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Theory of Knowledge: Can a Machine Know?

As simple as such a question may appear, it is far more complicated. To even begin addressing this knowledge issue, one must define what Knowing is. According to the Merriam-Webster Dictionary, “to know” has multiple meanings, of which include: “To have understanding of” and “To recognize as being the same as something previously known.” These two contradictory definitions can elicit multiple reasoning for a machine “knowing.” So, by these definitions, for a machine to know can either mean to understand or to recognize, two very different things. A machine knowing is quite dependent on the definition of “to know,” if one defines it as “to understand,” then a machine cannot know, but if one defines it as “to recognize,” then a machine certainly can know in the sense of the processes it is meant to perform.

Epistemology is an important area of philosophy dealing with the theory of knowledge. It focuses determining what is true knowledge and how do we receive the true knowledge. The word knowledge is known in different ways such as understanding, recognizing, grasping etc. According to the tripartite theory of knowledge which is the most popular account of knowledge, there are three conditions to in order to possess knowledge. They are belief, truth and justification. Belief is the first condition for the knowledge. ”Even if something is true, and one has excellent reasons for believing that it is true, one cannot know it without believing it.” (Holt para 2). Truth means the conformity with the reality. If a thing is known it must be true because false cannot be known and therefore, knowledge must be of the truth. “The third condition for knowledge is justification. In order to know a thing, it is not enough to merely believe it; one must also have a good reason for doing so.” (Holt para 4).

The knowledge can be received by two sources and they are known as empiricism and rationalism. There is a constant debate in philosophy on derivation of knowledge whether knowledge is empirical or rational. “In the philosophy of science, empiricism is a theory of knowledge which emphasizes those aspects of scientific knowledge that are closely related to experience, especially as formed through deliberate experimental arrangements.” (Science Reference: Empiricism para 2). Empiricism says that knowledge is obtained through senses and without senses we cannot understand the knowledge. The theory rationalism is quite different from empiricism postulating that the reason is the source for knowledge. In the view of philosophers who support the theory of rationalism in the acquisition of knowledge holds three types of knowledge. First one is the possession of the innate knowledge and they strongly say that innate knowledge is absolutely different from sense knowledge. Second type of knowledge is the truth of logic, mathematic or ethical truth. For example one plus one is two and it is the mathematical truth. It is true and it cannot be other. So also there is logical necessity drawn through the deduction and induction methods in the acquisition of knowledge. Thirdly, the rationalists say that even though there are truths which are grounded in experience, reasoning is important in the derivation of knowledge. For example, if two persons see a flower at a time, their understanding about the flower will be different. This shows that there is ‘reasoning’ among them in order to judge the beauty of the flower.

It is the age of artificial intelligence and “it is the science and engineering of making intelligent machines, especially intelligent computer programs.” (McCarthy para 1). “Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent.” (An Introduction to the Science of Artificial Intelligence para 1).

The artificial knowledge is used in every sphere of life especially in the health sector, information sector, administrative sector etc. At the same time robot is weaker when it is compared to human brain but it is widely believed that the artificial intelligence will cover the human brain in the near future.

“But robotic perception is much weaker in less defined situations, like understanding and responding to human behavior and even conversations.” (Science News: Robotic Perception para 3).

This knowledge issue is so very interesting in the sense that it requires the knower to understand what “knowing” really is, a topic one could argue and discuss for eternity. However, basing the definition on a general consensus, understanding whether a machine can know or not is easier. However, what defines a “machine?” According to the Merriam-Webster Dictionary, a machine is: “a constructed thing whether material or immaterial.” So, if a machine is constructed, then it was constructed to do something for what it was constructed to do, exactly. For example, Bill Gates invented the Windows operating system for Personal Computers on April 7, 1947 which revolutionized the way we live today. Now, we may see this first version of Windows as “imperfect” because it did not do everything we wanted it to do, and did things we did not think it was meant to do; it was imperfect. However, these errors we perceive with our senses are not because of the computer, but because of us. Everything programmed in the operating system was run exactly as it was put in. Simply because the computer did things we did not want it to do, does not mean it is imperfect because it did what it was programmed and built to do; the machine is perfect in the sense it does what it was made to do, but not what we wanted it to do.

