Human beings have several ways of knowing such as through intuition, perception, language and reasoning. Many other living organism bear these qualities but there are very few that has all and use them effectively. This is one of the characters that no other living organisms possess and may be what shapes us as human. Hence, such advancements in our quality of life have been made whereas all the animals lead similar life to those of centuries earlier because of the mere difference in ways of knowing. We are capable of obtaining knowledge by vision, conversation, feelings, and questioning. Using these four simple tools, us humans can “know”. However, if this is the method of knowing, are the machines aware of the ways of knowing?
Reasoning, in my view is the process of forming conclusions, judgments, or inferences from already existing knowledge to create new knowledge. It is believed that we humans are constantly reasoning. For example, from previous knowledge, I know that males generally have facial hair and do not have jutted breasts. Surprisingly this knowledge is used when I interact with new people. I use the previously held knowledge to form a conclusion of the newly acquired knowledge. Machines work very similar to this. Machines such as computers have been programmed and given specific guidelines to follow in order produce a certain outcome. For example, a broken airplane or a malfunctioning airplane will be tested by other machines to examine the problem. By discovering what is askew and gathering the data, the machine is capable of coming into a possibility of problems. This process is done deductively similar to the male example above. Machines discover whether the broken airplane has “facial hair” and “breasts” and can conclude if it is a male or not. This example suggests that a machine is able to know by reasoning.
A machine works logically by following the axioms and the premises. It takes the steps that are presented in order to achieve a simple task. For example, when we insert 1+1 on our calculator, by using deductive reasoning it will produce 2. The calculator generates rationally by following the simple guidelines such as A+B = C. With these straightforward principles, calculators can formulate any problems involving numbers ranging from negative infinity to infinity. This applies very much to computers. Computers are programmed specially and given specific steps so that the desired outcome is achieved.
Although a machine is able to know by reasoning, they are emotionally limited. Scientist Edward O. Wilson defined emotion as the modification of neural activity that animates and focuses mental activity. This statement refers to living organisms but not machines for they do not experience neural activity and mental activity. Emotion is something very irrational and that fluctuates constantly. In contrast, computers are very static and rational. For example, nowadays there are music programs that can read and play music. However compared to the original version of the masterpiece where actual artists and instrumentalists perform the piece, the program plays the piece in a more monotonic and dull mood without emotion. Emotion plays a big role in music and unfortunately, the program is unable to grasp this aspect. Because the program is designed for the sole purpose of just playing the music, therefore the piece sounds very static and absolutely emotionless.
If a machine is able to know, it must be able to know in at least one area of the knowledge. Mathematics is one area where machines are frequently used. Math is mostly logic where the solution or the answer is definite and absolute. Computers are totally mathematics. They operate on a given formula and generate an equation. With the help of machines we are now able to solve more complex problems using the graphing calculator. We can graph the slope of the curve; find the derivatives and the integrals faster and accurately. Does this indicate that machines are more knowledgeable in mathematically? In a way yes, machines unlike humans do not make silly petty mistakes. There are no errors or mistakes made in the process of following the premises and steps. In addition, the machines are able to read the inputs (numbers) and generate them accordingly, which evokes that the machines actually do understand the inputs and know what they are doing. This is shown by the correctness of the answer. The truthfulness of the solution however, is not important for we assume that the axioms are true. Regardless of the reliability of the axioms, if the conclusion follows the premises logically, then the solution is valid.
With recent developments in technology, translation sites are becoming more popular to multilingual users. Personally, I have resorted to the site at times when necessary however I never had much success with it. Often the translation would be perplexing and grammatically incorrect. In addition, the translation comes out almost like a puzzle forcing the user to fill in the gaps. Although it often misses small but crucial information and are grammatically inaccurate, the site is able to translate the general picture of the passage. In this aspect, translations sites can be useful to absolute beginners and anyone who has no clue of the passage. The translation site suggests how humans more knowledgeable in language and communication, but with the current sites, it demonstrates how machines do have future potential in this area. However, the current site can infer that no matter how many premises and guidelines are installed, machines will never know as much as humans do.
