AGI

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 listed in the AI Overview lecture.

1. What is AI?

Roughly speaking, Artificial Intelligence (AI) aims at building of computer systems that are similar to the human mind. However, different approaches have focused on different aspects of the human mind:

[Structure]
Rationale: Intelligence is produced by the human brain. Therefore, to build an intelligent computer means to simulate the brain structure as faithfully as possible.
Challenge: There may be biological details that are neither possible nor necessary to be reproduced in AI systems.
[Behavior]
Rationale: Intelligence is displayed in how the human beings behave. Therefore, the goal should be to make a computer behave exactly like a human.
Challenge: There may be psychological details that are neither possible nor necessary to be reproduced in AI systems.
[Capability]
Rationale: Intelligence is evaluated by problem-solving capability. Therefore, an intelligent system should be able to solve certain practical problems that were previously solvable by humans only.
Challenge: There are no defining problems of intelligence, and the special-purpose solutions lack generality and flexibility.
[Function]
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.
Challenge: The AI techniques developed so far are highly fragmented and rigid, and it is hard for them to work together.
[Principle]
Rationale: Intelligence is a form of rationality or optimality. Therefore, an intelligent system should always "do the right thing" according to certain general principles.
Challenge: There are too many things in intelligence and cognition to be explained and reproduced by a simple theory.

From top to bottom, they correspond to descriptions of human intelligence at increasingly general levels, and reproducing those descriptions 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.

2. Can AI be built?

The question "Can AI be achieved?" has no simple "yes/no" answer.

According to different definitions of "intelligence", AI can be "already achieved", "seems impossible", or "still hard to say".
Intelligence is widely taken as a matter of degree, and every objective of AI can be approached gradually.
The conclusion on what AI "can do" should distinguish the specific problem instances and the whole problem type, as well as between "in principle" and "in practice".
The conclusion on what AI "cannot do" should distinguish "an AI model", "an AI technique", and "the AI objective".

3. How to build AI?

The following is a brief comparison of the two major competing schools of AI (generated by an LLM):

Aspect Symbolic (Logical) AI Connectionist (Statistical) AI

Knowledge Representation Uses explicit symbols, facts, rules, and logical relationships. Uses distributed numerical representations encoded in weights and activations.

Learning Relies primarily on human-crafted knowledge and rules. Learns automatically from data through training.

Reasoning Excels at logical reasoning, deduction, and planning. Excels at pattern recognition but provides limited explicit reasoning.

Interpretability Highly transparent and explainable. Often difficult to interpret and commonly viewed as a "black box."

Data Requirements Can operate effectively with relatively small amounts of data. Typically requires large datasets for effective training.

Generalization Limited outside the scope of encoded rules and knowledge. Often generalizes well from training examples to unseen data.

Robustness to Noise Can be sensitive to incomplete, uncertain, or noisy inputs. Generally more tolerant of noisy or imperfect data.

Knowledge Acquisition Requires extensive knowledge engineering by experts. Acquires knowledge automatically during training.

Computational Cost Inference is often computationally efficient. Training can be computationally expensive and resource-intensive.

Typical Applications Expert systems, planning systems, theorem proving, and knowledge-based systems. Computer vision, speech recognition, natural language processing, and recommendation systems.

Aspect	Symbolic (Logical) AI	Connectionist (Statistical) AI
Knowledge Representation	Uses explicit symbols, facts, rules, and logical relationships.	Uses distributed numerical representations encoded in weights and activations.
Learning	Relies primarily on human-crafted knowledge and rules.	Learns automatically from data through training.
Reasoning	Excels at logical reasoning, deduction, and planning.	Excels at pattern recognition but provides limited explicit reasoning.
Interpretability	Highly transparent and explainable.	Often difficult to interpret and commonly viewed as a "black box."
Data Requirements	Can operate effectively with relatively small amounts of data.	Typically requires large datasets for effective training.
Generalization	Limited outside the scope of encoded rules and knowledge.	Often generalizes well from training examples to unseen data.
Robustness to Noise	Can be sensitive to incomplete, uncertain, or noisy inputs.	Generally more tolerant of noisy or imperfect data.
Knowledge Acquisition	Requires extensive knowledge engineering by experts.	Acquires knowledge automatically during training.
Computational Cost	Inference is often computationally efficient.	Training can be computationally expensive and resource-intensive.
Typical Applications	Expert systems, planning systems, theorem proving, and knowledge-based systems.	Computer vision, speech recognition, natural language processing, and recommendation systems.

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.

The Social Impact of Artificial Intelligence has been under active discussions.

AI researchers are aware of their responsibility on these topics, though most of them think that, according to the currently available evidence, progress in AI research will benefit the human species rather than to destroy it.

Of course, many crucial problems remain open, but to find their solutions, the research into AI should be sped up, not slowed down. Most widespreading concerns and fears about AI are based on misconceptions about the nature of AI.