Recently, Sam Altman said, “The Age of Giant AI Models is Over.” What he meant by that was, “our strategy to improve AI models by making them much bigger is providing diminishing returns.” So, I thought it would be interesting to explore if LLM’s hit the wall and have improvements dramatically slowed. Approximate Transcript: Hi, this video is about large language models MLMs hitting the wall and the implications of that. In case you haven’t heard, I shot a separate video about this. But Sam Altman recently stated that the age of giant models is over, which I think is a bit misleading. Basically, what he was saying was, you can’t improve any more by just adding more data and more parameters. And this makes sense. And this is something that some people have predicted was coming, because GPT, four just capture so much of the data. They didn’t release it. But if you look at like the GPT, two had 1.5 billion parameters, which is sort of like the amount of neurons or the amount of different kinds of factors that it considers GPT. Three had 1.7 170 5 billion. We don’t know how many GPT. Four had has, they didn’t release that. But estimates are that it’s a leap over GPT. Three. And that also, that potentially, they’re kind of out of data. Now more data is being created every day. So it’s really they’re out of data completely, but perhaps just there’s not enough to get like that exponential leap. But also, I think he implied and this makes sense that sometimes more data just isn’t necessarily better, doesn’t necessarily give you an a better answer to get more data. And I elaborate on that again, in my in my recent other video. So you know, let’s assume for the sake of argument that that large language models and opening I included, hit a huge wall, and they are maybe not unable to move forward, but their progress has slowed dramatically. And we don’t see anything like what people think maybe GPT, five should be for five or 10 years, that maybe there’s another technological development that needs to happen. So what comes about because of this, let’s look at the good. I think probably the biggest thing is for the world to kind of catch up mentally on unlike, you know, especially when it comes to misinformation being spread, and identifying that and helping people adjust to that new reality that we’re finding ourselves in right now, this year 2023, that’s probably the only good thing I can think of maybe the pause, the people who were in favor of a pause is just kind of happens naturally. I personally don’t think that the pause is a good idea. And you know, there’s three dots here, because I don’t really see a whole lot of good coming from this, I’m sure that there’s plenty of people that will be celebrating this, if this is the case, I will not be one of them. The bad, here’s here’s what I would say with the bad good tech is slow down, there’s a lot of really good use cases that really dramatically can help people’s lives that is coming about because of the AI models. And now maybe this in some cases, this doesn’t affect that in some cases, it likely will. So you know, just to give an example, there’s a bunch of different stuff with regards to health care, you know, saving lives, curing diseases that that AI is actually has already shown to be quite proficient at and moving forward rapidly. So perhaps that slows down to me, that’s bad. I think there’s also an argument to be made for this could actually be better for bad actors. And the reason for that is that I think that opening I’m moving forward will actually help tamp down the bad AI models, as they have demonstrated to me pretty thoroughly, that they do have good intentions. And that if there was a bad model that that GPT for GPT, five could help kind of tamp down, identify, fight back against that they would work on that and help with that. And so I think that this actually opens the door for bad actors. And it’ll it’ll make sense when I get to this last bullet point. Let’s look at like, kind of, like how good is GPT for right now. And I would say that it’s really freakin good. Like, I was trying to test the other day like, you know, it’s supposed to be bad at math. And it actually did a pretty good job of math and showing its work. And it got it right. Not like a super complicated thing. But more complicated than what you know, other people were saying it was, it was it was wrong. And I need to add the hallucinations here. So but there are still some things that it struggles with math, as we mentioned before recent events, hallucinations, I think that there’s some more if you want put put them in the in the comments below if you have any other ideas, but it still struggles with some things, but not a whole lot. It does a whole lot really, really, really well. So you know, I think right now, it’s actually at a point that is pretty profound, just GPT four as it is now. Now. So Sam Altman did state that there are other ways in which they are looking to improve it, and I believe I believe them. And but maybe it’s just slower. Let’s assume for the case of this argument that it’s slower. It’s just kind of more minor updates that come together more further down the line in terms of years to create a more complex hints of bigger change, which is kind of what they said, they did say that a lot of their improvements, were just a

