The purpose of this example is to illustrate how good humans are at recognizing items based on clues from the context. From the "Help, I've fallen and I can't get up" example it is useful to point out that if you saw the modified letter 'H' alone you probably wouldn't have been able to identify it. However, given the context of the word "Help" and the phrase as a you most are able to identify the letter as being an 'H'.
The challenge with many AI recognition systems is to enable the computer to use the context to identify objects.
One example of a recognition system that is currently available commercially is a Optical Character Recognition (OCR) systems. These systems, used in collaboration with scanners, can be used to convert paper documents into computer files suitable for storage and searching.
Another area where 'recognition' systems are becoming relevant is in the realm of information filtering. It is important to realize that in order to screen information it is necessary to 'understand' the content of the documents that are being screened. An example of this might be a filter that can scan the subject lines of e-mail messages and flag certain messages for further investigation. This type of system also must use some context to 'understand' the messages.
Another type of recognition system that has recently become commercially available is handwriting recognition systems. The most famous (or infamous) example of a handwriting recognition system is the Apple Newton. Interestingly enough these systems have many of the same limitations as the speech recognition systems described above.
Robotics Systems
In order to demonstrate some of the limitations and challenges of robotics the following demonstration was used:
At the start of the lecture ask the students to write a set of instructions for making a peanut butter and jelly sandwich. Collect the instructions from the students. Then tell the students that you will be using the directions to actually make some sandwiches. (Using some peanut butter, jelly, bread, and knifes that you have brought to class) Then, following the instructions exactly, demonstrate the difficulty of giving instructions to a being that has no common sense.
Interesting things that can occur during the demonstration are:
If the instructions don't specifically say to open the bag of bread, the jar of peanut butter, or the jar of jelly then the 'robot' should have a difficult time.
If the instructions specify that the the bread should be picked up, the knife should be picked up, etc, but never specify that the objects should be put down the 'robot' will begin to have problems.
The purpose of this example is to illustrate that information about spatial relationships and other commonsensical information is not known to a robot.
Common applications of robots include:
Stationary robots : Factory automation robots are an example of this. Mobile robots : Creating mobile robots that can endure harsh environments, drive in routine situations, or perform repetitive tasks over a large area. 1. NavLab - A driving 'robot' 2. Ambler, Dante and Dante II - Walking robots for hazardous conditions Vision Systems
The challenges and importance of vision systems were demonstrated with the following two demonstrations:
Demonstration 1 : Context and Vision Systems
Drop a small pile of rubber band (1 dozen or so) in front of one of the students. Ask the student to count the rubber band, without touching them. After the student completes that task, ask another student to lift one rubber band at a time using a stick, without disturbing the other rubber.
After this demonstration a discussion can be structured around what knowledge/techniques the students used to identify and manipulate the individual rubber bands. This can be used to demonstrate the importance of context for vision systems.
Demonstration 2 : Two-Dimensional Representation of Three-Dimensional Objects
Show the students the following picture:
[Image]
Ask the students what they see. Most answers will be things like: the a top view of a box that is projecting out of the board or a inside view of a box embedded in the board. At this point tell them that the picture actually is a single square with an X in it and another square on top of it (as shown below).
[Image]
This demonstration is intended to illustrate the interesting ability of many of us to use certain context cues to see 2-d representations as 3-d objects. This is an important skill for a driving robot to have - If it sees a square with an X and another square then it is likely to hit the first box-like object it encounters.
Another application of vision systems is monitoring images, such as satellite images for changes or monitoring manufactured parts for flaws.
Neural Networks
To demonstrate the value of neural networks (or neural nets) the following demonstration was used.
Write the following sentences on the board:
Madam, I'm Adam. A man, a plan, a canal - Panama Never odd or even. Step on no PetsThen ask the students if they can identify what the sentences have in common. After a few minutes also write the string of digits "11011011" on the board and say that the sentences are also similar to this string of digits.
The sentences are all palindromes or sentences (or strings of digits) that read the same from right-to-left and left-to-right. This is an example where there is an underlying pattern that is disguised by the context.
Essentially neural nets are software systems that attempt to identify patterns such as the palindrome pattern in spite of a distracting context.
An example of how this pattern recognition capability of neural nets is used in a business environment is its use by catalog retailers to identify potential customers. Using demographic information and records of past purchases retailers can use neural nets to identify customers who are most likely to purchase items (i.e. match a certain 'pattern') and this information can be used to target mailings and advertising.