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11 min

Generative AI vs Predictive AI: Know The Difference

Some people fear that artificial intelligence (AI) will soon take over the world and control our lives. Well, no need to worry—it is already doing it. Just think about it for a second: AI algorithms decide what TV show we’re going to watch next on Netflix, whether we will get approved for that bank loan, what products we will buy on Amazon, and so on and so forth. We live in an AI-driven world, and like it or not, artificial intelligence is not going anywhere. AI technologies will continue to evolve, bringing innovation to every industry. And if you want your business to remain relevant in this ever-evolving reality, you must embrace the technology. At some point, you will have to incorporate artificial intelligence into your company. The question is, will it be generative AI or predictive AI? Many businesses face the “generative AI vs predictive AI” dilemma, not knowing which technology will better suit their objectives. So in today’s article, AnyforSoft would like to provide a comparative analysis of the two approaches, explaining their differences and nuances. We’re about to discuss: What generative AI is and how it works; Its benefits and limitations; The applications of generative AI; What predictive AI is and how it works; Its benefits and limitations; The applications of predictive AI; The difference between generative AI and predictive AI (comparative table). Hopefully, this article will help you solve the “predictive AI vs generative AI” issue and decide which approach is best for your business. Without further ado, let’s get started! What is Generative AI and How It Works Generative AI (also called gen AI) is a type of artificial intelligence that generates new data (texts, images, videos, programming code, etc.) based on the datasets it was trained on. It does so in response to user prompts. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregressive models, and other machine learning technologies, it learns patterns in the training data and creates unique data based on those patterns. The key distinctive characteristic of generative AI tools is their ability to generate completely unique content. In other words, they can create something that doesn’t explicitly exist in the training data. One of the most popular examples of generative AI is, hands-down, ChatGPT. The model was trained using text databases from the internet. This included 570GB of data obtained from books, Wikipedia articles, and other textual content. More specifically, a whopping 300 billion words were fed into the system. As a result, ChatGPT can provide comprehensive, unique, and human-like responses to user prompts. Benefits and Limitations of Generative AI Models Now let’s discuss the benefits and limitations of generative AI models. Benefits of Generative AI: Simplifies content creation processes and can serve as a tool for inspiration for content writers, designers, artists, musicians, and other people involved in creative work. Can summarize complex documents and other sets of data. Can work with unstructured data. Automates many processes. For example, it can be used for code generation; you can generate HTML tags for your text, create simple scripts, or even do complex coding. Provides human-like responses and can be trained to act like an expert. For example, you can create an AI-powered chatbot that will act as a support representative for your company, which would allow you to provide instant replies to client inquiries and cut customer service costs. Great at problem-solving. Generative AI can be used to explore potential solutions to complex problems by generating novel ideas and suggestions. Now that we’ve discussed the advantages, let’s talk about the other side of the coin. Even though generative AI models have evolved dramatically over the years, they still have limitations. Generative AI Limitations: Prone to AI hallucination (generating incorrect or misleading results). AI models generate incorrect or misleading responses for numerous reasons, including insufficient training data, biases in that data, and incorrect assumptions made by the model. Expensive to train. As a rule, the more datasets you feed into your model, the more accurate it will be. However, obtaining large sets of data and then organizing and structuring them is challenging, expensive, and requires substantial computational resources. What’s more, you will have to ensure there is no biased information and sensitive data in your datasets, which complicates things even further. Lack of understanding. Generative AI fails to understand generated content. Since it lacks comprehension, it sometimes produces outputs that seem plausible but are actually incorrect. Vulnerability to adversarial attacks. Generative models are susceptible to adversarial attacks, where input data is carefully crafted to mislead the model into producing incorrect or unintended outputs. Applications of Generative AI Despite their current limitations, generative AI models are used in a myriad of industries: Gaming Generative AI systems have the potential to disrupt the gaming industry and are already being used in many projects. For example, the developers of The Matrix Awakens introduced AI-powered NPCs (non-player characters) to their game. Players can communicate with them via microphone and expect human-like responses. Interestingly, these NPCs can express different emotions and even tell you to leave them alone if you are excessively annoying. Even if you’re not a gamer, you should agree that this approach is much more engaging and fun than when you interact with a scripted NPC with boring and repetitive responses. We believe that in the near future, more games will leverage generative AI technologies, leading to immersive gaming experiences. E-Commerce E-commerce platforms use generative AI capabilities to create chatbots and virtual assistants. With their help, they increase user engagement, improve lead generation, collect customer data, and, most importantly, reduce customer service costs. Let’s take Harry Rosen’s AI-powered virtual assistant Hailey as an example. When a user lands on the store, they can start a chat with this bot and ask it pretty much anything. The bot will provide a human-like response, helping the user with their inquiry. By using an AI-powered assistant, Harry Rosen solves three issues: It reduces customer service costs. It improves user experience by providing an almost instant reply. It removes interpersonal communication barriers (talking to a chatbot is much easier for shy individuals than talking to a real customer support manager). Information Technology Generative AI tools like ChatGPT, MidJourney, DALL-E, and others are heavily used in the IT industry, as they simplify and automate many processes. Designers use them to get creative ideas for their work, generate simple layouts, etc. Content creators leverage generative AI to simplify content research, brainstorm ideas for future articles, create HTML tags for their content before publication, and so on. Marketers use such tools to create engaging email campaigns, social media posts, ad copies, and other materials. Developers refer to them for code generation, issue fixing, and other software development purposes. AI is simplifying so many aspects of an IT specialist’s routine that it has very quickly become an integral part of the information technology industry.