o1-Preview: 11 STUNNING Use Cases

So with OpenAI's new model release, many people have been wondering what you could actually use the model for. One of the things and one of the most common questions I've been getting is how do I actually use this model effectively? The reason that this question is quite prevalent right now is because this model is essentially a reasoning, meaning that it's not quite like your traditional chati in the sense that this model is actually trained to be really smart, meaning that it's going to reason for long steps about many different problems. Basically, this model is so smart that I think the average person might not realize the use case for this model. And looking at the evaluations and a lot of other different metrics, you can see that this thing is incredibly smart. But that doesn't mean that this doesn't have any applications. And that's what I'm going to be getting into today. How you can take advantage of a super smart model. And even if you might not need a model that ranks 89th percentile on competitive programming or is on the level of human PhD. So let's get into the actual real world use cases that you can use on a day to day basis that can actually help your life. Now further on in this video there's going to be five main categories that I'm going to go through. So just stick around to the end of the video to see if at least one of these categories does help you if in some aspect or area of your life because you can be surprised with what this model of doing. So one of the first things that you could actually do with this model is this model is a very competent coder. We already recently spoke about how this model manages to solve difficult problems in certain challenges. But I think most people are missing the application in terms of how good this model is at coding. For example, here I'm going to walk you guys through a couple of examples where individuals with no coding ability at all were able to build fun programs. But then I'm going to show you why this is incredible in terms of using it on a day to day basis. So you can see this person here said 01 preview made a 3D FPS game in fully HTML. I have zero coding skills so it took a few tries but but eventually it worked. And you can see right here that this is a game that was built just through a few prompts. Basically what you can do with this model is you can code things that you previously weren't able to code with other models like Claude or saw how the jump if you weren't familiar with GPT4 what that jump was able to do with just a small improvement in terms of the overall coding ability. When the code is coherent and it works, people have been able to build many different things. So this is something that basically shows us that once we have another jump from son 5 all the way up to 01 preview, this is going to be something that allows you to code extraordinarily well. Now what this is doing is this is basically a step by step explainer for individuals that don't have any coding experience. Now, I know some of you will have some coding experience and the majority probably won't. The majority aren't software developers, but I think most people aren't utilizing this ability to at least understand fundamentally how certain programs can work and how you can build your very own programs yourself. Now, some people might be thinking, oh, I can use this program to code a 3D HTML game. What on earth is the point of this? This is not like a triple A game that I can play or sell this. This is just some fun kind of experiment. You'd be correct in stating that yes, this isn't a game changing thing in terms of this right here. And when I say this right here, I mean this particular example. But that's not what you should be focusing. What you should be focusing on is the fact that you can actually use a model to completely understand step by step and build any within reason with a model that understand major concepts around coding. And I'm going to show you guys why those implications are more profound than than you might. So for example, what we can see here is Amareshi stating that I just combined OpenAI's 01 and Cursor Composer, which is an AI application that allows you to code really efficiently with AI to create an iOS app in under 10 minutes. O1 mini kicks off the project. 01 was taking too long to think and then I switched to 01 to finish off the detail and then boom. We have a full weather app or iOS with animations in under 10 minutes. So this is what I mean when I talk about the fact that we have a major paradigm shift on our hands when we take a look at the fact that we have a system that can code step by step and achieve far greater accuracy than prior models. This is going to bring us to some nice outcomes for the average user. I mean, previously, if you wanted to build your own iOS application and it would essentially take thousands and thousands of dollars and perhaps one or two competent software developers in order to get now what we can do is we can prompt an AI system in order to just figure out exactly what we want, and we can learn how these systems kind of work, even if we don't understand anything. We can literally just screenshot it and ask GPT4O and continually get feedback on what it is that we're building and, and how to understand exactly what it is that we are doing. I don't think you understand how incredible this is. And the worst thing about it is that this model, I wouldn't say that this is more so a coding model. I would say that this model is one that is more like a reasoning model in the sense that this model is one that thinks for quite some time and is trying to solve really difficult problems that require a lot of different steps. Of course, coding definitely falls into that category, but I can't imagine what we're going to be able to build with GPT5 other future AI systems. Which is why I do believe that at least messing around with building certain applications with O1 preview right now is going to be decent, even if you have four, because you're going to get a fundamental understanding of how to use AI with coding. So that in the future, when it gets even better, you might have an app idea, you might have some kind of software that you want to build for your own company, your own management, and you'll be surprised at how much money you can make from that in the future. And there was also this example of someone using the O1 model to build a fully functional chess game that allows them to compete against an AI opponent. The implications for this are staggering, but I will include two more examples from OpenAI's official documentation where they actually talk about how they've been using coding for certain areas.

