Generative AI vs Predictive AI

Hello everyone and welcome back to my YouTube channel. In today's video, we are going to take a look at the differences between generative AI and predictive AI. My name is Helena, and if you haven't done so already, please like, subscribe, and hit that notification bell. So the structure of today's video is we're going to talk about the definitions of these 2 terminologies, take a look at some use cases, and then we're going to take a look at some examples for each. Let's start off by taking a look at what generative AI is. So as its name suggests, generative AI is generating something new for you, and the thing it's generating could be text, images, video, code, or even audio. So essentially, you feed this algorithm a lot of what you wanted to create, for example, text, and then it's able to generate new text for you. The most popular generative AI tool the market today is ChatGPT. So ChatGPT has learned everything that's available on the web. So it's learned from all of the web pages and blog articles. And because it has learned this information, now it's actually able to generate new unique content. So basically, you can write anything that you want it to write. So let's take a look at a couple other use cases for generative AI as well. We have already just seen text. So ChatGPT, you can use this by just entering in a prompt and then it can create any kind of text you want, whether it's business letters, emails, reports, etc. But generative AI doesn't just stop with ChatGPT. You can also use generative AI to create images. The most popular image creation AI tool is Midjourney. So if you feed it enough images of a certain style, the AI will learn that that is that specific type of composition or style, and then you entering in a prompt telling the AI to create this certain type of image, it can start doing and creating new images going forward in the future. Generative AI can also be used to create videos, music, product design, as well as personalization. So what I want to point out here is 2 things. One is that with generative AI, it's learned from the type of asset that you want to generate, like so whether it's text, images, video, etc. And then you entering in a prompt, so that's the instruction that you give to AI, it's able to then create new and unique content based on what it's learned. Okay, so it's not just a repetition of what it's learned in its database, it's actually creating new and unique content. And then I also want to highlight a little bit about the last use case here, which is personalization. And this is a huge use case pretty much all e-commerce stores. For example, if you log in right now into your Amazon store, you will see very, very different product recommendations than what I see when I log into my Amazon account. So Amazon has essentially learned buying behaviors of all of its customers. So if someone bought item X, it knows how likely this, this particular person would be to buy item Y. And if there's high correlations between those 2 items, it will show those product recommendations in your feed, and it may not necessarily show those recommendations in my feed. So in this way, by utilizing AI, every single shopper is able to have a very personalized experience. Now that you understand what generative AI is, let's take a look at what predictive AI is. So predictive AI, like its name suggests, is going to make a prediction about the future based on historical data. So for example, if you're a project manager, you can feed it all of the past performance data of your project, you know, when tasks started, when it finished, what those tasks are, and then the algorithm is going to make a prediction of how long certain tasks in the future will take. Predictive AI is also used a lot in the financial markets as well as trading. So you can feed this AI algorithm all of the past data about a specific stock, and then it's going to predict the future price of the stock going into the future. So essentially, it's using past data to predict the future. Predictive AI uses large data repositories to recognize patterns across time, and then it's going to draw those inferences and suggest what the outcome and trends may be in the future. Here's an example of a predictive AI dashboard that project managers may, may be able to see. It's going to give you different data graphs and as well as statistics for your project, and it can also help you forecast the availability and productivity of your team members as well based on their past performance. Now let's take a look at some of the benefits of using predictive AI here. So predictive AI can help you predict future trends, so it's able to help you recommend products that you can sell as well as ways that you can improve customer service. It can also help you fine-tune your inventory levels as well, so based on how much people purchased of certain items in the past, it can tell you how many new inventory and which items to order in the future. Also, predictive AI can allow you to have better accuracy. So this AI adds a deeper dimension and greater accuracy to management processes without, you know, having all the human emotion going into that data because AI is predictive. It's just following this algorithm, is doing things over and over again, it can give you the most unbiased view for what decisions need to be taken to achieve the maximum level of success. And it also allows you to get more value out of this data, right? Some computations that would take a data analyst weeks, if not, you know, days and weeks to do, Now AI can do it in a minute or 2. It's allowing you to make sense of this data much quicker and it's allowing you with the right information, having that information in a timely manner to improve your decision-making across many different areas of the business. Now let's take a look at some use cases now for this technology. As mentioned earlier, predictive AI is used in financial services. By using predictive AI, you can enhance financial forecasting. You can pull all of the financial data related to different sectors of the business. And based on what the historical data is, you can then predict what the future business data and forecast may be. Predictive AI is also used for fraud detection. So credit card companies use this all the time. Whenever they start to see fraudulent or abnormal behavior in your spending pattern, it automatically sends you a text and notifies you of these suspicious activities so that you can detect whether or not someone has misused your credit card, right? Predictive AI is also used in healthcare. So based on the patterns of people developing certain diseases that doctors and healthcare professionals have observed in patients, the AI can predict how likely a specific patient is at risk for developing these kind of diseases as well. So it can be used to prevent diseases as well as finding successful treatments. So if this specific treatment worked really well for, let's say, 70% of patients who developed this disease, then based on the symptoms of the current patient, then, you know, the AI can even help the doctors decide, you know, whether a specific treatment plan what makes sense or not. Predictive AI can also be used in marketing. So based on the success of past marketing campaigns that the AI has learned from, the AI can recommend the most appropriate channels and messaging to help you optimize your marketing campaign. So as we have seen, both generative and predictive AI has its time and place. Both of this technology is super powerful. I just have this table up here to compare and contrast the differences between generative AI and predictive AI. In a nutshell, generative AI is generating new ideas, content, as well as it's allowing you to be able to create those text images much quicker. The AI has been taught to learn from patterns and these relationships in data so that it can be creative and generate new content. On the other hand, predictive AI, it's predicting something new. So it's fed a lot of information, and based on all of this historical information, it's able to predict future outcomes, right? So predictive AI is used for forecasting, classification, regression as well. So it doesn't create new content, but it's allowing you to make sense of large datasets that you have available now. So thank you so much for joining in for today's video. I hope you now understand the differences between generative and predictive AI, click the link in the description below to join my free course and learn more about AI and how it can help advance your career. Thank you so much for joining me. I'll see you soon.