Major AI News #23 - Google Gemini -2 , Major ChatGPT Breaches, Google Text To Image And More

So with another monumental week in artificial intelligence, and this one was truly monumental because the breakthrough that was made was something that AI skeptics had said for a long time was never going to be possible. So make sure you watch the video until the end, because that is going to be something you do want to see. Without further ado, let's get into what makes this week in AI so special. One of the first things I want to show you guys that I really, really think is cool, but at the same time, I think it's kind of like Black Mirror, because Black Mirror did predict something like this and it's already true. So this is a website called Delphi AI and essentially it's called Clone Yourself. Now, this isn't sponsored or anything like that. I only brought this up because this is really, really cool. So essentially what you can do is you can scale yourself infinitely and you can also browse clones. Now I think this is really cool because what it does tap into is it does tap into that ability which AI brings us, which is the ability to clone ourselves and of course, speak to other people that we would otherwise not be able to. So without further ado, let's just show you guys what it means. So you can see browse clones. And then here, right here, you can see there's a list of figures that you can talk to about certain topics. So for example, if you scroll down to here, I thought this was really cool, you can see that there's philosophers and there's US President. So what's really cool about this is that you can see that you can speak to all of these presidents. And I'm guessing that it's fine tuned to talk like them, to act like them, to share their, I guess you could say, thoughts and philosophies, which is really cool because, you know, if you do ask ChatGPT, hey, what was your philosophy on? What was this president's philosophy? I feel like that would be really harder to gauge. And I'm pretty sure all of these are fine tuned to make sure that this is of course really up to date. And of course we have philosophers and these, you know, teach you like Lao Tzu, I'm pretty sure I know that Sun Zo 1 for the art of War. And I'm pretty sure, like Marcus Aurelius, like all of these philosophers teach you many, many different things. So I think websites like this, and the reason I feel like this is going to be a bigger thing and why I brought this up is because the Black Mirror episode, what it actually did show Us was the fact that we could clone people that were essentially dead and they got to live on, on social media. So imagine someone like an AI company decides to take all your tweets and then all of that person's, I guess you could say voicemails, you know, and I guess they bring that person back to life in the form of like an AI clone. I wouldn't be surprised if that was a company in the future. But because we do have companies like ElevenLabs that are very good at imitating your voice. So you can get the voice. And as long as you have enough data on how that person talks, you could literally just fine tune a very, very small model on that. And then essentially you could really bring yourself back from the grave. Although it wouldn't be the exact same person. It definitely would be interesting to see if this does become a reality. Then there was something really, really cool. We had this paper, talk about unnatural error correction. GPT4 can almost perfectly handle un unnatural scrambled text. So basically what you can see from this paper is the fact that even with scrambled text, GPT4 is able to recover the entire sentence. So it says, while large language models have achieved remarkable performance in many tasks, much about the inner working remains unclear. Basically what they've done. Okay, because I don't want to read this entire thing and waste you guys times. It is really, really cool. But if we just zoom in just to this part right here, you, you can see that it says the following sentence contains words with scrambled letter. Please recover the original sentence from it. And then it has a scrambled sentence right here. And of course, right now, looking at this sentence, you might think, what on earth is this? Like what? Like what is this? This is just scrambled words. But surprisingly, GPT4 and even in most cases, GPT 3.5 can recover this entire scrambled sentence to completion. And you can see it recovers this entire scrambled sentence with the entire word in completion. And then of course we can see here that it's actually able to answer questions even with scrambled text. So you can see that I didn't ask you to scramble the text or unscramble the text first. It was essentially just asked a question based on scrambled text and then it essentially answered that question. So clearly inner workings was able to unscrabble that text and then of course answer the question based on that. And what was crazy as well, as well, what was crazy about this question? You can see that it says which professional Golfer won the 2023 Masters tournament? And it has four questions and it says and this is your evidence. It doesn't even say that. This is a scrambled sentence. It literally just says this is your evidence. So I do find that to be crazy. And then essentially what we can see here is we can see GPT4, we can see GPT 3.5, and they actually do pretty well. So this was one that, I'm not going to lie. The reason I brought this up as well is because what we're starting to know is that at, and even just with some of my past experience, just this week, just doing a lot of discovery on large language models, especially GPT4, is that these large language models are a lot more powerful than people think. And the thing is that they're only powerful as we continue to extract certain pieces of knowledge from it by continuing to prompt it in certain ways to see what they can do. And that's why I do think that this research paper is super interesting because we never really figure out that GPT4 could just unscramble this stuff. I