Generative AI Vs. Predictive AI: What’s The Buzz All About?
Artificial Intelligence (AI) has transformed countless industries in recent years. It has enabled machines to perform complex tasks and make data-driven decisions. Two prominent technologies in the AI domain have risen above everyone: Generative AI and Predictive AI. While they are similar in their approaches as they utilize ML algorithms, they have key differences.?
Let’s shed some light on their unique differences?
What is Generative AI?
Generative AI is a type of deep learning model that can generate various types of content such as videos, images, audio, and other synthetic data based on the data in which they are trained. Generative AI videos of Sora have been creating waves recently due to their impressive text-to-video conversions.?
Using Gen AI tools, content creators can easily create powerful outputs quickly. The release of ChatGPT was a turning point in the evolution of generative AI. Gen AI examples are image, video, audio, & code generation and organizational collaboration. Gen AI tools have flooded the market with businesses now able to choose and integrate into their workflows.
Top Gen AI companies such as OpenAI, Stability AI, and Cohere have been working to take this technology to the forefront of many industries. Let us now understand how Gen AI works.?
How Does Generative AI Work?
Gen AI models are commonly trained on large datasets with a large range of information including video, audio, text, and image. By analyzing these datasets, these models can understand the structure and generate new content based on the prompts by the user.
Generative adversarial network (GAN) is one of the most commonly used generative AI models. It consists of two main components.?
Both these components are trained simultaneously to improve the performance of the output. Now let’s move on to Predictive AI.
What is Predictive AI?
Predictive AI is a technology that focuses on forecasting future outcomes. It analyzes patterns in your existing dataset and makes accurate predictions so that you can make data-driven decisions. It also utilizes ML algorithms to learn from historical data.?
Predictive AI helps businesses to optimize their operations, identify areas of improvement, and make decisions to propel their business forward. It has been widely used in banking, healthcare, and manufacturing industries where accurate forecasting can provide the business a competitive advantage.?
How Does Predictive AI Work?
Predictive AI analyzes historical data and patterns to make accurate predictions about future outcomes. This data is gathered from various sources and vetted by taking care of irrelevant or missing variables.?
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Once the data is ready, ML algorithms such as time series forecasting or regression analysis can be used to train the data. The accuracy of the prediction depends on the quality and quality of the dataset, choice of algorithm, and parameters of predictions.
AI weather forecasting and AI predicting stock market have been the rising use cases for Predictive AI since its inception.?
Key Differences: Generative AI Vs. Predictive AI
Gen AI
Goal: Generate outputs based on prompts (Image, video, audio, text).
Use cases: Content creation
Data: Need a diverse dataset to learn and generate content
Architecture: Reinforcement learning, generative adversarial network (GAN), and variational autoencoders (VAE)
Predictive AI
Goal: Predict outcomes by analyzing historical and current data.
Use cases: Risk assessment and business analytics?
Data: Need historical datasets for accurate forecasting
Architecture: Time series forecasting, statistical algorithms, and regression analysis?
So What’s Next in Artificial Intelligence?
The difference that we discussed now between generative AI and predictive AI is likely to fade away in the coming years as both technologies merge. The algorithms are fairly similar but it's how they are put to work is where the distinction lies. Exciting times lie ahead in the technology that is artificial intelligence.