06-02-2012, 03:56 PM
Artificial Intelligence full report
[attachment=17206]
. Introduction
Artificial intelligence started as a field whose goal was to replicate human level intelligence in a machine. Early hopes diminished as the magnitude and difficulty of that goal was appreciated. Slow progress was made over the next 25 years in demonstrating isolated aspects of intelligence. Recent work has tended to concentrate on commercial aspects of "intelligent assistants" for human workers. No one talks about replicating the full gamut of human intelligence any more. Instead we see a retreat
into specialized sub problems, such as ways to represent knowledge, natural language understanding, vision or even more specialized areas such as truth maintenance systems or plan verification. All the work in these subareas is benchmarked against the sorts of tasks humans do within those areas.
Amongst the dreamers still in the field of AI (those not dreaming about dollars, that is), there is a feeling. That one day all these pieces will all fall into place and we will see "truly" intelligent systems emerge. However, I, and others, believe that human level intelligence is too complex and little understood to be correctly decomposed into the right sub pieces at the moment and that even if we knew the sub pieces we still wouldn't know the right interfaces between them. Furthermore, we will never understand how to decompose human level intelligence until we've had a lot of practice with simpler level intelligences.
In this paper I therefore argue for a different approach to creating artificial intelligence:
• We must incrementally build up the capabilities of intelligent systems, having complete systems at each step of the way and thus automatically ensure that the pieces and their interfaces are valid.
• At each step we should build complete intelligent systems that we let loose in the real world with real sensing and real action. Anything less provides a candidate with which we can delude ourselves.
We have been following this approach and have built a series of autonomous mobile robots. We have reached an unexpected conclusion © and have a rather radical hypothesis (H).
© When we examine very simple level intelligence we find that explicit representations and models of the world simply get in the way. It turns out to be better to use the world as its own model.
(H) Representation is the wrong unit of abstraction in building the bulkiest parts of intelligent systems.
Representation has been the central issue in artificial intelligence work over the last 15 years only because it has provided an interface between otherwise isolated modules and conference papers.
2. The evolution of intelligence
We already have an existence proof of, the possibility of intelligent entities: human beings.
Additionally, many animals are intelligent to some degree. (This is a subject of intense debate, much of which really centers around a definition of intelligence.) They have evolved over the 4.6 billion year history of the earth.
3. Abstraction as a dangerous weapon
Artificial intelligence researchers are fond of pointing out that AI is often denied its rightful successes. The popular story goes that when nobody has any good idea of how to solve a particular sort of problem (e.g. playing chess) it is known as an AI problem. When
an algorithm developed by AI researchers successfully tackles such a problem, however, AI detractors claim that since the problem was solvable by an algorithm, it wasn't really an AI problem after all. Thus AI never has any successes. But have you ever heard of an AI failure?
3.1. A continuing story
Meanwhile our friends in the 1890s are busy at work on their AF machine. They have come to agree that the project is too big to be worked on as a single entity and that they will need to become specialists in different areas. After all, they had asked questions of fellow passengers on their flight and discovered that the Boeing Co. employed over 6000 people to build such an airplane.
[attachment=17206]
. Introduction
Artificial intelligence started as a field whose goal was to replicate human level intelligence in a machine. Early hopes diminished as the magnitude and difficulty of that goal was appreciated. Slow progress was made over the next 25 years in demonstrating isolated aspects of intelligence. Recent work has tended to concentrate on commercial aspects of "intelligent assistants" for human workers. No one talks about replicating the full gamut of human intelligence any more. Instead we see a retreat
into specialized sub problems, such as ways to represent knowledge, natural language understanding, vision or even more specialized areas such as truth maintenance systems or plan verification. All the work in these subareas is benchmarked against the sorts of tasks humans do within those areas.
Amongst the dreamers still in the field of AI (those not dreaming about dollars, that is), there is a feeling. That one day all these pieces will all fall into place and we will see "truly" intelligent systems emerge. However, I, and others, believe that human level intelligence is too complex and little understood to be correctly decomposed into the right sub pieces at the moment and that even if we knew the sub pieces we still wouldn't know the right interfaces between them. Furthermore, we will never understand how to decompose human level intelligence until we've had a lot of practice with simpler level intelligences.
In this paper I therefore argue for a different approach to creating artificial intelligence:
• We must incrementally build up the capabilities of intelligent systems, having complete systems at each step of the way and thus automatically ensure that the pieces and their interfaces are valid.
• At each step we should build complete intelligent systems that we let loose in the real world with real sensing and real action. Anything less provides a candidate with which we can delude ourselves.
We have been following this approach and have built a series of autonomous mobile robots. We have reached an unexpected conclusion © and have a rather radical hypothesis (H).
© When we examine very simple level intelligence we find that explicit representations and models of the world simply get in the way. It turns out to be better to use the world as its own model.
(H) Representation is the wrong unit of abstraction in building the bulkiest parts of intelligent systems.
Representation has been the central issue in artificial intelligence work over the last 15 years only because it has provided an interface between otherwise isolated modules and conference papers.
2. The evolution of intelligence
We already have an existence proof of, the possibility of intelligent entities: human beings.
Additionally, many animals are intelligent to some degree. (This is a subject of intense debate, much of which really centers around a definition of intelligence.) They have evolved over the 4.6 billion year history of the earth.
3. Abstraction as a dangerous weapon
Artificial intelligence researchers are fond of pointing out that AI is often denied its rightful successes. The popular story goes that when nobody has any good idea of how to solve a particular sort of problem (e.g. playing chess) it is known as an AI problem. When
an algorithm developed by AI researchers successfully tackles such a problem, however, AI detractors claim that since the problem was solvable by an algorithm, it wasn't really an AI problem after all. Thus AI never has any successes. But have you ever heard of an AI failure?
3.1. A continuing story
Meanwhile our friends in the 1890s are busy at work on their AF machine. They have come to agree that the project is too big to be worked on as a single entity and that they will need to become specialists in different areas. After all, they had asked questions of fellow passengers on their flight and discovered that the Boeing Co. employed over 6000 people to build such an airplane.