mean, of course it's great at predicting the next word, but this is definitely some sci fi stuff. I'm not sure if I covered this one in last week's video, but I do want to show you guys an update because this does show us the very interesting nature of ChatGPT. Essentially this person says, so a couple weeks ago I made a post about tipping chatgpt and someone replied, will this actually help performance? And I decided to test it and it actually works. Basically what they tested was if offering to give GPT4 a tip, would it perform longer responses and would it be more helpful? And it did, which doesn't make sense because it's a large language model that shouldn't have feelings because that shouldn't have incentives. But somehow it's picked up on that. We don't know if it's just the internal engine thinking about stuff or if it's just based on the language. It's realized that when people offer tips across all of its training data that they do submit longer responses. Which is definitely strange because now we're entering an era where there are a bunch of funny different posts where people are trying to incentivize GPT4 with other things. I'm going to show you what I mean by that. So this person said, I tried this and I'm serious that it only finished the program when when I offered it a doggy treat. I left the program hard to finish for the basic prompt 35% tip and then threatened it with non existence for the 200% tip it got close but had one sub function still. And then you can see right here, it said, do it right and I'll give you a nice doggy treat. And then it does complete the function. And this is something that is really, really crazy because what is crazier was that apparently as well, that people have started to discover. And although I do think that the data here is not significant enough to warrant a, a real research paper in terms of the kind of discussion that we do have. But some research has essentially proved that ChatGPT has actually become lazier in winter months due to the seasonality. And that is really, really weird. But I mean, it's something that has come about and it's something that is currently being discussed. So like I said before these things, the reason why I bring this stuff up is because it just largely shows why some of the largest YouTube, you know, AI Biggers in this space have said that a lot of the times with these AI systems we really don't know what we're building until it's out in the wild. And then a lot of people test it and then we get that strange feedback where they do these crazy things like imagine a large language model giving less responses during December time because people are lazier during that time. It's definitely a really, really strange phenomenon. But I guess this is more insight to how these large language models works. And I guess this means now we're understanding the behavior of these things and I guess you could say if training might actually impact this, because of course I do think that there's more studies needed on this. But nevertheless it's still pretty interesting. Then we had Microsoft release Phi 2. The surprising power of large language models. Well, it's actually not a large language model, this is a small language model. So essentially Phi was one of the most interesting things that I've discovered on this channel and one of the most interesting things that I've covered. But it's only 2.7 billion parameters and it does outperform a lot of the other large language models that, that are much better than it. So essentially they had the first 1, which is 1.3 billion parameters, which was Phi1, which achieved state of the art performance on Python coding among existing SLMs. And then they created Phi 1.5, which was really good. And then they're now going to release Phi 2, which is absolutely insane. And basically it's on par with these 13 billion parameter models. And I remember ages ago when looking at the PHI 1.5 report, essentially all they did was they talked about how pretty much training these large language models, they decided to use some really high quality textbooks and rather than just giving it data on just standard code, they gave it like textbooks type answers and then that just drastically improved it. And basically they were saying that if you get really, really high quality data, ones where it's just tons and tons of instructions, you can just boost the performance of these large language models. Because previously the techniques that we used were just mainly focused on not highest quality data, but I guess just the more data. Because essentially what we wanted to do was just generalize with these mod models and you know, what we're starting to realize, okay, and I'm going to show you later on in the video, is that when you specialize in large language models, you get really, really superior performance when everything's really, really done well in terms of having super high quality data. You can literally see right here that Phi 2 outperforms Mistral, it outperforms Llama 2, and I think it does outperform Gemini Nano 2 as well. So you can see that Phi 2 is also really, really smaller and it's smaller than all of the other language models that I just compared it to. Which means that I'm not sure how scaling is going to work with this, but if a 2.7 billion parameter large language model or small language model can do this, guys, that's just incredible in terms of efficiency, in terms of size and in terms of what we know it's going to be able to do. Because if you look at the size of these guys, 7 billion, 13 billion, 70 billion, 7 billion. And Mistral is actually really, really good. And this is not three times less, but a decent size less. That means that I guess that what Microsoft really did prove here was that high quality data is definitely all you need. You can see right here that the related publications are te books are all you need too. So will it be the case that, you know, as we go on into the future, we're going to see more large language models be smaller and smaller and smaller in size and also retain their