So, based on the two definitions of “knowing” we have explored, can a machine know? If using the definition of “to recognize” then a machine certainly can. A machine recognizes laws it was given, or programmed, and executes functions based on these laws. The machine knows nothing else but what it is given and told to do. For example, if one types the letter “a” on the keyboard, the computer recognizes this function and spits out an “a” on the screen. Through this way of knowing, a machine certainly recognizes, or knows, how to respond. However, if using the definition of “to understand,” it is far more difficult to define whether a machine can know or not. The definition of “to understand” also has two definitions: “to make sense of a language” or “to perceive mentally.” Based on the first definition, a machine truly can “understand” because that is all, for example, a computer does. Computers are written in languages, and know how to respond and recognize that language based on its understanding of it. However, based on the other definition, one can argue a machine cannot know because “mentally” is associated with the human mind, which, although possible to simulate, impossible for a machine to have (unless an event occurs such as that in the “Terminator”). Through the ways of knowing, machines can certainly “know” in the sense of understanding and analyzing their surroundings by their sense perception. For example, I once programmed a robot with a motion sensor to respond to movement directly in front of it. When I, for example, walked in front of the robot, it responded appropriately due to its “senses” sensing my movement. As well as sense perception, machines can know through the language way of knowing. For example, my robot which I programmed was written in a language called C++. Since most machines are written in languages, they know how to respond to situations by deciphering these languages and responding appropriately based on how they are programmed. When, for example, my robot sensed the motion of my feet, it processed and recognized this through the written language which I programmed it with; it knew, based on this language, what to do in response to the movement. However, is it the machine independently perceiving its surroundings and deciphering languages, or is it man, whom programmed and built the machine, telling them and doing the “thinking” for them? It is very true that the machine would not exist without man, but is it no different for man? Man would not exist without the Earth. Did the Earth give us this perception and language? Everything, (depending on one’s beliefs), comes from something else in our perceived world. So, it is not man that is doing the perceiving and deciphering for the machine, but the machine itself. Just like a newborn child, once they are created and taught, machines become autonomous and can use this sense perception and language deciphering on its own. Furthermore, this further supports that a machine can “know” in the sense of “to know” meaning “to recognize.” This perception and deciphering of language signifies the machines knowledge, or “understanding,” of how to respond to the motion. However, this does not support this controversial question if one uses the definition of “to know” as “to understand” because, although the machine recognizes a situation through language and sense perception, it does not understand why it is recognizing, or even bothering to recognize the situation and how to respond to it. Through other ways of knowing, namely reason and emotion, “Can a Machine Know?” can be both supportive and refutive. [Researching examples to support and acknowledge counter argument].

In conclusion, we can say that we live in a mechanized world. The machines are invented to fulfill all the activities from the smallest one to complex one. Today, the advanced robots are invented which are able to understand the pulse of the atmosphere. The computerized services are far sophisticated in the health care sector and also all the faster development in every field is due to the advanced technology. Today the major problem in the epistemology is that knowledge is restricted to information technology. Older days the knowledge was the part of person and now it becomes the part of information technology. Therefore, a wide gap is created between the person and knowledge even though knowledge is on the finger tip. The machines can know and understand the changes in the atmosphere and they can respond to those changes.

Works Cited

An Introduction to the Science of Artificial Intelligence. Think Quest, 1997. Web.

Holt, Tim. The Tripartite Theory of Knowledge. Theory of Knowledge. Info, 2006. Web.

McCarthy, John. What is Artificial Intelligence?: Basic Questions. 2007.

Science News: Robotic Perception, on Purpose. Science Daily: Your Source for the Latest Research News, 2009. Web.

Science Reference: Empiricism. Science Daily: Your Source for the Latest Research News, 2009. Web.


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