As humans, we tend to learn new things by comparing them with what we already know. Generative AI builds on practices from Predictive AI that’s been around for decades. Generative artificial intelligence (gen AI) uses data in order to create something new. Predictive AI uses data to forecast or infer a highly likely prediction of what could happen in the future.
Generative AI
Generative AI (gen AI) is artificial intelligence that responds to a user’s prompt or request with generated original content, such as audio, images, software code, text or video. Gen AI models are trained on massive volumes of raw data. These models then draw from the encoded patterns and relationships in their training data to understand user requests and create relevant new content that’s similar, but not identical, to the original data.
Most generative AI models start with a foundation model, a type of deep learning model that “learns” to generate statistically probable outputs when prompted. Large language models (LLMs) are a common foundation model for text generation, but other foundation models exist for different types of content generation.
Predictive AI
Predictive AI blends statistical analysis with machine learning algorithms to find data patterns and forecast future outcomes. It extracts insights from historical data to make accurate predictions about the most likely upcoming event, result or trend.
Predictive AI models enhance the speed and precision of predictive analytics and are typically used for business forecasting to project sales, estimate product or service demand, personalize customer experiences and optimize logistics. In short, predictive AI helps enterprises make informed decisions regarding the next step to take for their business.
Generative AI vs predictive AI
Both generative AI and predictive AI fall under the AI umbrella, 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 sample content. Predictive AI can use smaller, more targeted datasets as input data.
- Output — While both AI systems employ an element of prediction to produce their outputs, generative AI creates novel content whereas predictive AI forecasts future events and outcomes.
- Most generative AI models lack explainability, as it’s often difficult or impossible to understand the decision-making processes behind their results. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics. But interpreting these estimates still depends on human judgment, and an incorrect interpretation might lead to a wrong course of action.
- Most generative AI models rely on these architectures:
- Diffusion models work by first adding noise to the training data until it’s random and unrecognizable and then training the algorithm to iteratively diffuse the noise to reveal a desired output.
- Generative adversarial networks (GANs) 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 high-quality outputs.
- Transformer models use the concept of attention to determine what’s most important about data within a sequence. Transformers then use this self-attention mechanism to process entire sequences of data simultaneously, capture the context of the data within the sequence and encode the training data into embeddings or hyperparameters that represent the data and its context.
- Variational autoencoders (VAEs) are generative models that learn compressed representations of their training data and create variations of those learned representations to generate new sample data.
- Many predictive AI models apply these statistical algorithms and machine learning models:
- Clustering classifies different data points or observations into groups or clusters based on similarities to understand underlying data patterns.
- Decision trees implement a divide-and-conquer splitting strategy for optimal classification. Similarly, random forest algorithms combine the output of multiple decision trees to reach 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 data points plotted in chronological order to project future trends.