Reflection and refraction of light

Laws of Reflection. When a beam of light, traveling in a homogeneous medium, comes to a second medium, some of the light is reflected. At a polished or silvered surface, nearly all the light is reflected. At the surface of clear glass, only a small part of it is reflected. The greater part of it enters the glass and passes through. In Fig. 3, let AB represent the reflecting surface, MP the perpendicular or normal to this surface, OP the incident ray, and PN the reflected ray. The angle OPM between the incident ray and the normal to the surface is called the angle of incidence. The angle MPN between the reflected ray and the normal to the surface is called the angle of reflection. Reflection at such a surface occurs according to the following two laws:

First Law of Reflection. The incident ray, the reflected ray, and the normal to the surface lie in the same plane.

Second Law of Reflection. The angle of incidence is to equal to the angle of reflection.

Refraction. Experiments have shown that light travels with the greatest speed in a vacuum and that it travels with different speeds in different mediums. When it passes obliquely from one medium to another in which it has a different velocity, there occurs a change in the direction of propagation of the light. This bending of the ray of light when passing from one medium to another is known as refraction.

Refraction can be illustrated by taking a cup which is opaque and placing a coin on the bottom of it at the point B, so that the far edge of the coin can just be seen when the eye is at E. If now, without moving the eye, water is poured into the cup, the coin will come completely into view. The ray BA as it leaves the water is bent away from the normal NA. Other rays are in bent in a similar manner, and an image of the coin is formed at C, so that the depth of the coin below the surface of the water seems to have been lessened. Here it is seen that rays coming from the water to the air are bent away from the normal. The rays are always bent away from the normal when they enter a medium in which their velocity is greater than it was in the medium from which they came.

Fig. 3. Reflection of light from a plane mirror. The angle of incidence is equal to the angle of reflection

Text 19 notions of intelligence

It is quite possible to set out an approximate scale of intelligence: most people are more intelligent than most chimpanzees, a word processor is a more intelligent machine than a typewriter, etc. Nevertheless there is no scientific definition of intelligence. Intelligence is related to the ability to recognize patterns, draw reasoned conclusions, analyse complex systems into simple elements and resolve contradictions, yet it is more than all of these. Intelligence is at a higher level than information and knowledge, but below the level of wisdom, it contains an indefinable "spark" which enables new insights to be gained, new theories to be formulated and new knowledge to be established.

Intelligence can also be examined from the point of view of language. Information can easily be represented as words, numbers or some other symbols. Knowledge is generally expressed in a language or as mathematics. Intelligence is at the upper limit of language: instances of patterns or deductive reasoning can be written down, and certain general principles can be stated. However, the creative "spark" of intelligence is almost impossible to express in language.

The only widely accepted definition of artificial intelligence is based on a test devised by Alan Turing in 1950:

Suppose there are two identical terminals in a room, one connected to a computer, and the other operated remotely by a person. If someone using the two terminals is unable to decide which is connected to the computer, and which is operated by the person, then the computer can be credited with intelligence.

The definition of artificial intelligence which follows from this test is:

Artificial intelligence is the science of making machines do things that would require intelligence if done by people.

No computer system has come anywhere near to passing the Turing test in general terms. Nevertheless, progress has been made in a number of specific fields. It would take a very good chess player in the 1980s to be able to tell whether he or she were playing against a computer or a human opponent. Most car drivers are unaware which parts of their cars have been assembled by robots, and which by manual workers.

Conventional data processing is based on information; artificial intelligence is based on knowledge. A central problem for artificial intelligence is an adequate representation of knowledge on a computer. On the one hand the representation must be "rich" enough to be of practical use. On the other hand. it must be simple enough for processing by a computer.

Fig. 4

A semantic network

A technique of knowledge representation which is widely used is semantic networks. As shown in Figure 4, a semantic network shows a set of relationships between objects. It is a flexible method of representation, allowing new objects and new relationships to be added to a knowledge base. Accordingly, semantic networks are often used in computer systems which have some form of learning capacity.