With the increase in performance and capabilities of today’s computers, researchers and scientists are excited about the possibility of computerizing human intelligence in the form of computer programs so as to make computers possess a certain degree of human-like intelligence. In the middle of last century, this human endeavor has produced a new technical term that was named as: Artificial Intelligence (AI).
However, until today, there is no common consensus on the definition of Artificial Intelligence. For example, it is still not clear about whether Artificial Intelligence literally refers to computerized human-intelligence or machine’s self-intelligence.
As we know, the study of Artificial Intelligence historically focuses on problem-solving (i.e. analysis) and learning. The difficult issue of synthesis (i.e. creativity) has not yet been received much attention. Although artificial intelligence may literally mean man-made intelligence inside machines or robots, the contents discussed under the traditional paradigm of artificial intelligence suggests that it implicitly deals with computerized human-intelligence or computational intelligence (i.e. rationality).
Then, we can raise this question: What do we mean by intelligence from an engineering point of view? Refer to my book on Fundamentals of Robotics published in 2003. One possible definition of intelligence is as follows: “Intelligence is the self-ability which links perception to actions so as to achieve intended outcomes. Intelligence is a measurable attribute, and is inversely proportional to the effort spent in achieving an intended goal.”
In a conference held in Italy in 2004, I have further made a concise definition of machine (or robot) intelligence as follows: “Robot intelligence is an attribute engendered by a robot’s brain, under the governance of causality and rationality. Causality is the study of intelligence without considering motivation (i.e. value and belief), while rationality is the study of intelligence in relation to motivation.”
In view of above definitions, it is clear to us that the achievement made so far in the field of AI is still very limited. Many questions remain un-answered, for example: How to make machines to acquire and learn knowledge by themselves? How to make machines to communicate knowledge or meanings by themselves? How to make machines to understand knowledge and meanings by themselves? How to make machines to synthesize (i.e. create) knowledge or meanings by themselves?
Therefore, it is still a tremendous challenge for us to develop innate algorithms or engineering principles underlying Artificial Self-Intelligence (AsI). And, our goal toward this research direction is to develop practical engineering solutions to the problem of how to make future machines and robots to autonomously learn, understand, synthesize, and communicate meanings.