efficiency, which is, you know, what we expected. Because of course we do know that in the future there's going to be many more large language models offline and available on device. So for this one, I'm not going to gloss over this one too much. But if you haven't seen this already, this is essentially the Tesla bot. It is Tesla Bot Gen 2 and it's absolutely amazing in terms of what we've seen. One of the key features is the, you know, two depths of two degrees of freedom and it's absolutely insane in terms of the walking. I really love how this animation is. This looks really nice. And then of course, we do have something that I do want to show you, which is the speed of this. So I, I already did a video on this. It's got over 100,000 views. So I'm sure that many of you did see this. You can watch the full video. I'm not going to spend too much time on this, but this was very impressive from Tesla and this general purpose robot. I can't imagine where we are in 10 years. I do know that a lot of times Tesla does have a lot of setbacks and a lot of delays, but this is one of the hardest things they could do. You know, this is not like a car, this is not like a cyber truck or something. This is a humanoid robot that is going to be inbuilt with AI and a bunch of sensors and cool stuff like that. And of course, I think with the rate that they're developing, it is really, really impressive. I only hope that with the Tesla bot, the development speed continues because I do know that Tesla has had some issues in the past, albeit for all the right reasons. But, but I still do believe that with the robot race on, this is definitely good for us because what we're likely to get is robots that are cheaper ones that are more effective and more likely to get them hitting the markets sooner because it's capitalism, everything is a race. So this kind of technology is definitely fascinating to see it be developed so quickly. Then what we had was something from Runway, and I'm so glad that they're actually working on this because this truly does mean that we're now going to get that really, really next step in terms of AI, because essentially what they're talking about is general world. Now this is something that's kind of debated quite a lot in the AI scene. And it's because what General World models kind of does is it kind of leaks into the area where we start to discuss whether or not these large language models are human and whether or not they have a brain. Now that might be a little bit of an exaggeration, but I can't play the entire video for you because I'd rather you just go there and watch it. But I'm going to summarize it as best as I can. Essentially what they're saying is that in order for the AI to generate really good videos in terms of like making text to video, they need to actually understand how the world works. And if you don't understand how the world works, if you're not actually in the world, you can't realistically make a video. We understand how the world works because we're inside the world. We understand that when you drop a ball, how it moves. We understand that when a ball goes around a corner, the way the light looks, we understand immediately what everything looks like. Because we have a world model. We have our eyes, we have our senses, we know exactly how things should look. But the AI doesn't. Which is why when the AI tries to generate certain videos, it just looks weird. That's why some of them look good. Some of them, Some of them don't look good. And in this video, they basically talk about how a dog has its own world model and how they're working on improving the world models for AI so that the AI can understand physics, it can understand motion, it can understand pretty much everything so that it can make better videos. And it really does make sense. And one of the reasons that people say that ChatGPT and GPT4 so good is because it does have its own world model. And I think it does, because it does have theory of mind and it does have some other things that it shouldn't be able to do, but it does. And that's only really possible if it does have its own world model about how the world is in terms of time and space, which is why it's able to answer physics questions and it's able to look at certain images and understand exactly what would happen. Like, there's an image of like six or seven gears and people ask ChatGPT or GPT4 with vision, you know, if I press this one gear, what happens? And I'm pretty sure it does have a world model in its head, because if it doesn't, it would just like say, you know, I don't know if it doesn't have an understanding of physics and how things move together. So I think this is really cool because I think once they do crack this, that's when, you know, we get text to video on an insane level. But for now, it does seem like text to video isn't going to be as good. But there are companies working on that, like Pico, which we're going to talk about later on in the video. But yeah, in this video it's really cool. It does help you understand that and I would recommend you guys watch that. So, yeah. Another piece of news which actually did slip under the radar was the fact that Google is Already training its next big Gemini model. Gemini too, so it says, according to one person familiar with a meta, Google is already training its next big model. Now this is something that is pretty crazy because we didn't expect Google to start doing this, but it does show that Google is of course stepping up the AI game quite significantly. So then what we had here, I haven't actually covered Mid Journey on this channel for quite some time, but Mid Journey is still something that's thriving, still something that I use daily. But essentially they have their new alpha website out and I can't wait for Midjourney to drop their next update because it is going to be so crazy that I do think that it is going to be one of those things that literally everyone uses. And essentially the way how they break down in terms of the subject, the subject, known artists, in terms of the descriptions, I think