This statement by Sam Altman is provocative… …there seems to be an implication that giant AI models are no longer useful… …but this is not what Sam means. Approximate Transcript: Hi, this video is about something that sounds really profound that Sam Altman said, recently, the open ai ai CEO, he said that the age of giant AI models is already over. I think this statement is taken out of context is a bit misleading, because to me, and I saw a smaller, kind of a smaller headline that I clicked on that made it seem even more salacious is kind of is he saying that it’s just like, chat tivities done? Like, it’s not good anymore? That’s not what he’s saying. That’s kind of what I would my first reading of it. It’s like, oh, we’re not going to use them anymore. No, he’s, they’re going to use the large language models. What he really means by this is, is that they can’t make they can’t really grow the improvement of them by making them bigger. That’s, that’s the short answer, there’s a little bit more context I want to add as well, which is that this is this has been the philosophy of open AI, from the beginning. And for quite some time, there’s a, you know, there. ndarray, Carpathia, very famous in the AI world, I believe he was the head of AI at Tesla. And then I think he’s actually at open AI. And now I remember, I’ve watched several of his videos, and one of the things that he talked about, was that, number one, the code for these AI models over the last basically, since 2017, when Google released their transformers paper, the code is very short. And it really hasn’t changed a whole lot. It’s like, I think 500 lines, which for code is very, very small. And then he talks about sort of like the, I believe it was him that the the strategy, the way to improve it is just make it bigger, you know, just keep making it bigger, add more parameters. And parameters are sort of like neurons. And to give context that they show it here in this article of GPT. Two had 1.5 billion parameters. This is funny tag line to be generated by artificial intelligence, I wonder if this is like an AI movie, or series about AI? Anyway, 1.5 billion, and then GPT, three 1.7 5 billion parameters, and it made it way, way better. And that was a large reason for the improvement. And then GPT, four, they didn’t announce how many parameters there already it but it’s supposed to be much bigger. And so what he’s saying is by adding more parameters or neurons, it’s not going to improve the model, there’s diminishing returns in that area. And up to some point, this is going to not give you more, I think another way of looking at this is also more data, it doesn’t necessarily add improvements to the quality of, of the of the model, but just in general, from a standpoint of like data analysis, more data isn’t always better, doesn’t always improve things. And, you know, just real quick aside, if you think well, why should I believe you about data, basically, for the last 20 years, data has, I’ve done data from a theoretical and from a practical standpoint, you know, I have a master’s degree in Industrial Engineering, which is closer to actually data science than it is than it is engineering. And it’s worth a lot of lots of statistics and analysis of huge, weird datasets. And then, you know, I worked at a semiconductor factory where there was, there’s a lot of complicated data, you know, spreadsheets with 10s, of 1000s of rows and dozens of columns. And, and I’ve worked there for about six years. And then for the last 11 years, I’ve done SEO, which is another kind of like practical data analysis, this is very different than the semiconductor, but still, more data. So I’ve been studying data, it’s been my jam for a very long time. And it makes sense, sometimes more data doesn’t add a clearer picture to the situation. And so this in they have talked about actually, this shouldn’t come as a surprise, even though the headline is kind of like, whoa, this shouldn’t come as a surprise, because this has been talked about for a while that number one, they’re going to run out of data to crawl. And that’s not entirely accurate. Because more data is being created every day, more and more in that rate of increase, that rate of new data is increasing over time. But it certainly hasn’t been increasing at the rate at which they have increased their models. But additionally, it doesn’t necessarily help again, help kind of clarify the situation. I think I’ve got a reasonable analogy. It’s sort of like imagine you’re trying to draw like a 3d picture. And you put in your first button and you can only do with dots, you put it in with a handful dots. And you can see like that line of, you know, a guy on a motorcycle so you kind of know what it is. And then you put in a bunch more dots and you get a lot more clarity. You can see more here His facial expression, and you can see that he’s got like a bandage on his leg or whatever. And then you put in more dots, and you get a very clear picture. Now, when you add more dots to the, to the picture to the dataset, there’s no additional clarity, or it’s very minor, the clarity that is added to the situation. And I think this, this kind of metaphor works for, for the, how they’re dealing with the data and the parameters of, you know, GPT, four and beyond. Because, you know, it