Anyone who has worked with ChatGPT, Gemini, or any other artificial intelligence system of that kind has probably felt that they do understand. An AI communicates with us much like an ordinary human being. They make jokes, and they respond to our sarcasm and minor objections in ways that seem remarkably human.
If you ask one of them to prepare presentation slides for your lecture, it may do a better job than you would yourself. I have even heard that many students are now using AI to complete their school and college assignments.
So then, isn't it obvious that they understand things?
If you ask one of them to prepare presentation slides for your lecture, it may do a better job than you would yourself. I have even heard that many students are now using AI to complete their school and college assignments.
So then, isn't it obvious that they understand things?
Certainly not. The reason lies in the very way today's AI systems have been designed. In reality, they possess no power of understanding whatsoever.
All an AI does is this: based on what you have said, or on what it was previously trained on, it simply performs pattern matching and predicts what the most appropriate answer to your question is likely to be.
That may not be quite as bad as it sounds. After all, many of us do exactly the same thing. Most people function very much like pattern-matching prediction machines. We rarely make the effort to understand things deeply.
So then, what exactly is involved in genuine understanding?
To put it in a highly simplified way, it means connecting a newly encountered word to something we already know. Or, in other words, discovering the meaning of a new word through something that is already familiar to us.
But this connection need not be limited merely to words. It can go much further than that.
For example, the moment someone says the word "cat," our mind associates that word with a soft furry creature having four legs, a long tail, and a tendency to purr. In reality, we connect a word with the entire description of the living being it represents.
Nor is our understanding limited merely to visual experience.
If you have ever traveled to certain Southeast Asian countries, the mere mention of the word "durian" may immediately bring many details to mind: that sharp smell that can almost make one nauseous, and yet the surprisingly pleasant taste that lingers in the mouth.
In other words, understanding is not simply matching one word with another. It involves connecting that word to the perceptions of all our senses, to past experiences, and to knowledge that has already been accumulated. But remember, these connections are not permanent. They can change over time as new information becomes available. And they can later be recalled and used again.
Are AI systems incapable of doing this?
In the AI systems currently available, that is certainly the case. An AI is fundamentally a machine confined to language. Its world consists primarily of words, sentences, and a vast storehouse of knowledge.
If an AI is taught that "a durian is a fruit with a strong smell," it merely associates the word "durian" with a description of that smell. But this is possible only during its training phase. Only its creators can teach it in that way. You and I cannot do so afterward.
Are you surprised?
Perhaps not. You already know very well that no matter how intelligent an AI may appear, it is ultimately just a computer program. Yet, have you ever wondered how an inanimate program can accomplish so much?
Let us look a little deeper into the origins of these AI systems.
Today's AI systems are called "Large Language Models." They operate entirely around human language. The beginnings of these programs were quite simple. Their original purpose was merely to translate from one language into another.
Most of us learned new languages in school by studying grammar, vocabulary, and similar things. But none of us learned our mother tongue in that manner. Yet we can speak it fluently and with relatively few grammatical mistakes. How did that become possible?
It was not through conscious study or deliberate understanding.
Research has shown that a child begins learning its mother tongue while still in the mother's womb. Even before birth, the sounds of people speaking outside can be heard by the fetus.
Although the brain is still incomplete at that stage, it already begins trying to identify the boundaries between words in the language being spoken around it. However, it has no ability to understand what it is hearing.
How does it manage this?
That is the miracle performed by the neurons in its brain!
A neuron is like a tiny biological computer inside our brain. There are billions of such neurons within us. As the fetus grows into a child, some of these neurons are still forming. Others have already formed and are preparing themselves for specific tasks. Still others are in the process of establishing their roles within the brain. These neurons are the real actors behind this remarkable phenomenon.
Some clever researchers observed this process. They attempted to imitate nature itself. This led them to the idea of an "artificial neural network," modeled loosely on the human brain.
Although the concept began in the 1940s, the truly significant breakthrough came during the 1980s in the form of the backpropagation algorithm. An algorithm is simply a computer program.
These programs attempt to imitate the way biological neural networks function. However, the amount of computation hidden within such simulations is so enormous that, in the early days, it was extremely difficult to use them for any practical purpose.
Computers certainly existed then, but they were far too slow to perform the vast calculations required to implement these artificial neural networks effectively.
Later came the era of new parallel-processing computers. These machines could perform thousands of calculations simultaneously. With the support of such powerful hardware, the idea of truly practical AI systems emerged around 2018.
These systems were trained on virtually everything available in the world: all kinds of information freely accessible on the internet, the distilled contents of countless books, and much more.
They were also taught what kinds of information should not be accepted and how to interact appropriately with human beings. This marked the birth of modern AI systems. However, they became widely accessible to the general public only in 2022.
At first glance, these AI systems seem capable of understanding our instructions, following our directions, and even creating astonishing images. There appears to be no limit to what they can do.
But the real question remains unchanged: do they truly understand?
In their present form, certainly not. To genuinely understand, they would need the ability to create new associations between concepts. They would need to update their knowledge continuously. Fundamentally, they would need the ability to remember new information.
Even if we restrict ourselves solely to communication through words, these programs do not possess such memory. Although they may appear to remember small amounts of information, they cannot universally and autonomously update their underlying knowledge base. They are capable only of recognizing patterns and making predictions.
Does that mean we have reached the ultimate limit of AI? Certainly not. In one sense, these machines were intentionally designed this way.
Even while we sleep, our brains continue updating memories and reinforcing what we already understand. Today's AI systems cannot do that. They were not designed to function like human beings, whose memory systems are continuously updating themselves twenty-four hours a day.
