[oldandtired]

You make a claim here with "Each answer displayed astonishing understanding of what occurs." and the question you fail to ask is: Whose understanding?

The responses are based on the accumulated knowledge of humans and not machines. The systems have not thought through anything and understand nothing. A process of analysing or pattern matching the input question with the data stored retrieves an answer. But that data stored is human knowledge and human effort not machine.

If you look very carefully at the results obtained, it either contains "interesting errors" (for which an intelligent human would pick up) or it is a summation of human knowledge.

The answers still have to be tested and confirmed for rationality and applicability by humans. In other words, this is a tool like all tools created by humans.

I have seen too many examples of what are supposed to be correct answers that contained subtle and not so subtle errors.

Like every system we have ever made, Garbage in gets us Garbage out. We are the ones responsible to checking those answers and making sure that they make sense in the real world.

[IanCal]

> A process of analysing or pattern matching the input question with the data stored retrieves an answer.

They're not just retrieving stored text like pulling the most relevant passage from a database. If they were they'd not be able to deal with things outside the training set. They couldn't write code for a custom library that was created after the cutoff (they can with a description), and they couldn't write about terms made up in the question.

[worksonmine]

I don't see why not. It's not taking a single answer from a database no, it's taking several based on probability and merging them into what it thinks we're looking for. If you learn to multiply with code to perform one task, you can then apply that knowledge for a completely different task. It may look like solving a completely new problem but the LLM doesn't even see the difference.

When you use the term "custom library" that might be you over-complicating the task. It's still just looking up function to do x, function to do y and applying it to the output. Don't get me wrong it's impressive where we're at but there's no need to exaggerate it as magic.

[IanCal]

> . It's still just looking up function to do x, function to do y and applying it to the output.

I mean no, no it isn't.

I'm giving it info on how to construct data models with a custom library, so interacting with that is not using anything previously stored, and then giving it businesses/tasks to model as simple human descriptions.

If you tell me that something which

* Takes a human description of a problem

* Describes back to me the overall structure and components required to solve it with a hierarchy

* Converts that into code, correctly identifying where it makes sense for an address to be contained within a model or distinct and referenced

* Correctly reuses previously created classes that are obviously not in its original dataset

has no understanding or reasoning and it just regurgitating things it's seen before simply mashed together, I don't know what to say.

Frankly

> it's taking several based on probability and merging them into what it thinks we're looking for. I

Sounds pretty much like understanding and reasoning to me.

> but there's no need to exaggerate it as magic.

I'm absolutely not saying there's magic. Humans aren't magic and they can do reasoning. I'm saying it's not just looking up text and regurgitating it.

I think this is supported by things like othello-gpt, which builds an internal world model and outputs based on that.

[worksonmine]

It's difficult for me to assess how original your library is without examples, maybe I could find the exact implementation on github within 30 minutes. But I've yet to see anything that isn't just mashing together stackoverflow and git repositories to save time. I get the same answers with less wordy fluff from a simple search, but I also know where to look.

It's impressive that it knows the difference between "how many are 5 more apples than 10" compared to "how many percent are 5 apples of 10" (I don't know if it does, just assuming). But the first release also tried to reason why the weight of 1 pound of nails depends with the simple prompt "how much do 1 pound of nails weigh". That's most likely a perfect example of it mashing the classic "what weighs more, 1 pound of nails or 1 pound of feathers".

It IS just looking in a database, and mashing it with some fluff. I'm happy to be proven wrong but I need more than your word for it. My experience is that as the topic gets more niche (less data in the training set) the worse the answers I get and it starts making things up based on probability. It doesn't reason in the sense I assume you're expecting.

[IanCal]

Have you had a look at othello gpt? https://thegradient.pub/othello/

It's a nice constrained example of a transformer learning a world model, not just looking up responses.

> It's impressive that it knows the difference between "how many are 5 more apples than 10" compared to "how many percent are 5 apples of 10" (I don't know if it does, just assuming). But the first release also tried to reason why the weight of 1 pound of nails depends with the simple prompt "how much do 1 pound of nails weigh". That's most likely a perfect example of it mashing the classic "what weighs more, 1 pound of nails or 1 pound of feathers".

Is there a formulation here that would get to a point where you'd think it's not just mashing things together? Are there elements of a simple question that would be required?

