What is the greatest invention of the universe? Undoubtedly, it is human being. Then, what is the greatest invention of human being so far? Most likely, the answer is human language. Indeed, languages are human-made inventions, which help mankind to encode, record, acquire, transfer and most importantly re-discover both propositional and procedural knowledge.
Interesting enough, if we ask this question of “what is knowledge?” to public, you will get many versions of answers. Ironically, everyone knows what knowledge refers to, but yet is not able to give a commonly acceptable definition. Ten years ago, we have advocated the following definition: Knowledge are the clusters of properties and constraints of all entities. In other words, knowledge refer to properties and constraints possessed by all entities in existence. It is our view that the above definition of knowledge will receive least critics from the research community. So far, we have not yet found any better version of definition of knowledge. If you know one, please share with the research community.
Once we know what knowledge refer to, we can continue to raise the following question: what is the best way of representing knowledge? If you go to ask this question in front of college students or researchers, it is for sure that you will not get the correct answer most of the time.
Surprising enough, all of us innately know, but mentally are not able to speak out, the correct answer which is: human languages. Yes, human languages and the extensions of them are the best ways of representing knowledge discovered by human beings. This fact is largely ignored by the textbooks or research community of Artificial Intelligence because knowledge representation is still considered as “an issue which still looks for definite answer”. Despite such shortcoming in research community, public expectation on Artificial Intelligence is still increasingly high.
On the other hand, from the viewpoint of human languages, we can easily understand the nature of mathematics, which are also called as technical languages. In fact, mathematics are extensions of human languages, and are for the purposes of describing properties and constraints at much deeper levels of abstraction. Therefore, it is not a surprise for people to say that mathematics are simply technical languages invented by human beings.
Also, from the viewpoint of human languages, we should call programming languages the extensions of human languages, instead of calling them machine languages. This is simply because programming languages are for the purposes of describing algorithms, logics, control loops and data processing in the form of programs (which have no difference from texts). So, we must say that programming languages are not for machines to use. Especially, if all of us love human languages, all of us in general, and college students in particular, should equally love mathematics and programming languages.
Knowing the nature of human languages, we can now examine the nature of this massively-used term: learning. We all know that an individual will normally spend about 6 years in primary school, 6 years in secondary/high school, and at least 4 years in university, for the sole purpose of learning under the guidance of teachers. Therefore, learning is a process which involves the presence of learners and teachers. And, the primary activities of learners are to learn knowledge as well as skills, and to discover better ways of learning, while the primary activities of teachers are to teach knowledge as well as skills, and to discover better ways of teaching.
At this point, everyone should be able to understand the true nature of languages as well as learning. However, if you think further, you will find out that the following questions are still waiting for satisfactory answers: Why is a human being able to learn any human language in the world? Why isn’t an animal (e.g. a Monkey, a Cat or a Dog) able to learn a little bit of human language? What will be the principles which will enable machines to learn human languages?, etc.
Therefore, the actual challenge in learning is to make machines of tomorrow to be able to master all human languages and the extensions of them. Without considering the aspect of human languages, deep learning will always remain deeply superficial.