A lot Generative AI Tools seem to have the power of prediction. Conversational AI chatbots like ChatGPT can suggest the next line of a song or poem. Software like DALL-E or Midjourney can create original artwork or realistic images from natural language descriptions. Autocomplete tools like GitHub Copilot can recommend the next lines of code.
But generative AI is not predictive AI. Predictive AI is a class of its own. artificial intelligenceand while this approach may be less well-known, it remains a powerful tool for businesses. Let’s take a look at both technologies and the key differences between each.
What is Generative AI?
Generative AI (AI generation) is artificial intelligence that responds to a user’s prompt or request with original generated content, such as audio, images, software code, text, or video.
Gen AI models are trained on massive volumes of raw data. These models then leverage the patterns and relationships encoded in their training data to understand user requests and create new, relevant content that is similar, but not identical, to the original data.
Most generative AI models start with a foundation modela type of deep learning model that “learns” to generate statistically probable outcomes when prompted. Large language models LLMs are a common base model for text generation, but other base models exist for different types of content generation.
What is predictive AI?
Predictive AI combines statistical analysis with machine learning algorithms for finding patterns in data and predicting future outcomes. It extracts information from historical data to make accurate predictions about the most likely future event, outcome, or trend.
Predictive AI models improve the speed and accuracy of predictive analysis and are generally used for commercial purposes forecast to project sales, estimate demand for products or services, personalize customer experience, and optimize logistics. In short, predictive AI helps companies make informed decisions about the next step for their business.
What is the difference between generative AI and predictive AI?
Generative AI and predictive AI are both AI, but they are distinct. Here’s how the two AI technologies differ:
Input or training data
Generative AI is trained on large datasets containing millions of content samples. Predictive AI can use smaller, more targeted datasets as inputs.
To go out
While both AI systems use an element of prediction to produce their results, generative AI creates new content while predictive AI forecasts future events and outcomes.
Algorithms and architectures
Most generative AI models rely on these architectures:
- Diffusion models work by first adding noise to the training data until it is random and unrecognizable, then training the algorithm to iteratively diffuse the noise to reveal a desired output.
- Generative Adversarial Networks GANs (Geometric Networks) consist of two neural networks: a generator that produces new content and a discriminator that evaluates the accuracy and quality of the generated content. These adversarial AI algorithms encourage the model to generate increasingly qualitative results.
- Transformer models Use the concept of attention to determine what is most important in the data in a sequence. Transformers then use this self-attention mechanism to simultaneously process entire sequences of data, capture the context of the data in the sequence, and encode the training data into embeddings or hyperparameters that represent the data and its context.
- Variational autoencoders (VAE) are generative models that learn compressed representations of their training data and create variants of these learned representations to generate new data samples.
At the same time, many predictive AI models apply these statistical algorithms and machine learning models:
- Grouping classifies different data points or observations into groups or clusters based on similarities to understand underlying data patterns.
- Decision trees implement a divide-by-divide strategy for optimal classification. Similarly, random forest Algorithms combine the results of multiple decision trees to achieve a single result.
- Regression models determine the correlations between the variables. Linear regressionfor example, represents a linear relationship between two variables.
- Time series The methods model historical data as a series of data points plotted in chronological order to project future trends.
Explainability and interpretability
Most generative AI models lack explainabilitybecause it is often difficult or impossible to understand the decision-making processes behind their results. Conversely, the estimates of predictive AI are more explainable because they are based on numbers and statistics. But the interpretation of these estimates still depends on human judgment, and incorrect interpretation can lead to misconduct.
Use cases for generative AI and predictive AI
The choice to use AI depends on various factors. In an IBM® AI Academy video on Selecting the Right AI Use Case for Your BusinessNicholas Renotte, principal AI engineer at IBM Client Engineering, notes that “ultimately, choosing the right use case for next-gen AI, AI, and machine learning tools requires paying attention to a lot of moving parts. You need to make sure the best technology is solving the right problem.”
The same goes for choosing between generative AI or predictive AI. “If you’re implementing AI for your business, you really need to think about your use case and whether it’s a good fit for generative AI or whether it’s a better fit for another AI technique or tool,” Renotte says. “For example, many businesses want to generate financial forecasts, but that doesn’t typically require a generative AI solution, especially when there are models that can do it for a fraction of the cost.”
Generative AI Use Cases
Because it excels in content creation, Gen AI has multiple and varied functionalities Use cases. Other applications may emerge as the technology advances. Here’s how generative AI applications can be implemented in various industries:
- Customer service: Organizations can use latest generation AI-based chatbots and virtual agents to provide real-time support, deliver personalized responses, and initiate actions on behalf of a customer.
- Games : Gen AI models can help create real-world environments, realistic characters, dynamic animations, and stunning visual effects for video games and virtual simulations.
- Health care : Generative AI can create synthetic data to train and test medical imaging systems to better preserve patient privacy. Gen AI can also propose entirely new moleculesthereby accelerating the drug discovery process.
- Marketing and advertising: Generative AI can design engaging visuals and craft compelling ads and sales copy, personalized for each target audience.
- Software development: Code generation tools can speed up the process of writing new code and automate the debugging and testing phases.
Use cases of predictive AI
Predictive AI is mainly used in finance, retail, e-commerce, and manufacturing. Here are some examples of applications of predictive AI:
- Financial forecasts: Financial institutions use predictive AI models to forecast market trends, stock prices, and other economic factors.
- Fraud detection: Banks are using predictive AI to detect suspicious transactions in real time that indicate fraudulent activity.
- Inventory management: By projecting sales and demand, predictive AI can help businesses plan and control inventory levels.
- Personalized recommendations: Predictive AI models can help analyze patterns in customer behavior data for more personalized suggestions that can lead to better customer experiences.
- Supply Chain Management: Predictive AI can help in optimizing logistics and operations, production plans, resource allocation, and workload planning.
Learn how generative AI and predictive AI can power your business
Choosing between these two technologies doesn’t have to be a matter of either/or. Businesses can embrace both generative AI and predictive AI, strategically using them in tandem to benefit their business.
Learn about the IBM watsonx™ platform and how it can accelerate your AI goals. Leverage the generative AI capabilities of models built on watsonx.ai™ to help you discover trends and anomalies, so you can make accurate forecasts and predictions tailored to your needs.
Learn how Watsonx can bring your AI vision to life
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