Here's a slightly trickier one for it "Which weighs more, a pound of feathers or balloons made from one pound of rubber then filled with 100g of helium?"

https://chat.openai.com/share/b841c96f-e46c-4adf-8ec3-8778ff...

[worksonmine]

Very impressive, but is it any more original than classic search engines' old trick of regular expressions to figure out if I mean the currency or weight when I ask "1 pound =" with the contexts USD or kg after "="? Does it understand the input, or are there just enough discussions in the training data to make it look like it is? I'm not convinced it's not the latter.

It uses context to figure out we're trying to convert something to something else. Then it adds all those numbers up. Taking helium into consideration is no doubt interesting, but they've also polished that task since that was the common critique they got so very wrong with the first release (which I mentioned they had fixed). I'm not qualified to assess this part of the answer;

> "If the balloons displace more than 100g of air when filled with helium, then they would effectively weigh less than if they were left empty. If they displace exactly 100g of air, then the balloons would have the same weight as if they were left empty."

I don't know enough to understand how much 100g of helium is and how it behaves. And it doesn't try to explain it to me, it mentions it then takes the easy route assuming it's a trick question. What does that tell you? I guess there are similar discussions around and it gives me the summary. Why doesn't it tell me how much air it displaces under what circumstances? Temperature etc, it should be easy if it's not just a simple discussion on a random forum. A conversion regex could do it.

This comment[1] has a very impressive example. But anything I'm qualified to assess has mostly been meh. If the fix is better training data does that mean it's reasoning or regurgitating? The mistakes it makes are what tells me how it works, not when it tricks me that it's correct. To me it's a very well polished search engine summary.

[1]: https://news.ycombinator.com/item?id=37219351

[corethree]

> You make a claim here with "Each answer displayed astonishing understanding of what occurs." and the question you fail to ask is: Whose understanding?

The answer is obvious. The LLM is understanding the concepts. The last question was unique. The resulting answer was also unique.

It was not a "retrieved" answer. It was a unique answer. A correct composition of several underlying concepts. A correct composition can only be formulated if the machine had correct understanding of each concept and how they relate to one another.

This thing understands you. It wholly owns this understanding. It is not regurgitating knowledge. It is inventing new answers.

Wake up man. I had the LLM invent 6 regions and heat the cup of coffee to plasma levels of heat. The answer and composition of concepts was remarkable.

You're calling it a parlor trick because of subtle errors? Bro. Come on.

[oldandtired]

> The answer is obvious. The LLM is understanding the concepts

Who created the LLM? Whose understanding underpins the LLM?

Certainly not the LLM.

> This thing understands you.

Does it? Or is this a result of the intelligence of the human beings involved in building the LLM?

> I had the LLM invent 6 regions and heat the cup of coffee to plasma levels of heat.

Did the LLM actually invent anything? Or was this result directly based on you and your intelligence with the recorded knowledge of all the human sources involved in the solution?

> You're calling it a parlor trick because of subtle errors?

I haven't called it a parlour trick. All I am saying is that there is no intelligence in these systems. Human intelligence built them, but these systems in and of themselves have no intelligence.

We do of course build many intelligent systems all the time, they are called children.

[corethree]

>Who created the LLM? Whose understanding underpins the LLM?

Who created you? Whose understanding underpins you? Asking these questions about you is as irrelevant as asking it about the LLM.

Just because books, educations your teachers, the internet and your parents and the environment shaped everything you know doesn't preclude your membership into the category of things that are capable of understanding.

>Does it? Or is this a result of the intelligence of the human beings involved in building the LLM?

It does understand you. The intelligence of human beings who built it aren't directly involved as it was trained on external data.

>Did the LLM actually invent anything? Or was this result directly based on you and your intelligence with the recorded knowledge of all the human sources involved in the solution?

Does a human actually invent something or is it directly based on recorded knowledge?

You're asking irrelevant questions. Humans do not create things out of thin air either. Humans also invent things by composing existing knowledge to form concepts. The inventing that LLMs can do is equivalent in totality to our understanding of the word "invent"

>I haven't called it a parlour trick. All I am saying is that there is no intelligence in these systems. Human intelligence built them, but these systems in and of themselves have no intelligence.

