programming-for-dummies

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Machine Learning
Such image recognition software examines the shape of a person’s face along
with the physical position of the nose in relation to the eyebrows, lips, and
chin. No matter how many disguises cheaters may wear, they can’t hide or
change the physical dimensions of their faces, which is how such image
recognition programs can spot them.
In the field of robotics, image processing is crucial because it gives robots
the ability to sense their environment and avoid or move toward certain
objects. Teaching a robot to avoid objects in its path might seem easy until
the robot looks through a plate glass window and mistakes the window for
an opening that it can roll through to get to the other side.
Although image recognition might help a robot identify the edges of walls or
the end of a road, image recognition must also teach a computer to recognize
shadows or reflections that can distort images. Primitive image recognition
might simply distinguish between patches of light and dark, but more
sophisticated image recognition could not only recognize an object in its
path but also identify what that object might be, which involves a related
category of artificial intelligence known as image processing.
In a limited domain of objects, seeing and understanding images can be determined
in advance, but in the real world, the number of images a computer
might see and need to recognize is nearly infinite. To solve the problem of not
only recognizing an image but also understanding what that image might be,
computers need to learn independent of their human programmers.
Machine Learning
The preceding examples of problem-solving artificial intelligence programs
only mimic intelligence, but all a computer’s intelligence is programmed by
humans ahead of time. When faced with different situations, these problemsolving
programs behave the same way no matter what the circumstances
might be.
That’s why the second, and more important, focus of artificial intelligence
involves teaching computers to learn, otherwise known as machine learning.
Machine learning can involve training by humans, but it’s more commonly
associated with self-learning in an unsupervised environment.
One way to mimic machine learning is to insert problem-solving knowledge
into a program and then allow the program to modify its database of knowledge.
That’s the idea behind two popular programming languages — LISP
and Prolog — which are specially designed for artificial intelligence.