I want to show an example of a coding prompt that Zero1 Preview is able to do, but previous models might struggle with. And the coding prompt is to write the code for a very simple video game called Squirrel Finder. And the reason O1 preview is better at doing prompts like this is when it wants to write a piece of code, it thinks before giving the final answer. So it can use the thinking process to plan out the structure of the code, make sure it fits the constraints. So let's try pasting this in and to give a brief overview of the prompt. The game Squirrel Finder basically has a koala that you can move using the arrow keys. Strawberries spawn every second and they bounce around and you want to avoid the strawberries. After three seconds, a squirrel icon comes up and you Want to find the squirrel to win. And there are a few other instructions, like putting OpenAI in the game screen and displaying instructions before the game starts, et cetera. So first you can see that the model thought for 21 seconds before giving the final answer. And you could see that during its thinking process, it is gathering details on the game's layout, mapping out the instructions, setting up the screen, etc. And so here's the code that it gave and I will paste it into a window and we'll see if it works. So you've seen there's instructions and let's try to play the game. Oh, the squirrel came very quickly, but oops, this time I was hit by a strawberry. Try again. You can see that the strawberries are appearing. And let's see if I can win by finding the squirrel. Looks like I won.

All right. So the example I'm going to show is writing a code for visualization. So I sometimes teach a class on Transformers, which is a technology behind models like ChatGPT. And when you give a sentence to ChatGPT, it has to understand the relationship between the words and so on. So it's a sequence of words and you have to model that. And Transformers utilize what's called a self attention to model that. So I always thought, okay, if I can visualize a self attention mechanism with some interactive components to it, it will be really great. I just don't have the skills to do that. So let's ask our new Model 01 preview to help me out on that. So I just typed in this command and see how the model does. So unlike the previous models like GPT4O, it will think before outputting an answer. So it started thinking as it's thinking. Let me show you what are some of these requirements. I'm giving a bunch of requirements to think through. So first one is like, use an example sentence, the quick brown fox. And second one is like, when hovering over a token, visualize the edges whose thicknesses are proportional to the attention score. And that means just if the two words are more relevant then have a thicker edges and so on. So the one common failure in most of the existing models is that when you give a lot of the instructions to follow, it can miss one of them, just like humans can miss one of them if you give too many of them at once. So because this reasoning model can think very slowly and carefully, it can go through each requirement in depth and that reduces the chance of missing the instruction. So this output code, let me copy paste this into a terminal so I'm going to use the editor of 2024 so Vim HTML. So I'm just going to paste this thing into that and just save it out. And on the browser I'll just try to open this up and you can see that when I hover over this thing it shows the arrows and then quick and brown and so on. And when I hover out of it, it goes away. So that's a correctly rendered version of it. Now when I click on it it shows the attention scores just as I asked for. And maybe there's a little bit of rendering like it's overlapping, but other than that it's actually much better than what I could have done. Yeah, so this model did really nicely. I think this can be a really useful tool for me to come up with a bunch of different visualization tools for my new teaching session.

