3203. Introduction to Artificial Intelligence
AI as a Whole
The current research of AI can be summarized by analyzing the various answers to the basic questions.
1. What is AI?
Roughly speaking, Artificial Intelligence (AI) aims at building of computer systems that is similar to the human mind. However, different aspects of the human mind have been focused in different approaches:
From top to bottom, they correspond to descriptions of human intelligence in more and more general level, and to reproduce that description in computer systems. Since different descriptions have different granularity and scope, the above objectives are related, but still very different, and do not subsume each other. The best way to achieve one is usually not a good choice for the others.
Rationale: Intelligence is produced by the human brain. Therefore, to build an intelligent computer means to simulate the brain structure as faithfully as possible.
Background: Neuroscience, biology, etc.
Examples: SyNAPSE, HTM
Challenge: There may be biological details that are neither possible nor necessary to be reproduced in AI systems.
Rationale: Intelligence is displayed in how the human beings behave. Therefore, the goal should be to make a computer to behave exactly like a human.
Background: Psychology, linguistics, etc.
Examples: Turing Test, cognitive modeling
Challenge: There may be psychological details that are neither possible nor necessary to be reproduced in AI systems.
Rationale: Intelligence is evaluated by problem-solving capability. Therefore, an intelligent system should be able to solve certain practical problem that is currently solvable by humans only.
Background: Computer application guided by domain knowledge
Examples: IBM Watson, expert system
Challenge: There is no defining problems of intelligence, and the special-purpose solutions lack generality and flexibility.
Rationale: Intelligence refers to a collection of cognitive functionality, such as perceiving, reasoning, learning, acting, communicating, problem solving, etc. Therefore the goal is to reproduce these functions in computers in a divide-and-conquer manner.
Background: Computer science
AI textbooks, Soar, LIDA
Challenge: The AI techniques developed so far are highly fragmented and rigid, and it is hard for them to work together.
Rationale: Intelligence is a form of rationality or optimality. Therefore, an intelligent system should always "do the right thing" according to certain general principles.
Background: Logic, mathematics, etc.
Examples: AIXI, NARS
Challenge: There are too many things in intelligence and cognition to be explained and reproduced by a simple theory.
2. Can AI be built?
Since the idea of AI or "thinking machine" appeared, there have been various objections against it. Some people claimed that they have proved that AI, or whatever it is called, is simply impossible, due to certain fundamental limitations of computers. Common arguments include:
Many researchers has rejected some of the objections. Classical arguments can be found in the following works:
Obviously, AI researchers believe that AI can be built, that is, computer systems can become more and more similar to the human mind, in various senses.
- There are certain knowledge and functions in the human mind that cannot be reproduced in computer systems.
- Computers can only process symbols and data according to their form, but can never get their meaning.
- Computers can only follow given programs, so can never be truly autonomous and creative.
3. How to build AI?
To achieve the ultimate goal of AI, different overall strategies have been followed in AI research:
Obviously, the selection of development strategy partially depends on the selection of the research objective. Currently the mainstream AI mostly follows the first approach, while the Artificial General Intelligence (AGI) community has been exploring the other two approaches.
[Hybrid – divide and conquer]
Approach: To develop individual functions first using different theories and techniques, then to combine them together.
Argument: (AA)AI: More than the Sum of Its Parts, Ronald Brachman
Difficulty: Compatibility of the theories and techniques
[Integrated – architecture plus modules]
Approach: To design an architecture first, then to design its modules accordingly using various techniques.
Synergy: A Universal Principle for Feasible General Intelligence?, Ben Goertzel
Difficulty: Identification, specification, and coordination of the functions
[Unified – extending a core technique]
Approach: Using a single technique to start from a core system, then to extend and augment it incrementally.
Argument: Toward a Unified Artificial Intelligence, Pei Wang
Difficulty: Versatility and extensibility of the core technique
4. Should AI be built?
Even if we have found out how to achieve AI, it does not necessarily mean we really want to do it. Like all major scientific discoveries and technical breakthroughs, AI has the potential to revolutionize our life and even the fate of the human species, either in a nice way or an evil way — or, as things usually go, a mixture of the two.
Common concern of AI includes:
- AI will dominate the world.
- AI will lead to an uncertain future.
- AI will cause unemployment.
AI researchers are aware of their responsibility on this topics, though most of them think that, according to the currently available evidence, progress in AI research will benefit the human species than to destroy it.
Of course, many crucial problems remain open, but to find their solutions, the research of AI should be speed up, not slowed down. Most wide-spreading concerns and fears about AI are based on misconceptions about the nature of AI.