It is important to remember that our ultimate goal is survival. If someone builds a system that may not meet the strict definition of AGI but still poses a significant threat to us, then the terminology itself becomes less relevant. In such cases, employing a ‘taboo-your-words’ approach can be beneficial.
Now lets think of intelligence as “pattern recognition”. It is not all that intelligence is, but it is big chunk of it and it is concrete thing we can point to and reason about while many other bits are not even known.[1]
In that case GI is general/meta/deep pattern recognition. Patterns about patterns and patterns that apply to many practical cases, something like that.
Obvious thing to note here: ability to solve problems can be based on a large number of shallow patterns or small number of deep patterns. We are pretty sure that significant part of LLM capabilities is shallow pattern case, but there are hints of at least some deep patterns appearing.
And I think that points to some answers: LLM appear intelligent by sheer amount of shallow patterns. But for system to be dangerous, number of required shallow patterns is so large that it is essentially impossible to achieve. So we can meaningfully say it is not dangerous, it is not AGI… Except, as mentioned earlier there seem to be some deep patterns emerging. And we don’t know how many. As for the pre-home-computer era researchers, I bet they could not imagine amount of shallow patterns that can be put into system.
I hope this provided at least some idea how to approach some of your questions, but of course in reality it is much more complicated, there is no sharp distinction between shallow and deep patterns and there are other aspects of intelligence. For me at least it is surprising that it is possible to get GPT-3.5 with seemingly relatively shallow patterns, so I myself “could not imagine amount of shallow patterns that can be put into system”
I tried Chat GPT on this paragraph, like the result but felt too long:
Intelligence indeed encompasses more than just pattern recognition, but it is a significant component that we can readily identify and discuss. Pattern recognition involves the ability to identify and understand recurring structures, relationships, and regularities within data or information. By recognizing patterns, we can make predictions, draw conclusions, and solve problems.
While there are other aspects of intelligence beyond pattern recognition, such as creativity, critical thinking, and adaptability, they might be more challenging to define precisely. Pattern recognition provides a tangible starting point for reasoning about intelligence.
If we consider problem-solving as a defining characteristic of intelligence, it aligns well with pattern recognition. Problem-solving often requires identifying patterns within the problem space, recognizing similarities to previously encountered problems, and applying appropriate strategies and solutions.
However, it’s important to acknowledge that intelligence is a complex and multifaceted concept, and there are still many unknowns about its nature and mechanisms. Exploring various dimensions of intelligence beyond pattern recognition can contribute to a more comprehensive understanding.
It is important to remember that our ultimate goal is survival. If someone builds a system that may not meet the strict definition of AGI but still poses a significant threat to us, then the terminology itself becomes less relevant. In such cases, employing a ‘taboo-your-words’ approach can be beneficial.
Now lets think of intelligence as “pattern recognition”. It is not all that intelligence is, but it is big chunk of it and it is concrete thing we can point to and reason about while many other bits are not even known.[1]
In that case GI is general/meta/deep pattern recognition. Patterns about patterns and patterns that apply to many practical cases, something like that.
Obvious thing to note here: ability to solve problems can be based on a large number of shallow patterns or small number of deep patterns. We are pretty sure that significant part of LLM capabilities is shallow pattern case, but there are hints of at least some deep patterns appearing.
And I think that points to some answers: LLM appear intelligent by sheer amount of shallow patterns. But for system to be dangerous, number of required shallow patterns is so large that it is essentially impossible to achieve. So we can meaningfully say it is not dangerous, it is not AGI… Except, as mentioned earlier there seem to be some deep patterns emerging. And we don’t know how many. As for the pre-home-computer era researchers, I bet they could not imagine amount of shallow patterns that can be put into system.
I hope this provided at least some idea how to approach some of your questions, but of course in reality it is much more complicated, there is no sharp distinction between shallow and deep patterns and there are other aspects of intelligence. For me at least it is surprising that it is possible to get GPT-3.5 with seemingly relatively shallow patterns, so I myself “could not imagine amount of shallow patterns that can be put into system”
I tried Chat GPT on this paragraph, like the result but felt too long:
Intelligence indeed encompasses more than just pattern recognition, but it is a significant component that we can readily identify and discuss. Pattern recognition involves the ability to identify and understand recurring structures, relationships, and regularities within data or information. By recognizing patterns, we can make predictions, draw conclusions, and solve problems.
While there are other aspects of intelligence beyond pattern recognition, such as creativity, critical thinking, and adaptability, they might be more challenging to define precisely. Pattern recognition provides a tangible starting point for reasoning about intelligence.
If we consider problem-solving as a defining characteristic of intelligence, it aligns well with pattern recognition. Problem-solving often requires identifying patterns within the problem space, recognizing similarities to previously encountered problems, and applying appropriate strategies and solutions.
However, it’s important to acknowledge that intelligence is a complex and multifaceted concept, and there are still many unknowns about its nature and mechanisms. Exploring various dimensions of intelligence beyond pattern recognition can contribute to a more comprehensive understanding.