OpenAIs New AI Research Is Incredible...

So we do have new research from OpenAI called Simplifying, Stabilizing and scaling continuous time consistency models. Now, AI image generation has made great strides, but the current mainstream methods, primarily diffusion models, tend to be slow. OpenAI has developed a new, more efficient and faster method called simplified continuous time consistency Models. This is a key achievement, as scaling continuous time consistency models can generate high quality images in just two steps compared to the dozens or even hundreds of steps required by previous methods, thus speeding up the entire process. Now, if you don't understand how traditional AI image generation works, these traditional AI image generators rely on something called diffusion models. So imagine these models as starting with chaotic noisy image and slowly cleaning it up over time, over many stages, until it becomes a recognizable picture. And this process takes anywhere from 500 to 100 steps to generate an image, making it computationally expensive and too slow for real time applications. Now they've got a new solution, scm. Now, SCM changes the game by taking giant leaps instead of many small steps, requiring just two steps to create an image. And this actually makes the model approximately, wait for it, guys, 50 times faster than traditional methods. For instance, it can generate an Image in around 0.11 seconds on specialized hardware. I don't know about you guys, but generating images in 0.1 seconds, 50 times faster is absolutely incredible. Now, if we want to break down this technological achievement, we can look at the scale. The system is large, containing 1.5 billion parameters, capable of working with high resolution images of up to 512 by 512 pixels. Now, of course, there is a speed versus quality trade off. Typically a faster process results in lower quality. But SCM manages to maintain high quality images despite being much faster. And moreover, it requires less than 10% of the computational power of older models. Now, if you want to know how this actually works, instead of the traditional method, which is where the model starts with random noise and iteratively removes the noise, like unscrambling a jumbled, puzzled one piece at a time. The new method, scm, instead of gradual noise removal, SCM takes a shortcut, jumping directly from noise to the final image. And this is akin to having a blueprint of the complete puzzle and assembling it very quickly. So we've got the real world impact here, guys. The real world impact of this is that the speed of SCM could enable real time image generation, making it possible to edit photos instantly, apply real time video effects, or quickly create images for apps and games. And that's what I'm going to get into in a second. Now, do you remember this research? Because when we take a look at scm, we understand that this thing is absolutely crazy. It can generate images ridiculously fast. But this brings me back to some research earlier this year that was developed by Google, called Genie. Now, if you don't remember, Gen Genie, developed by Google DeepMind, is a generative interactive environment. So imagine being able to create and explore entire virtual worlds just by giving simple prompts like text, images or even sketches. GENIE can take these inputs and generate interactive environments that users can control and explore frame by frame, which is quite similar to how one might play a video game. It's like a GENIE that can make a new world for you, and hence the name. Now, genie, okay, and just bear with me for a second, is a system that learns from over 200,000 hours of video data available online. And the interesting part is that it learns without labels or instructions, kind of like a child watching and understanding how things move and interact without explicitly being told what's happening. Now, the craziest thing about this is that if we do take GENIE and SCM together, the simplified continuous time consistency model described earlier, it's actually super fast at generating images, taking only two steps to generate high quality results. And genie, on the other hand, focuses on generating entire video like interactive environments. What if we were to combine the speed of SEM with genie's ability to create interactive scenes? We could potentially develop real time immersive experiences. For example, we could get instant interactive worlds. Imagine typing in a few words and a rich virtual world appears almost instantly for you to explore or play in. We could have real time video game creation. Developers or even players could design levels or scenarios on the fly as they play, like building their dream game or dream world in seconds. Or we could even have augmented reality. With the speed of SEM, adding virtual objects or creatures into the world could become nearly instantaneous, making AR more engaging and more fluid. And if we manage to combine this research, we could see a leap towards real time virtual environments that users can interact with as easily as snapping a photo, turning static image generation into dynamic playable worlds. But how? We just discuss artificial reality. What's actually augmented reality? Take a look at what this Reddit user called Jessa722 2 posted. He actually shows the way that augmented reality could be in the future. Imagine you have goggles on and this is the kind of thing that you're able to see. I think this kind of editing and this kind of, you know, visual display shows us exactly the kinds of things that should be available to us in the future. I'm not sure if I would switch mine to horror. I don't think I need to be any more scared as I already live in a pretty scary apartment. So this is something that I think is pretty cool. But what if we wanted to use these filters for something else? This user on YouTube, Nikian, has actually shown how you can transform different video games into remarkably realistic footage. You can see right here that GTA San Andreas, a popular version of the GTA series, actually looks like real life graphics when prompted with Runway. Runway recently released some filters in which you can use them to basically change any kind of content into another style of content. So if you want to do this yourself, you can. They do have a free plan, but this is the kind of thing that these, you know, consistency models might be able to enable in the near time future. If we can get models that are able to, you know, produce tons of images at real time, we could get virtual worlds, virtual games, augmented reality, and much, much more. Now I pose this question to you. If you do have this, what will be your largest application for these real time models? Will you be in AR in your own custom environment? Will you be playing your own custom game as your own custom character in its own custom world with your own custom AI filter? Or will you be generating multiple AI images just for whatever might please you? I mean, there's going to be a million different use cases in a million different areas and it's quite likely that some of them we won't be able to predict. Let me know what you guys think about OpenAI's research. I think it's really cool that they're now starting to be a little bit more open. And I think this kind of shows us that OpenAI is probably once again, in some areas, still a lot far ahead.