Lab 10

While Shannon's method uses probability, the Hartley method lacks probability. Hartley uses the log base 2 for all numbers. Because of this, Shannon's is more useful because the amount of uncertainty within a set of alternatives is calculated by how much information is needed.

Lab 9

Within this lab, I basically refreshed my memory of excel when I was in the K-201 course. In this lab however, we learned how to use formulas such as SUM and COUNT which made solving arithmetic for multiple columns much easier. Also, we learned how to create histograms which was extremely efficient but at the same time very easy to do. One of my favorite features of Excel is the drag down function that cuts down on time and work. It is extremely handy when working with several columns and rows. In my opinion, Excel is probably one of the most popular and most useful software that companies use. If a company needs to make lists of eligibility for certain benefits for employees, Excel is easily capable of doing that. There will be a great chance that when I am hired for a company, software like or Excel will be used. I enjoy using Excel and hope to learn more about it within the near future.

Logic Gate #2 with Truth Table

Truth Table
0 0 ] 1 1
0 1 ] 1 1
1 0 ] 1 1
1 1 ] 0 0

Logic Gate #1 with Truth Table

Truth Table

0 0 ] 1
0 1 ] 0
1 0 ] 0
1 1 ] 1

Binary to Decimal and Reverse

To convert 110010101 to decimal, one must remember the formula that Professor Rocha gave us. So 2^8 would equal the 1 on the far left. The number beside that would be 2^7. This would continue to 2^0 which would be on the far right. For every 1, you would calculate the number for it. So for this number, you would add 2^8+2^7+2^4+2^2+2^0. This equals 405. To convert 529 to binary, you must first divide 529 by 2. Since that leaves a remainder of 1, you write a 1 down on the far right. For everytime you get a remainder, you must write a one down. If there are no remainders then write a zero down. 529 is equal to 1000010001 in binary. Positional number systems is a numeral system in which each position is related to the next by a constant multiplier, or a common ratio. This means that each position was presented with a unique symbol or by a limited set of symbols. A non-positional system means that each position does not need to be positional itself. An example of this would be the Hellenistic astronomers used one or two alphabetic Greek numerals for each position (one chosen from 5 letters representing 10–50 and/or one chosen from 9 letters representing 1–9, or a zero symbol), whereas Babylonian numerals used groups of two kinds of wedges representing ones and tens (a narrow vertical wedge ( | ) and an open left pointing wedge (<)) — up to 14 symbols per position (5 tens (<<<<<) and 9 ones ( ||||||||| ) grouped into one or two near squares containing up to three tiers of symbols, or a place holder (\\) for the lack of a position).

Chapter by Andy Clark

In Chapter 6, Clark begins with example of the trails left behind by slugs and ants. As he explains how these trails have multiple functions such as making the production of the mucus trail to help other slugs not have to produce as much mucus, he relates how these paths can be related to humans. An example he used was Amazon.com. In this example he explained how Amazon used a pedestrian trick in that when he bought a certain CD by Nick Cave, it also recommended other CDs that other customers bought who also bought the Nick Cave CD. This method is an effective way to help customers open up to other CDs and other types of music. This technique of relating one person's interests to others is called collaborative filtering. Another way of thinking was created by our own Luis Rocha. This method was called "swarm intelligence." According to Rocha, "relatively dumb individual agents, such as ants and bees, create beautiful, complex, and life-enhancing structures by following a few simple rules and by automatically pooling their knowledge courtesy of chemical traces and structural alterations laid by their own activity" (Clark 48). With collaborative filtering, Norman Johnson believes that this filtering reveals similar characteristics such as to those pheromone-based groups.
Not only are stores now able to recommend what other types of music to listen to, but companies are now creating self-organizing webs. These self-webs is able to create, enchance, and disable links between pages as an automatic result of use (Clark 50). An example of this would be if several users moved from Amazon.com to Ebay.com and then to WorldofWarcraft.com, the web will create a direct line from Amazon to WorldofWarcraft. The web will also configure a prefered way you would want to it be displayed. This is a technique that search-engines such as Google have used to search for what the user desires.
Technology in my opinion has surpassed everyone's expectations. With these tools of relating one person's interests to another, companies have now been able to profit much more and consumers have been able to benefit much more as well. One thing I do contemplate is if such wonderful things will one day more harm than good at the rate technology is growing.