Generative AI vs Predictive AI: Everything You Need to Know

Last updated by Naina Batra on Aug 30, 2024 at 05:46 PM | Reading time: 6 minutes

Artificial Intelligence is changing the world around us at a whirlwind pace. Two concepts within the various concepts of AI stand out today – Generative AI and Predictive AI. Despite both being based on machine learning, there’s a difference between Gen AI and Predictive AI.

While Generative AI aims at creating something from scratch, Predictive AI aims to assess future occurrences. The functions of both the AIs is pretty much self explanatory, with one is primarily used to generate something new and another predicts or forecasts.

Both the categories of AI use the available data sets to output results. However, they use the existing data sets or information differently for different cases.

With the help of vast datasets and sophisticated ML learning techniques, Generative AI can generate a diverse range of original content across various domains. This is more or less similar to what it has learned.

Predictive AI, on the other hand, is used for prediction. Unlike the Generative AI, Predictive AI's work is to analyze the present data and the data it has learned and predict the outcome. This type of AI uses historical data and translates it into forecasted trends and events.

Predictive AI is commonly used in applications such as forecasting sales, predicting customer behavior, or diagnosing diseases based on symptoms.

Going forward, we will learn more about how the two broad categories of Artificial Intelligence are different from each other. But, let's just head first to knowing each of them separately for better understanding

What is Generative AI and How Does it Work?

Generative AI generates original content, imitating human patterns and creativity. It could be a music track, an artwork, or even a news piece

The work of any generative AI goes through the following steps:

Machine learning basics: Generative AI is based on machine learning techniques, primarily deep learning algorithms. The latter analyses vast content bodies – text, images, sounds, etc.
Recognition of patterns: The AI algorithm identifies patterns, structures, and relations within the data. It learns a “language” unique to the data it learns from.
Generation of content: Finally, the learned generative AI can start creating entirely original works. By combining the recognized patterns, it creatively cooperates with them, generating its own – novel and yet similar to the training data.

Benefits of Generative AI

Generative AI has tremendous potential in many industries. From being a supportive tool for creators, aiding them in exploring new creative avenues to serving as a catalyst for innovation and efficiency across various sectors.

More Creativity: It enhances creativity, helping artists, designers, and even musicians to dig into new grounds. AI produces design solutions, composes music, or writes original creative content – poems, scripts, etc.
Creating content at scale: Generative AI can assist with content creation and automate various tasks, from writing product descriptions, and social media posts, to marketing content. This allows freeing more resources for humans to engage in analytical and strategic challenges.‍
Personalized experiences: It goes beyond the simple variant1-variant2 type of content. Along with generating images of things that a person can like more or less, generative AI can also generate images that a person will like the most.

Generative AI is permeating every industry facet, making it increasingly essential to grasp its fundamentals. For those uninterested in delving deep into AI complexities, mastering Generative AI offers a pragmatic approach.

By incorporating Generative AI skills into your role, you can immediately leverage its benefits and stay relevant in a rapidly evolving technological landscape.

Our Applied Gen AI program has been built for working professionals who want to tap into Generative AI and build applications.

We also have Advanced GenAI program for Machine Learning Engineers, Applied Scientists, Data Scientists, who are looking to master LLMs and Generative AI.

Also, learn from Interview Kickstart's co-founder how to job hunt in the age of Generative AI! You should not miss the video if you're clueless how to find a job when Generative AI is on the rise.

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What Is Predictive AI and What Does It Do?

In Predictive AI, the model collects even the largest amount of historical data possible, and this data may come from various sources, such as customers, sensor readings, or finance records.

Pattern Recognition: Machine learning algorithms collect and analyze the data, then look for patterns or correlations. These patterns point to trends or relationships that may not be clear to humans.‍
Predictive Modelling: Based on these recognized patterns, the AI constructs models that can forecast the future based on these patterns. These models can be simple linear regressions or complex neural nets.

Predictive AI is applied in the following fields:

Finance: It can be used to predict how the stock market will change and to evaluate people’s creditworthiness or chances of committing fraud.
Healthcare:Artificial Intelligence in Healthcare is making strides, thanks to ML algorithms. Predictive AI is used to pinpoint patients who are likely to develop some kind of disease or to calculate how efficient a certain therapy is.
Retail: It can help with the forecasting of the product demand and optimizing inventory.‍
Marketing: It is used to adjust marketing strategies, especially while managing ad campaigns and targeting specific audiences.