So yes, today's AI systems do not possess genuine understanding. But that does not mean they never will. There is no reason to conclude that such a thing will always remain impossible.
Considering the speed at which AI technology is advancing, the day may not be far away when AI understands at least the world of language much as we do, even if not the full range of sensory experiences available to human beings. Since words constitute such a large part of our perceived world, even reaching that stage would represent a tremendous achievement.
All an AI does is this: based on what you have said, or on what it was previously trained on, it simply performs pattern matching and predicts what the most appropriate answer to your question is likely to be.
That may not be quite as bad as it sounds. After all, many of us do exactly the same thing. Most people function very much like pattern-matching prediction machines. We rarely make the effort to understand things deeply.
So then, what exactly is involved in genuine understanding?
To put it in a highly simplified way, it means connecting a newly encountered word to something we already know. Or, in other words, discovering the meaning of a new word through something that is already familiar to us.
But this connection need not be limited merely to words. It can go much further than that.
For example, the moment someone says the word "cat," our mind associates that word with a soft furry creature having four legs, a long tail, and a tendency to purr. In reality, we connect a word with the entire description of the living being it represents.
Nor is our understanding limited merely to visual experience.
If you have ever traveled to certain Southeast Asian countries, the mere mention of the word "durian" may immediately bring many details to mind: that sharp smell that can almost make one nauseous, and yet the surprisingly pleasant taste that lingers in the mouth.
In other words, understanding is not simply matching one word with another. It involves connecting that word to the perceptions of all our senses, to past experiences, and to knowledge that has already been accumulated. But remember, these connections are not permanent. They can change over time as new information becomes available. And they can later be recalled and used again.
Are AI systems incapable of doing this?
In the AI systems currently available, that is certainly the case. An AI is fundamentally a machine confined to language. Its world consists primarily of words, sentences, and a vast storehouse of knowledge.
If an AI is taught that "a durian is a fruit with a strong smell," it merely associates the word "durian" with a description of that smell. But this is possible only during its training phase. Only its creators can teach it in that way. You and I cannot do so afterward.
Are you surprised?
Perhaps not. You already know very well that no matter how intelligent an AI may appear, it is ultimately just a computer program. Yet, have you ever wondered how an inanimate program can accomplish so much?
Let us look a little deeper into the origins of these AI systems.
Today's AI systems are called "Large Language Models." They operate entirely around human language. The beginnings of these programs were quite simple. Their original purpose was merely to translate from one language into another.
Most of us learned new languages in school by studying grammar, vocabulary, and similar things. But none of us learned our mother tongue in that manner. Yet we can speak it fluently and with relatively few grammatical mistakes. How did that become possible?
It was not through conscious study or deliberate understanding.
Research has shown that a child begins learning its mother tongue while still in the mother's womb. Even before birth, the sounds of people speaking outside can be heard by the fetus.
Although the brain is still incomplete at that stage, it already begins trying to identify the boundaries between words in the language being spoken around it. However, it has no ability to understand what it is hearing.
How does it manage this?
That is the miracle performed by the neurons in its brain!
A neuron is like a tiny biological computer inside our brain. There are billions of such neurons within us. As the fetus grows into a child, some of these neurons are still forming. Others have already formed and are preparing themselves for specific tasks. Still others are in the process of establishing their roles within the brain. These neurons are the real actors behind this remarkable phenomenon.
Some clever researchers observed this process. They attempted to imitate nature itself. This led them to the idea of an "artificial neural network," modeled loosely on the human brain.
Although the concept began in the 1940s, the truly significant breakthrough came during the 1980s in the form of the backpropagation algorithm. An algorithm is simply a computer program.
These programs attempt to imitate the way biological neural networks function. However, the amount of computation hidden within such simulations is so enormous that, in the early days, it was extremely difficult to use them for any practical purpose.
Computers certainly existed then, but they were far too slow to perform the vast calculations required to implement these artificial neural networks effectively.
Later came the era of new parallel-processing computers. These machines could perform thousands of calculations simultaneously. With the support of such powerful hardware, the idea of truly practical AI systems emerged around 2018.
These systems were trained on virtually everything available in the world: all kinds of information freely accessible on the internet, the distilled contents of countless books, and much more.
They were also taught what kinds of information should not be accepted and how to interact appropriately with human beings. This marked the birth of modern AI systems. However, they became widely accessible to the general public only in 2022.
At first glance, these AI systems seem capable of understanding our instructions, following our directions, and even creating astonishing images. There appears to be no limit to what they can do.
But the real question remains unchanged: do they truly understand?
In their present form, certainly not. To genuinely understand, they would need the ability to create new associations between concepts. They would need to update their knowledge continuously. Fundamentally, they would need the ability to remember new information.
Even if we restrict ourselves solely to communication through words, these programs do not possess such memory. Although they may appear to remember small amounts of information, they cannot universally and autonomously update their underlying knowledge base. They are capable only of recognizing patterns and making predictions.
Does that mean we have reached the ultimate limit of AI? Certainly not. In one sense, these machines were intentionally designed this way.
Even while we sleep, our brains continue updating memories and reinforcing what we already understand. Today's AI systems cannot do that. They were not designed to function like human beings, whose memory systems are continuously updating themselves twenty-four hours a day.
So yes, today's AI systems do not possess genuine understanding. But that does not mean they never will. There is no reason to conclude that such a thing will always remain impossible.
Considering the speed at which AI technology is advancing, the day may not be far away when AI understands at least the world of language much as we do, even if not the full range of sensory experiences available to human beings. Since words constitute such a large part of our perceived world, even reaching that stage would represent a tremendous achievement.
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