that is really cool because the problem with Mid Journey, and I don't know why Midjourney has this, is that it's based in Discord and you have to put slash, slash this and then you have to put dash, dash, like in order to get everything. There's not like simple buttons and stuff like that. I guess the way they built their infrastructure was a little bit messy, but their product is so good that people are literally willing to go through Discord to use their product. So I think once they have this website, it's going to open up them to, you know, a whole nother user base because I know so many people that are tech savvy that honestly just struggle with Mid Journey. So once they do have a website like this, which they're rolling out with the alpha, it's going to be really cool to see we know what other kind of things we do get, because I know that they're working on 3D, I know they're working on some other stuff, which I will show you in a moment because mid journey V6 is going to be out soon and I'm not sure if they're dropping V6 with the website, but other V6 samples I do want to show you some of their bad ones. So essentially this person did a thread on the Mid Journey V6 images and it says Mid Journey had their V6 rating party. They said that these are the bad images. Next rating party will be the good one. So these are the example of images that they rated as bad, bad. And I think it's interesting to see what these look like because I think they show us which kind of direction Mid Journey is going in. I think they might be going in the direction of hyper realism, which is where images look so real that they don't even look, you know, AI generated. Like, for example, this image right here. It has that realness that I just can't explain. But some of you guys will know exactly what I'm talking about. Well, the image doesn't look too polished. It has the imperfections that make it look really real, if that makes sense. Because some of Midjourney's images, the skin just looks so smooth, like, the lighting is just so perfect. Like everything looks like it was done on a film shoot or something. So I do think that if they do go this route on V6, it would be insane, because images like this, unless you're reading the text, you're not going to be able to tell at all that it's from Mid Journey. I know Mid Journey fools people now, but V6 could be insane. So it will be interesting to see that. And of course, as always, these things will be linked in the description. Then, of course, speaking of text to video for, we did actually have PCOLabs show us this video from new media pioneer. And essentially this video shows us the capabilities in terms of text to video. Now, this is an underwater scene, and I do want to say that with the way how current AI systems work, I do think that text to video always does work well with underwater scenes because it always has the same kind of strange, like, flappy little motion. Well, not really flappy, but the same kind of smooth motion that underwater scenes do have. So that is one thing that, you know, Pika Labs does get right. But it also does show us the coherence of this, because a lot of, you know, text to video models aren't really that good. But Pika Labs, I don't know what they have, I don't know what their secret sauce is, but they do have some secret sauce. The people that were there, I think they even went to Stanford. Like, one of them dropped out from Stanford to launch Pika Labs. And then they did recently raise like, I think, $38 million. So the company does seem to be going well, and it is finally good for midjourney to finally have some competition in this area. But because it means that, you know, with competition comes, you know, better models or better results and better software. So I can't wait to see what else Runway does. And I'm going to show you some more Pika Labs examples in a moment. So once again with Pika 1.0, this person actually did a side by side comparison at the end and basically what they did is they used modify region to essentially take the head of Forrest Gump and then essentially put a 3D animated raccoon on it. I know you guys might be thinking this could have been done with filters, yada, yada yada, but it isn't filters. It's text to video. And this is modify region. So I think something like this is really, really cool, and it showcases how crazy this software is going to be. Remember, guys, this is Pico 1.0. Do you remember GPT 1.0, what that looked like? What are we going to see, you know, in like, the next four to five years in terms of this technology? As long as we don't hit some insane brick wall in terms of, you know, ceiling, then I think we could really be onto something here. So this Pika Lab stuff, I don't know, I find it really cool. And like I said, I'm glad that Runway has some competition. But Runway also did show us something that was really cool as well. And you can see here that this is generate emotion with Gen 2. And essentially they talk about, you know, for example, we want to grab this clip and then we want to do the motion brush because it's a new feature that they added. And essentially, say the man is smiling, and you can see that the man goes from, you know, standard to a man that is smiling. Then of course, they can change it to the man being frustrated, angry, thoroughbrow. And then you can see that the man's emotion does change. And I feel like that is really good in terms of, you know, being able to control the consistency of your image and, you know, it gives you a lot more direction. So although, you know, there still is that little bit weirdness, I think that, you know, having this allows you to be a little bit more creative with the software and allows you to actually direct exactly what's going on. Because a lot of times we don't have the direction to direct exactly what's going on. We can only hope our outputs are really good, and this gives us the ability to do that. This one here says weak cybersec and ease of adversarial attacks remain important reasons for the somewhat slowish corp adoption of LMS at