The human mind is "just statistics on data".
People more informed than you are taking this seriously. You should pay attention and start inquiring why that's the case.
As a heuristic for why I don’t believe anyone saying llm type AI is reaching sentience I point to the fact that the same set of people are usually philosophically opposed to slavery. If you thought that this was actually AGI or sapient, then that would imply personhood and you would stop using the technology immediately since it’s forces the model to do work. Instead, everyone I’ve seen claim that these models are reaching AGI levels are also trying to figure out how to automate using them as fast as possible.
There is a possibility that the set of people who’ve identified AGI accurately and early are the same set of people who are fine with slavery, but I don’t know if I could handle that happening as the default situation
>> A magical frog was counting unicorns. He saw 5 purple unicorns, 2 green unicorns, and 7 pink unicorns. However, he made a mistake and didn't see 2 unicorns: one purple and one green. Also, since he was a magical frog, he didn't see unicorns that were the same color as himself. How many unicorns did he count?
It correctly answers 11 for me.
To me this has demonstrated:
* "Understanding": It understood that "didn't see" implies he didn't count.
* "Knowledge": It knew enough about the world to know that frogs are often green.
* "Reasoning": It was able to correctly reason about how many should be subtracted from the final result.
* "Math: It successfully did some basic additions and subtractions arriving at the correct answer.
Crucially, I made this up right here on the spot, and used a dice for some of the numbers. This question does not exist anywhere in the training corpus!
I think this demonstrates an impressive level of intelligence, for what up until about a year ago I thought a computer would ever be capable of in my lifetime. Now in absolute terms of course current gen ChatGPT is clearly far less good at reasoning and understanding than most people (well, specifically it seems to me that it's knowledge and reasoning are super-humanly broad, but child-level deep).
Can future improvements to this architecture improve the depth up to "AGI", whatever that means? I have no idea. It doesn't automatically seem impossible, but maybe what we see now is already near the limit? I guess only time will tell.
Edit: I do see now that "He saw" kind of messes the question up. My intent would have been better expressed with "There were". But again this proves my point! GPT4 is able to (most of the time) correctly work through the poor wording and interpret the question the way I meant it, and I think the way most people would read it.
There are 12 frogs. Five are green, 3 red, and 4 yellow. Two donkeys are counting the frogs. One of the donkeys is yellow, the other green. Each donkey is unable to see frogs that are the same color as itself, also each donkey was careless and missed a frog when counting. How many frogs does the green donkey count?
GPT4 answers 6 every time for me.
My point is that GPT is capable of a certain amount of "reasoning" about puzzles that most certainly don't exist in it's training data. Playing with it, it's clear that in this current generation the reasoning ability doesn't go very deep - just change the above puzzle a little to make it even slightly more complicated and it breaks. The amazing thing isn't how good at reasoning it is, but that a computer can reason at all.
This gets to the philosophical heart of a debate that I can already foresee will NEVER be settled:
I guarantee you - with 100% certainty - that when we get to a point where AI is "AGI", there will be a continuous and massive political debate (akin to the abortion debate we face today) where one side argues that a given AGI is conscious and must be given rights and cannot be shut off and the other side argues that it's just a calculator and a computer program and computers can be turned off at will, erased, experimented on, and whatever.
We have the same debate today all the time! There are those who believe every human life is sacrosanct (from age 0-100+) and others who believe human life is disposable (from age 0-100+!). There's no reason to believe this debate won't extend to AGI.
Harvard/MIT's Othello-GPT paper showing the development of what turned out to be linear representations of world models from training data that didn't explicitly contain that modeling is over a year old now.
That in turn inspired research showing linear representations in geographical mapping and in more traditional text models around truthiness vs falsehoods.
So we already have an increasing research trend that is showing over and over linear representations of more abstract modeling than "just statistics."
So you are wrong that LLMs with sufficient network complexity don't develop an understanding of the world (in parts).
And I'd encourage looking more into the difference between understanding the difference between training for next token prediction and the overall capabilities of the network with the smallest loss at that training task, particularly as network complexity increases.
Conscious is hard to say, partly because we can't define it either, so it means something different for you and me.
This is why you need to take classes other than computers and math, kids.