Introduction: Generative AI versus Predictive AI
Artificial intelligence (AI) is hot right now.
But there are many kinds of AI. So what type should you be using? And for which use cases?
In this post, we’ll focus on two:
What is the difference between Generative AI and Predictive AI?
In summary:
- generative AI focuses on creating new and original content,
- while predictive AI aims to make accurate predictions or forecasts about future events.
Generative AI generates new examples that resemble the training data (like generating an image of Kanye West based on thousands of online pictures of Kanye).
Predictive AI analyzes historical data to identify patterns and relationships for making predictions (like, what ad placements should be used to best convert a given audience cohort – based on past trends).
Technically speaking, they employ separate techniques, algorithms, methodologies, and have different applications across various industries.
However, practically these forms of AI are typically generating some kind of output. Output that can and should be used to put into action.
Generative AI Overview
Generative AI, also known as generative artificial intelligence, is an exciting field that focuses on creating computer systems capable of generating new and original content.
Unlike traditional AI systems that are designed to perform specific tasks based on pre-programmed rules, generative AI is designed to learn from existing data and generate new content that resembles the input it was trained on.
This type of AI has the potential to revolutionize various industries, including art, music, writing, and even scientific research.
At the core of generative AI is a model called a generative model, which learns the underlying patterns and structures in a given dataset. The model then uses this knowledge to generate new examples that share similar characteristics with the original data. This process involves training the model on a large dataset and allowing it to learn the statistical relationships and dependencies within the data.
Generative AI has shown remarkable results in various creative fields.
- In the field of art, generative AI can create unique and visually stunning artwork by analyzing existing paintings and generating new ones that mimic the style of famous artists.
- In music, generative AI can compose original melodies and harmonies by learning from a vast collection of music pieces.
- In writing, generative AI can generate coherent and engaging stories or even assist authors in developing new plot ideas.
Predictive AI Overview
Predictive AI, also known as predictive artificial intelligence, is a field that focuses on developing systems capable of making accurate predictions or forecasts about future outcomes based on current and historical data.
At the heart of predictive AI is a predictive model, which is trained using historical data to identify patterns and relationships. The model learns from this data and extrapolates from the patterns it discovers to make predictions about future events.
The more data the model is trained on, the more accurate its predictions can become.
Predictive AI finds applications in a wide range of industries.
- In finance, predictive AI can analyze historical market data to predict stock prices or identify potential investment opportunities.
- In healthcare, it can analyze patient records and medical data to forecast disease progression or predict patient outcomes.
- In marketing, predictive AI can analyze consumer behavior and preferences to make targeted recommendations or personalize advertisements.
The algorithms involved enable the models to recognize extremely complex patterns and correlations in the data. Hence, predictions with a high degree of accuracy.
Comparing and Contrasting Generative AI with Predictive AI
Generative AI and predictive AI are two distinct branches of artificial intelligence with different goals and approaches.
Goals:
- Generative AI: The goal of generative AI is to create new and original content.
- Predictive AI: The goal of predictive AI is to anticipate and forecast events or trends.
Data Usage:
- Generative AI does not require specific labels or target output. Rather, it learns the underlying patterns and structures of the data.
- Predictive AI analyzes historical data to identify patterns, but requires labeled data with known outcomes to train models.
Techniques and Algorithms:
- Generative AI models generate content by learning the probability distribution of the training data and sampling from it. (It often uses techniques like generative adversarial networks (GANs), variational autoencoders (VAEs), or deep reinforcement learning.)
- Predictive AI uses techniques such as regression analysis, time series analysis, classification algorithms, or machine learning models like decision trees, random forests, or neural networks.
Output:
- The output of generative AI is newly generated content that did not exist in the original dataset, but resembles the training data.
- The output of predictive AI is predictions or forecasts about specific outcomes or events in the future.
Generative AI Use Cases & Applications
It almost goes without saying that this type of AI has tons of potential applications. Here are a few:
- Art Generation
- Music Composition
- Fashion Design
- Product Design
- Creative Writing
- Image and Video Synthesis
- Virtual Character Creation
- Architecture and Interior Design
- Scientific Research
- Video Game Content Generation
A few of those to expand on:
- Art Generation: Generative AI can create unique and visually appealing artwork by learning from existing paintings and generating new pieces that mimic the style of famous artists.
- Creative Writing: Generative AI can generate coherent and engaging stories, poems, or even assist authors in developing new plot ideas by analyzing existing literature and generating original content.
- Image and Video Synthesis: Generative AI can generate realistic images and videos by learning from a dataset of existing visual content, allowing for the creation of synthetic visuals for various applications.
Popular tools that use generative AI are ChatGPT, Midjourney, and Creative.ai. These are content generation tools that save time and enable creativity.
Predictive AI Use Cases & Applications
Similarly, predictive AI has countless applications. Here is a short list of options:
- Financial Forecasting
- Demand Forecasting
- Predictive Maintenance
- Fraud Detection
- Customer Churn Prediction
- Healthcare Diagnosis and Prognosis
- Predictive Analytics in Marketing
- Energy Consumption Forecasting
- Traffic Prediction and Optimization
- Supply Chain Optimization
A few of those to expand on:
- Fraud Detection: Predictive AI can analyze patterns and anomalies in transaction data to detect fraudulent activities, such as credit card fraud or insurance claims fraud, allowing organizations to identify and prevent fraudulent behavior in real time.
- Predictive Analytics in Marketing: Predictive AI can analyze customer data, demographics, purchase history, and online behavior to identify target audiences, personalize marketing campaigns, and optimize customer segmentation strategies.
- Demand Forecasting: Predictive AI can analyze historical sales data, market trends, and customer behavior to forecast future demand for products or services, enabling businesses to optimize inventory, production, and supply chain management.
An example of a tool using predictive AI is Google Ads – whose smart bidding features are using the predictions to make automated actionable decisions on bidding amounts, keywords, and more.
Another example is Aidaptive’s eCommerce AI platform. The technology uses predictive AI to look at online user behavior and purchases in order to predict what products, images, and results are most likely to lead to transactions. This output is implemented to automatically run conversion rate optimization in real time on websites.
Conclusion
In summary, generative AI’s goal is to create new and original content from training data, while predictive AI aims to make accurate predictions or forecasts about future events based on what happened in the past.
Both types are very powerful and have countless applications.
Generative AI gets a lot of attention because of how consumer-facing the outputs are: written words, video content, audio sounds, and automatically generated images from scratch.
Predictive AI tends to have more behind the scenes applications for businesses looking to run operations more efficiently and effectively.
AI is proliferating at an astounding rate, and these two types lead the pack for high-value use cases.
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