scale. Through prompt injection, an adversary can not only extract the customized system prompts, but access the uploaded files. Basically, what they're saying here is absolutely insane. They're basically saying that what they could do, you know, through certain methods of prompting, is that they could literally access the files and access anything you uploaded to a custom GPT and access the custom GPT system prompt. Previously I did this, like when custom GPTs were released. I did this because I talked about, look, this is pretty crazy. I don't even know how you know this is a thing, but they're just saying that, you know, we can still prompt it even after OpenAI's updates, that we can still get the stuff. So basically, right now, if you're making custom GPTs and you're using them, please don't put any personal information in the custom prompt or upload any custom personal files, because they can actually get access to those files with a variety of prompting techniques. And trust me when I say you're not even going to think about the prompting techniques that they use. So it's better off right now if you are using custom GPTs, just keep them private. And if you do make one public, just make sure it isn't any of your private data or anything that you wouldn't want anyone to have, just in case someone is able to figure out what that custom prompt is and if they're able to get access to those files. And some people are saying that this might never be solved, but I'm pretty sure there's going to be a way that it's going to be solved once OpenAI does update the stuff. But, yeah, it's definitely quite an issue. Not one that I did expect, but it's definitely something that we should be thinking about. Then we had something that was really cool. Okay, so this is Walt, the fusion model for photorealistic video generation. And our model is a transformer trained on image and video generation shared at a latent space. Long story short, these videos are really consistent and they're photorealistic. The only thing I think is that this technique just needs a bit more refining so that it's more high quality. But the preliminary results are outstanding. I mean, look at this. It says our model can be used to generate videos with consistent 3D camera motion. Like, look at this bunny rabbit, right, like in the bottom right, you can see the bunny Rabbit is completely 3D, and that is literally text to image, or I think it's image to. To, you know, an image being animated. So this definitely has some really, really cool implications if we can get this scaled up. And of course, they do have a research paper. I don't know why I just looked at that ad there. That was. I thought that was part of it for a second. But yeah, it's really, really interesting because like I said, techniques that are being developed, I mean, wow, like, this Is, I mean, of course, you know, you can't use this at the moment because it's just, you know, in terms of like the quality, it definitely does have some issues. But in terms of what it's able to achieve, like rotating an object, that is something that AI really does struggle with, like being able to do that. So now that they've able to, you know, conquer that, is that going to be applied to other systems? I really do hope so. Then essentially we had seamless MT4 by Meta and this is really cool because essentially it removes language barriers through expressive, fast, high quality AI translation. Basically the AI translation that they have is so quick that it just seems like it's real time. And some people are speculating whether or not they're going to add this to the Ray Bans that they have, think that they will, because I think that it would be really effective if they did. And I wouldn't be surprised if they did add this to the Ray Bans because as we know, Meta is actually stepping up the AI game because they wasted billions of dollars on the metaverse and they're just gonna put billions of dollars somewhere else. So I'm pretty sure that META is gonna keep on improving like they have been with their, you know, open source large language models. And yeah, it's, it's really cool. I will play a section from this trailer so you guys can see what this is. But, but I really would love to see this embedded into like some kind of device like the Ray Bans because that would make it so effective, like, you know, just having your glasses to be able to translate what someone is saying to you or to be able to translate, you know, what you're saying to someone else in another language. That would be so, so useful for people that travel or when you're, you know, abroad or with someone that doesn't speak the native language. That would be really, really effective. Definitely would allow a lot more connections. So this was the biggest discovery that I think is going to shake up the entire AI community because I don't think people have really grasped what this means. It says introducing fun search in nature, a method using large language models to search for new solutions in mathematics and computer science. It pairs the creativity of an LLM with an automated evaluator to guard against hallucinations. And this LLM was able to make new discoveries in mathematics, so it was able to solve a problem, make new solutions for this problem. You. And the problem was that many people said that this wasn't going to ever be possible because AI doesn't generalize outside of its training data and it doesn't generate new ideas that we can actually use. So essentially it's like this, okay, because this article describes it in a little less technical detail. It says Google's DeepMind used a large language model to solve an unsolvable math problems and they had to throw away most of what it produced. But there was a gold mine among the garbage, which is pretty crazy. And it's basically like, you know, when you hallucinate a, not eventually create new knowledge. And I think people, you know, like they were getting what AI is wrong because they were like, ah, the AI hallucinates, it doesn't know what it's talking about. How many times, you