Predictive AI Advantages

Here are some advantages of Predictive AI:

Improved decision-making: It is able to anticipate future trends and boost businesses by improving their work process. This will save time and improve the efficiency of the organization.
Risk management: Predictive software determines the risks and helps you to be prepared for any pitfalls. Fraud detection, IT security, and operating machinery—everything is estimated beforehand.
Personalized user experience: Predictive software is used by online and offline markets to evaluate the customers' preferences based on their previous searches, purchases, or other online activity. The platform can offer you what you want to see.

What’s the Difference Between Gen AI and Predictive AI?

Up until now, it's easier to understand the comparison between the Gen AI and Predictive AI. In simpler terms, it is Novelty vs Forecasting.

One focuses on generating new content by taking prompts from available data, the other analyzes the available data to predict. However, each has its own distinct focus and functionality.

Focus:

Generative AI: Generative AI focuses on creating new and original content. One way to think about it is as an artist’s palette—Generative AI draws on knowledge from existing works, but its goal is to take these inspirations and use them to create something entirely novel.
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Predictive AI: By contrast, Predictive AI is the fortune teller of the AI world. As the name suggests, this type of AI doesn’t create anything; rather, it analyses existing data to make future predictions.

Based on patterns and relationships it identifies in historical data, Predictive AI can make informed guesses as to what might happen next.

This capability is incredibly valuable for companies and organizations who want to derive meaningful insights from their data to make informed decisions.

Data usage:

Predictive AI: Unlike that of generative AI, the output of the predictive AI is the data it generates and prompts.

As for the previous instance, the e-commerce company uses its historical data on sales and customer preferences. It generates a marketing plan, for instance, with the help of predictive AI on how fast the new product will be sold.

The AI uses historical data on related products and makes predictions based on data patterns.

Other than that, while the outputs of the generative AI are definitely created novelties, the predictive AI never gives answers but probabilities, forecasts, and classifications.
Other examples include the possibilities of a customer’s churn, predicted sales in one quarter, and classifications such as spam and primary emails.

Both the Generative AI and Predictive AI have some limitations as well, understanding the same can offer a better comparison.

Limitations of Generative AI

Generative AI has several limitations even though it is a powerful technology:

Bias and control: The output is only as good as the data it was trained on. Any bias and skewed data input into the model will be reflected in the output. It is also difficult to control the exact output of the AI.
Originality and creativity: The AI can generate a new output, but the question remains about the originality of the output and the essence of human creativity in general. The AI might be using just remixes of ideas it was trained on.
Ethical concerns: The development of realistic deep fakes and AI-generated content modification software brings a lot of concerns and challenges. A more responsible approach to developing and using generative AI is needed.

‍Predictive AI Limitations

These are some of the limitations of Predictive AI:

Data quality: Even the most advanced model will not predict correctly if the data used for training is of poor quality. Predictive software is as good as the database that provides source information.
Unexpected events: None of the models will guess about events out of the general source. Data Prediction can be mistaken in case of unwanted events or sudden topic changes.
Understandability: Finding the correct predictions in case of the algorithm is too complex for ordinary people to understand.

‍Difference Between Gen AI and Predictive AI FAQs

1. What is the difference between predictive and AI?

Predictive AI is a type of AI that focuses on forecasting future events based on historical data. AI, on the other hand, is a broader field encompassing various functionalities, including prediction and content creation.

2. What is the difference between AI and GenAI (Generative AI)?

Similar to the above, AI is a broader term. Generative AI is a specific type of AI that creates entirely new content, like images, music, or text formats.

3. Is ChatGPT predictive AI?

No, ChatGPT is a large language model, which falls under Generative AI. It is trained on large data sets and generates output based on the prompts given to it.

It largely focuses on generating creative text formats like poems, code, scripts, etc. It refers to information available on the internet, including books, scholarly articles, and many more materials while giving output. It doesn’t predict future events, hence ChatGPT is not Predictive AI.

4. Which best describes the difference between predictive AI and generative AI?

They focus on different goals: Predictive AI forecasts the future, while Generative AI creates new content in the present.

5. What is generative AI vs predictive AI vs conversational AI?

Generative AI: Creates new content (images, music, text).
Predictive AI: Forecasts future events (stock prices, customer churn).
Conversational AI: Interacts with users in a conversation-like manner (chatbots, virtual assistants).

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AUTHOR

Naina Batra

Manager, Content Marketing

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Attend our Free Webinar on How to Nail Your Next Technical Interview

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8 Biggest Challenges of Machine Learning

Impact of Generative AI on Jobs in 2024 and Beyond

Top Python Scripting Interview Questions and Answers You Should Practice

Complex SQL Interview Questions for Interview Preparation

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

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