that such patterns could exist from different strings of integers. It only makes me wonder what other kinds of patterns there could be with other types of integers. These patterns don't necessarily have to just be confined to the mathematical world either. It makes me think about other patterns that may be occurring within other aspects of our natural world. Making it seem as though our world can all be related in some way in some form. It's a very interesting concept that Hofstadter put forth that really made me see new and interesting ideas about patterns, math, and the natural world.