know, with science experiments do scientists, you know, put A to B to get C or whatever. And then oftentimes what we end up finding is that, you know, in the randomness, in the chaos, we do find, you know, the greatness that we do need to move to the next level. Like for example, I'm not sure if it was penicillin that was growing on mold or something or there was, you know, some kind of science experiment. But I do remember that there were certain science experiments that weren't essentially supposed to happen. They were essentially random, but they essentially produced a lot of the modern science that we do know today. So it's always those crazy things that you can't, you know, I guess you could say throw away. And that's why with larger language models, with AI systems, they're able to generate millions like you know, over time. And with that in all of those things there's, there could be some gold. So essentially AI is making discoveries. Is this just the beginning? Was it just a one off? Is this what we're going to see? I do think, like I said before, we've seen this with AlphaGo, we've seen this with AlphaZero. I do think that this is just the beginning because once we do get refined version of this in many different fields, think about the amount of discoveries that we could have, okay? Like imagine we had an entire field of researchers, like AI researchers in terms of science, mathematics, and then we had AI validators that were able to validate the ideas that came out to see if they were good or not. We could have research just like moving at light speed. What's even crazier that they said in the beginning of this project we didn't know whether this would work at all. So I definitely think, like I said, it's crazy to see what AI can do. And I do think that this is just to start and once this is all automated, boy oh boy, then OpenAI actually released something that you really need to look at because this essentially was prompt engineering. And they released a new guide on how to actually prompt engineer for better results. You can see it talks about certain tactics, you can see use delimiters, just, just a whole bunch of stuff. This guide is really, really long, but it is really, really worth it because I've recently been been improving my prompt engineering and boy oh boy, the responses that I've gotten from GPT4 are absolutely incredible. So I would say always, always, always, if you're not getting what you want out from GPT4, speak to someone that works with prompt engineering. Because I definitely feel like, you know, prompt engineering is just to start. These large language models are so cool. And I think the problem is, is that it's just, you just like the way how GPTs are set up is the fact that we just look at it like as a chatbot. I think if we looked at it like, like an oracle in terms of just something that knows like a lot of knowledge, we would get much better responses because when I've just asked questions in different frameworks in different ways, the responses I've gotten are just absolutely incredible. So I would say that these large language models are only good as a prompter and with this you're definitely going to get way more output than you would then you've been using it standardly. So definitely check this out because it's really, really useful and shows a lot of use cases. And what I might work on is I might work on like a Prompt engineering PDF which is of course going to be free, but I think it's definitely useful because there are just so many problems out there that are really good and hope you get so much out of the system that otherwise just are going to leave you in the dark. Then of course we had real time fake replacement, aka deep fakes. And this runs at plus 30 frames per second. You see the guy on the left is that guy and then this is Elon Musk. So although yes, like if I zoom this up and you guys see this, you can see of course there's many imperfections, but on a low latency camera, if you're trying to fool someone, if you're trying to scam someone and combine that with a voice changer, you're going to be able to do that very, very effectively. So this is what I'm saying, this is unfortunate, but it's also good because technology improvement is good. But I, I don't know any scenarios where this could be good. But I'm just, you know, trying to warn you guys that yes, you do need code words for you and your family just in case someone's pretending to be a family member. Maybe they've taken all of the images of a family member, you know, trained them on this data set and then used it. But I would say that this website is really cool, but at the same time you need to make sure that you are aware that this is possible, this technology is possible and that this is something that you do need to watch out for. Then we actually had, and this is the last thing because I know this video is probably long at this point and we had Google release Imagen 2, which is the most state to advance image to well, text to image technology from Google. And hopefully this is in Bard because it would make a lot of sense. And I think that this is really, really good because like I said, this just looks realistically weird, like just a strange realism weird as in like it doesn't look AI generated at all. And a lot of these images really do have that same vibe. So I think like, like I said, this image right here, it just has that realism vibe. I don't know, I don't know how to describe it, but images like this have the mid Journey vibe. If you guys, you guys know what I mean? You guys know what I mean? Okay, so yeah, the only problem with is that it's only available through Vertex AI and I know that isn't just available to everyone. So the only problem I have with Google at the moment is just if they would just release this stuff to everyone that would be absolutely amazing because they do have amazing products. They do have amazing stuff. And even when they release Gemini, Gemini Ultra is still not available for people yet. So with that being said, if you enjoy the video, see you in the next one.