Vapnik is one of a growing body of artificial intelligence (AI)
researchers discovering something that teachers have long known—or at
least, believed—to be true: There is a special, valuable communication
that occurs between teacher and student, which goes beyond what can be
found in any textbook or raw data stream. By bringing the tools of
computation and machine intuition to the table, AI researchers are
giving us a more complete picture of how we learn. They are also
broadening the study of education to include quantitative, numerical
models of the learning process itself. “The thing that AI brings to the
table is that it forces us to get into the details of how everything
works,” says John Laird, a computer scientist at the University of
Michigan. If there was any doubt that good teachers are important,
machine learning is helping put it to rest.
The teacher-student code has its roots in the sheer complexity of the real world, a complexity that has long bedeviled AI research. Is that flat surface a table? A chair? The floor? What if it’s partly in shadow, or partly obscured? After years searching for simple ways to answer these questions, the AI community is finding that the complexity of the real world is, in some ways, irreducible.
[--]
“This means that a good decision rule is not a simple one, it cannot be described by a very few parameters,” Vapnik said. In fact, he argues that using many weak predictors will always be more accurate than using a few strong ones.1 One approach to capturing complexity is to feed hundreds of thousands, or even millions, of points to a computer, which is called brute force learning. It works well enough, and is the driving engine behind most Big Data commercial enterprises, in which machines are set loose on terabytes of data in order to understand everything from scientific problems to consumer behavior. In fact, Vapnik developed one of the key technologies used by Big Data, called Support Vector Machines. But brute force methods are also slow, inefficient, and useless when data is not plentiful, such as when studying biopsy images for cancer.
Vapnik describes privileged information as a second kind of language with which to instruct computers. Where the language of brute force learning consists of technical measurements, such as shapes, colors, forces, and the amount you spent on groceries, privileged information relies on metaphor. And metaphor can make the difference between smart science and brute force science.
To see privileged information at work, we need look no further than the human (or robot) body. The body is special because it has particular ways of interacting with its environment. A room with chairs in it is understood differently by a human with legs than by a robot without them. The thousands of points of raw data describing the room collapse into a few simple ideas when subject to the constraints and demands of a physical body. If a teacher knows what it’s like to have a body, he, she, or it can pass these simple ideas to a student as privileged information, creating an efficient description of a complex environment.
- More Here
The teacher-student code has its roots in the sheer complexity of the real world, a complexity that has long bedeviled AI research. Is that flat surface a table? A chair? The floor? What if it’s partly in shadow, or partly obscured? After years searching for simple ways to answer these questions, the AI community is finding that the complexity of the real world is, in some ways, irreducible.
[--]
“This means that a good decision rule is not a simple one, it cannot be described by a very few parameters,” Vapnik said. In fact, he argues that using many weak predictors will always be more accurate than using a few strong ones.1 One approach to capturing complexity is to feed hundreds of thousands, or even millions, of points to a computer, which is called brute force learning. It works well enough, and is the driving engine behind most Big Data commercial enterprises, in which machines are set loose on terabytes of data in order to understand everything from scientific problems to consumer behavior. In fact, Vapnik developed one of the key technologies used by Big Data, called Support Vector Machines. But brute force methods are also slow, inefficient, and useless when data is not plentiful, such as when studying biopsy images for cancer.
Vapnik describes privileged information as a second kind of language with which to instruct computers. Where the language of brute force learning consists of technical measurements, such as shapes, colors, forces, and the amount you spent on groceries, privileged information relies on metaphor. And metaphor can make the difference between smart science and brute force science.
To see privileged information at work, we need look no further than the human (or robot) body. The body is special because it has particular ways of interacting with its environment. A room with chairs in it is understood differently by a human with legs than by a robot without them. The thousands of points of raw data describing the room collapse into a few simple ideas when subject to the constraints and demands of a physical body. If a teacher knows what it’s like to have a body, he, she, or it can pass these simple ideas to a student as privileged information, creating an efficient description of a complex environment.
- More Here
No comments:
Post a Comment