Totally false. Not only are you wrong but experts in AI including the father of modern AI disagree with you completely and utterly.

If I copied your brain and replicated exactly that brain is "from human intelligence" but that copy of your brain is still an intelligence independent of it's origins and where it got it's knowledge.

>We do of course build many intelligent systems all the time, they are called children.

It's like you're eating your own logic. We also build intelligent systems called LLMs. Same concept.

[oldandtired]

You miss the point of the questions in relation to the LLM. However, your questions are important in relation to the fundamental difference between humans and what they create.

Let me put it this way: Humans are started with a single cell that eventually grows into an extraordinarily complex entity. If you look at new born babies, they have a capability of learning and exploring that we do not see in any artificial construct that we make.

Our artificial constructs have to be essential fully developed physically before we can then start the process of programming them. Humans have a capability to learn as they develop. We see this occur in all living things.

There is a fundamental insurmountable category difference here between living things and artificial constructs that we make.

Any appearance of understanding is based on the logic that we program into these artificial constructs. They cannot exceed what is programmed into them. Interestingly, living organisms can often exceed that. I think that all programmers should undertake a study of living things to gain a greater appreciation of what we do and just how simplistic are the things we do. That is a particular philosophical point of view that I hold.

I think that your appreciation of what we do and the constructs we create is not in accordance with reality. Not that this is particularly strange as far too many people have a much higher view of our technological prowess, especially when comparing to what has gone before. Starting from my undergraduate engineering days and the ongoing study of engineering and technological history, it has become quite clear that we are often today, quite ignorant of just how technologically advanced previous eras were in all sorts of different areas. There are plenty of research groups that are researching how previous generations were able to do things that we do not know how to do today.

> Does a human actually invent something or is it directly based on recorded knowledge?

Here, we do know that there is at least three ways that invention can arise. Logical progression on recorded knowledge, imagination as to how to solve a problem (thinking outside of the box), observation of the natural world around us.

> You're asking irrelevant questions.

For you to say that the questions I asked were irrelevant shows that you have limited yourself in your pursuit of knowledge and understanding.

> Humans do not create things out of thin air either.

When thinking outside of the box, they do. But I suspect that you may not appreciate this particular point.

> Humans also invent things by composing existing knowledge to form concepts.

As pointed out above, this is one mode of invention.

> The inventing that LLMs can do is equivalent in totality to our understanding of the word "invent"

Here, I disagree with you. But that is very likely to be a philosophical/metaphysical difference between us.

> Totally false. Not only are you wrong but experts in AI including the father of modern AI disagree with you completely and utterly.

Do you understand that you have devolved into a fallacious argument here? This is a seriously flawed fallacious argument on your part. The problem here is that your argument assumes that these [experts] you are referring to are correct, when you have not demonstrated that. Nor have they. There are many experts and others (all highly intelligent and talented people) who for all their intelligence and talent are just wrong. This has been shown to be the case many many times throughout the last century (let alone before that) when our understanding has changed because some little known person has come up with anew idea. One of the best examples here would be someone you would know of - Albert Einstein.

Now when you say

> If I copied your brain and replicated exactly that brain is "from human intelligence" but that copy of your brain is still an intelligence independent of it's origins and where it got it's knowledge.

Here, you have a problem. What is the difference between a living brain and a dead brain? In a single instant, we go from life to death and yet we don't know at this point in time what that difference is. There are lots of experiments being done today which are trying to study if there is a non-physical aspect to intelligence and free-will. Different experimenters in the same team have quite different interpretations of what the data means.

Do be so quick to assume that you know, when the researchers who study this can't agree.

> It's like you're eating your own logic. We also build intelligent systems called LLMs. Same concept.

Not at all. Intrinsically different and there is a vast categorical difference between children and our artificial constructs. From your comment, is it a valid assumption that you do not have children of your own or grandchildren of your own or even pets?

Let me ask a question, what is your background in building systems that augment human capability? If you have been involved in building LLM's, let me know.

[IanCal]

It's generating code for a brand new library based on explanations from me, it can write poems about the current news headlines and it can answer hypotheticals with words I've made up. I agree it cannot be just looking up stored answers.

Gpt Othello is a good discussion about this that's more constrained too.