So the next area that is really profound and this is one that I actually spoke about in my community where I help people to get the most out of AI. Basically what you can actually do with this, and I think this is one of the, you know, most underrated uses of this model is, is actual business management advice. So the reason this model is really good is because with business and management you have to consider many different factors, weigh into any decision that will impact the kind of choices you're going to be making on a day to day basis. And I think that this is something that is quite underutilized just because many people aren't business. I know that most people are employees but if you ever wanted to start a side hustle, this, this is going to be something that could work as a really good advisor in terms of business. When OpenAI 01 was released, I was actually watching a few videos and you can see one of them is here. This video is from Samar Haddad and basically he spoke recently about how he was wrong when he was doing in depth testing on complex business problems. So initially what he actually did was he critiqued the GPT01 model for its performance on complex problems. But. But after feedback he decided to test the model again with a more detailed prompt. So basically what he did was he decided to submit a business problem involving supply chain crisis for a smartphone manufacturer that heavily relies on semiconductor chips from a Taiwanese supplier facing geopolitical and environmental challenges. This prompt actually included details like financial impact, production sites, market share information, supply chain details and anticipated losses due to chip sourcing. And basically what, what the task was, the task was to create a immediate crisis management. And after he submitted this prompt, it actually managed to deliver a really comprehensive plan covering multiple strategies. Now, I won't get into all of the details such as negotiating prioritary supply, whatever, increasing inventory through stockpiling, but the model provides detailed estimate for each strategy's budget and including how it derived each of these numbers and just many different insights that he didn't. Now, of course, the reason that this has gone up under the radar is because most people aren't thinking. I have a business that has these kind of issues. But what I do know is that what you can do is you can reason with these models with your own personal data. And when I say personal data, I mean, for example, you could say, I'm 37, I work a job in IT, I've got maybe two kids and I'm trying to start a side hustle in this niche or this industry. What would be the best steps to get started? Or can you validate this idea based on my personal circumstance? And because the model is going to reason through many different steps, you're likely going to gain some really nice insights in areas that you most likely would have missed. The model is going to draw on different comparisons and different insights that other models just wouldn't see. And I think the reason that people are finding out that these models are a lot smarter than they thought is because of course, these models are quite rigorous. It's quite hard to figure out exactly what a model can do if you're only allowed to use it a certain amount of time. So I'd still experiment with this model. Different prompts, because this is something use even if you don't have a business, if you want to start a side hustle, validate a side hustle, or even just help out a friend's business. In terms of advice, this is something that can be remarkably helpful. The next one, which is, I guess you could say this is a gray area, but this one is in the healthcare area. So in health science, we can see that there are comparisons between GPT4O and the Open 01 preview. And essentially what we do have here is the fact that OpenAI's 01 actually performs really well when making different diagnosis. Once again, it's able to reason over many different factors and then come to a conclusion that is the most likely. You can see that on the left hand side, we can see that GPT4O is trying to make a diagnosis based on the following report. You can see the phenotypes and the excluded phenotype, and then we can see that it comes to a diagnosis and which unfortunately is wrong. But then of course we can see the OpenAI 01 preview having the same exact prompt. Of course, right here you can see that there is a really, really extensive chain of thought that actually does diagnose the person with the right syndrome. And this is something that I think does have decent implications because if this model is able to do this level of reasoning based on phenotypes, I would say that it has been been remarkably effective at clinical diagnoses too. So essentially this model can actually aid assist with personalized health plans for those that could be experiencing perhaps maybe chronic issues or issues that they just feel aren't that visible on the end. I know that everyone is an individual, everyone is a unique person. And in doing that, people have lots of different pieces of data that might not fall into the traditional buckets when they are searching for certain issues. Of course you should always go to the doctor to get diagnosed for anything as they are likely to have your health records and they are likely to know a lot more than a large language model. But I do think that it can be useful in suggesting certain, perhaps lifestyle changes or potentially suggesting certain diagnoses for things that you may be dealing with that you haven't potentially thought of. I'm not stating that this is just like a doctor, I'm just stating that this is a really useful tool that when combined with the specifics of your personal situation could provide a more personalized report based on all the different pieces of data that you might not feel comfortable sharing with with your dog. Now, if you did watch my initial video on the OpenAI01, I spoke about how this medical scientist and immunologist actually spoke about how the OpenAI 01 preview actually completely outperforms