Many generative AI tools appear to have the power to predict. Conversational AI chatbots like ChatGPT can suggest the following verse of a song or poem. Software like DALL-E or Midjourney can create original artwork or realistic images from natural language descriptions. Code completion tools like GitHub Copilot can recommend the following few lines of code.
However, generative AI just isn’t the identical as predictive AI. Predictive AI is its own class of artificial intelligence and even though it is a lesser-known approach, it remains to be a strong tool for businesses. Let's explore the 2 technologies and the important thing differences between them.
What is generative AI?
Generative AI (Generation AI) is artificial intelligence that responds to user prompts or requests with generated original content resembling audio, images, software code, text or videos.
AI generation models are trained on massive amounts of raw data. These models then leverage the encoded patterns and relationships of their training data to know user queries and create relevant latest content that is analogous, but not an identical, to the unique data.
Most generative AI models start with a Foundation modela variety of deep learning model that “learns” to generate statistically probable outputs when asked. Large Language Models (LLMs) are a standard base model for text generation, but there are other base models for various kinds of content generation.
What is predictive AI?
Predictive AI combines statistical evaluation with machine learning algorithms to discover data patterns and predict future outcomes. It extracts insights from historical data to make accurate predictions concerning the most probably upcoming event, consequence, or trend.
Predictive AI models improve the speed and precision of predictive analytics and are typically used for business forecasting to predict sales, estimate demand for services or products, personalize customer experiences, and optimize logistics. In short, predictive AI helps corporations make informed decisions concerning the next step for his or her business.
What is the difference between generative AI and predictive AI?
Both generative AI and predictive AI fall under the umbrella term AI, but they’re different. This is how the 2 AI technologies differ:
Input or training data
Generative AI is trained on large datasets containing hundreds of thousands of pieces of sample content. Predictive AI can use smaller, more targeted datasets as input data.
output
Both AI systems use a component of prediction to provide their results. Generative AI creates novel content, while predictive AI forecasts future events and outcomes.
Algorithms and architectures
Most generative AI models are based on these architectures:
- Diffusion models This works by first adding noise to the training data until it’s random and now not detectable, then training the algorithm to iteratively scatter the noise to provide the specified output.
- Generative adversarial networks (GANs) consist of two neural networks: a generator that produces latest content and a discriminator that evaluates the accuracy and quality of the generated content. These controversial AI algorithms stimulate the model to generate increasingly higher quality results.
- Transformer models Use the concept of attention to find out what’s most significant concerning the data in a sequence. Transformers then use this self-attention mechanism to process entire sequences of information directly, capture the context of the info within the sequence, and encode the training data into embeddings or hyperparameters that represent the info and its context.
- Variations Autoencoder (VAEs) are generative models that learn compressed representations of their training data and create variations of those learned representations to generate latest sample data.
Many predictive AI models now use these statistical algorithms and machine learning models:
- Clusters classifies different data points or observations into groups or clusters based on similarities to know underlying data patterns.
- Decision trees Implement a divide-and-conquer partitioning strategy for optimal classification. Likewise random forest Algorithms mix the output of multiple decision trees to provide a single result.
- Regression models Determine correlations between variables. Linear regression, for instance, represents a linear relationship between two variables.
- Time series Methods model historical data as a series of information points presented in chronological order to predict future trends.
Explainability and interpretability
Most generative AI models lack explainability since it is usually difficult or unattainable to know the decision-making processes behind their results. In contrast, predictive AI estimates are more explainable because they’re based on numbers and statistics. However, the interpretation of those estimates still will depend on human judgment, and incorrect interpretation can result in the unsuitable plan of action.
Use cases for generative AI vs. predictive AI
The decision to make use of AI will depend on several aspects. In an IBM® AI Academy video on selecting the fitting AI use case for your corporation, Nicholas Renotte, principal AI engineer at IBM Client Engineering, points out that “ultimately, selecting the fitting use case for AI, AI and machine learning tools requires consideration of various moving parts. You have to be sure the most effective technology is solving the fitting problem.”
The same goes for deciding whether to make use of generative or predictive AI. “When implementing AI for your corporation, you really want to take into consideration your use case and whether it's a great fit for generative AI or whether one other AI technique or tool is best suited,” says Renotte. “For example, many corporations need to do a financial forecast, but that doesn't normally require a generative AI solution, especially when there are models that may do it at a fraction of the associated fee.”
Use cases for generative AI
Because generative AI is great for content creation, the use cases are quite a few and varied, and more could emerge as technology advances. Here are some examples where generative AI applications may be implemented in several industries:
- Customer Service: Organizations can use chatbots and artificial intelligence-based virtual agents to offer real-time support, provide personalized responses, and initiate actions on behalf of a customer.
- Play: Gen AI models will help create real-world environments, lifelike characters, dynamic animations, and vivid visual effects for video games and virtual simulations.
- Healthcare: Generative AI can create synthetic data to coach and test medical imaging systems to raised protect patient privacy. Gen AI may propose completely latest moleculesthereby accelerating the drug discovery process.
- marketing and promoting: Generative AI can design engaging visuals and write compelling ad and sales copy tailored to every audience.
- Software development: Code generation tools can speed up the technique of writing latest code and automate the debugging and testing phases.
Use cases for predictive AI
Predictive AI is principally utilized 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 aspects.
- Fraud detection: Banks use predictive AI to detect suspicious transactions that indicate fraudulent activity in real time.
- Inventory management: By forecasting sales and demand, predictive AI will help corporations plan and control their inventory.
- Personalized Recommendations: Predictive AI models will help analyze patterns in customer behavior data to make tailored suggestions that may result in an improved customer experience.
- Supply chain management: Predictive AI will help optimize logistics and operations, production schedules, resource allocation, and workload planning.
Discover how generative AI and predictive AI can advance your corporation
Choosing between these two technologies doesn't must be an either-or alternative. Companies can adopt each generative and predictive AI and strategically use them together to drive their business.
Learn more concerning the IBM watsonx™ platform and the way it could actually aid you achieve your AI goals faster. Use the generative AI capabilities of models built on watsonx.ai™ to uncover patterns and anomalies and create accurate forecasts and predictions tailored to your needs.
Find out how Watsonx can bring your AI vision to life