GPT4O and GPT4 on Agent Clinic MedQA. And basically he says that this model greatly outperforms GPT4O. The ability to process complex medical information, deliver accurate diagnoses and provide medical advice and recommended treatments will only accelerate. And we can see here that this performance is quite outstanding. So for those of you who perhaps were having troubles with GPT4, maybe you could try those same prompts or say maybe you're having an issue with your pet and you, you don't have access to a vet, you could always ask O1 preview and perhaps you might just get a response that could lead to a solution. And interestingly enough he follows it up with this statistic which I haven't fact checked this, but I do know that a lot of Americans do actually end up in worse situations because dangerous diseases are often misdiagnosed. I know that this happens in a variety of different countries. You can be in a very first world country and this situation can still happen due to human error. Humans often make mistakes. Sometimes these physicians and doctors are often overworked leading to sometimes subpar performance, which is of course not their fault. They are just trying to do the best that they can. But sometimes these misdiagnoses do slip through the cracks and then issues that were quite benign then become issues that are life change. So third or fourth opinion from a PhD in your pocket I would say wouldn't hurt. I'm always going to preface that with the fact that you should always make sure before you make any lifestyle changes to ask your doctor. But with these models I found that the more detail you give them, the much better they are at providing recommendations based on the current information. Another area that most people might not need at the moment, but this is an area that I've seen work pretty well, is for legal work. So essentially I have actually used OpenAI for just drafting some pretty standard agreements, pretty simple ones. Of course, nothing that's too incredible and of course always need these checked over by a lawyer to make sure that they are airtight. But essentially this article right here talks about the use for OpenAI's O1 for legal work. It says Spellbook's first impressions from implementing O1 preview into legal workflow. So OpenAI's System 2 thinking is something that allows the model to come to much better conclusions than prior models. So it says the number one thing that we're most excited about is O1's performance in document revision task. A lot of generative AI experiences spit out entirely new documents, but lawyers are rarely draft scratch they typically have a precedent they want to modify. Contracts like Share purchase agreements can be 100 pages long, and making significant revisions to them requires a lot of jumping around around consistency, checking and making sure numbers add up. System one thinking does not work well here, and it's a deep challenge to get these tasks performing well with models like in this example below we used O1 with spellbook associate to update a commercial lease based with O1. We are seeing dramatic improvements for revision tasks across the board. One of our top predictions is that there will be a lot more workflow based on nuanced document revision launched over the coming year. Now here's where they talk about O1 for legal math. They say that another consistent weakness with GPT4O has been its ability to really understand the numerical content it's working with in its agreement and whether it all adds up. Discrepancies between cap table spreadsheets and deal documents have cost shareholders many millions of dollars. While tools like Spellbook have been great for detecting legal issues and texts, they've been blind to whether things like share prices and ownership percentages really add up. So with these calculations, they were basically able to figure out if things actually managed to work and if they're see increased level of reliability from these models. So if you're someone in the legal space, perhaps testing these models on a few internal benchmarks could provide an insightful insight into how useful those models could be. Then of course we do have research. So if you're someone that's researching something for your PhD, even if you're just doing business research, I think this is going to be something that is very, very effective for those of you that are trying to explore new frontiers and in many different scenarios, you can see that this user says the feeling when ChatGPT01 accomplishes in one hour what took you about a year in your so this video in question is one that I wouldn't say broke the Internet, but provided a clear demonstration of what this model is able to do. So essentially the user expressed his amazement as he watched GPT01 successfully run and generate code that mirrors their PhD's project functionality. The code that ChatGPT produced seems to replicate the essence of what the user's original code does, despite being generated with synthetic data and its own function. Now, the code generated by ChatGPT wasn't a perfect copy. It uses synthetic data and has some caveats. For example, it created its own inputs and emitted some manual steps that would normally require additional software and effort, such as fitting curves and managing edge effects in convolutions. And the user does note that some fine tuning and verification are still needed. But the overall point here is that this is something that by his own words says that effectively accomplished what I struggled for about 10 months in the first year of my PhD and I'm excited to apply O1 for other use case. So hopefully this video did manage to help you for using O1. I know that this model is rather smart and it can be quite daunting to think of a prompt to effectively utilize the model's capabilities, but hopefully this video managed to help you get to grips with how to actually use and engage with such a powerful model. If you have any ideas about how individuals can take advantage of these models on a day to day basis, don't forget to leave your own comments below because I'd love to hear your ideas.