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What distinguishes predictive AI from generative AI?

AIPred

Introduction#

A lot of generative AI tools appear to have predictive capabilities. ChatGPT and other conversational AI chatbots can recommend the next line of a poetry or song. Natural language descriptions can be converted into creative artwork or realistic visuals using software such as DALL-E or Midjourney. The following few lines of code can be suggested by code completion tools like GitHub Copilot.

Recursive AI, however, is not generative AI. Even while it may not be as well-known as other forms of artificial intelligence, predictive AI is nonetheless a potent tool for companies. Let's look at the two technologies and their main distinctions from one another.

Generative AI: What is it?#

Artificial intelligence that creates original content—such as text, photos, audio, software code, video, or pictures—in response to a user's prompt or request is known as generative AI, or gen AI.

Large amounts of unprocessed data are used to train Gen AI algorithms. Then, using the correlations and patterns that have been stored in their training data, these models are able to comprehend user requests and provide fresh information that is pertinent to the original data while maintaining certain differences.

The foundation model, a kind of deep learning model that "learns" to produce statistically likely outputs when prompted, is the first step in the creation of most generative AI models. Although numerous foundation models exist for other types of content production, large language models (LLMs) are a common foundation model for text generation.

Predictive AI: what is it?#

Statistical analysis and machine learning techniques are combined by predictive AI to identify trends in data and project future results. It uses insights gleaned from past data to forecast most likely future events, outcomes, or trends with accuracy.

Predictive AI models are commonly used for business forecasting to project revenues, estimate product or service demand, tailor customer experiences, and optimize logistics. They improve the speed and precision of predictive analytics. To put it briefly, predictive AI assists businesses in determining the best course of action for their particular situation.

What distinguishes predictive AI from generative AI?#

Under the general category of artificial intelligence, both generative and predictive AI are separate. The two AI technologies vary in the following ways:

1. Data input or training#

Millions of samples of material are used in enormous datasets to train generative AI. More focused, smaller datasets can be used as input data for predictive AI.

2. Results#

While both AI systems use some degree of prediction to produce their outputs, predictive AI makes predictions about what will happen in the future, whereas generative AI generates original content.

3. Architectures and algorithms#

These designs are the foundation of most generative AI models:

  1. Diffusion models function by first introducing random and unidentifiable noise into the training set of data, and then training the algorithm to disperse the noise iteratively until the intended result is revealed.

  2. Two neural networks make up generative adversarial networks (GANs): a discriminator that assesses the created content's quality and accuracy, and a generator that creates new content. The model is encouraged to provide outputs of ever higher quality by these adversarial AI techniques.

  3. Transformer models prioritize the most crucial information in a sequence by using the attention notion. The training data is then encoded into embeddings or hyperparameters that describe the data and its environment by transformers using this self-attention mechanism to analyze complete data sequences simultaneously.

  4. Variational autoencoders (VAEs) are generative models that produce fresh sample data by learning compressed representations of their training set and varying those learned representations.

In the meantime, these machine learning models and statistical algorithms are used by numerous predictive AI models:

  1. In order to identify underlying data patterns, clustering divides various data points or observations into groups or clusters according to commonalities.

  2. For the best classification, decision trees use a divide-and-conquer splitting method. In a similar vein, random forest algorithms blend several decision trees' output to produce a single outcome.

  3. Correlations between variables are found using regression models. A linear relationship between two variables is represented, for example, by linear regression.

  4. In order to predict future trends, time series approaches model past data as a set of data points shown chronologically.

Interpretability and Explainability#

Because it is frequently difficult or impossible to comprehend the decision-making processes behind the outcomes of most generative AI models, these models lack explainability. On the other hand, because predictive AI projections are based on data and statistics, they may be understood better. However, interpreting these estimates still requires human judgment, and a mistaken interpretation could result in the wrong action being taken.

Use cases for predictive versus generative AI#

Using AI depends on a number of aspects. The chief AI engineer at IBM Client Engineering, Nicholas Renotte, states in an IBM® AI Academy video on choosing the best AI use case for your company that "finally, picking the right use case for gen AI, AI, and machine learning tools requires paying attention to numerous moving parts." Make sure the greatest technology is being used to the appropriate problem.

This also applies to choosing between generative and predictive AI. "You really need to think about your use case and whether it's right for gen AI or whether it's better suited to another AI technique or tool," advises Renotte when using AI for your company. "Many businesses, for instance, wish to produce a financial forecast, but that usually won't call for a general artificial intelligence (Gen AI) solution, especially since there are models that can accomplish that for a much lower cost."

Use cases for generative AI#

Generation AI has a wide range of applications as it is so good at creating content. With the development of technology, more may appear. Here are some examples of industries where generative AI solutions can be used:

  1. consumer service: Businesses can give real-time help, individualized responses, and take action on behalf of a consumer by utilizing chatbots and virtual agents driven by Gen AI.

  2. Video games and virtual simulations can benefit from the use of Gen AI models to help create lifelike characters, dynamic animations, realistic surroundings, and striking visual effects.

  3. Healthcare: To further protect patient privacy, generative AI can provide synthetic data for testing and training medical imaging systems. Additionally, Gen AI can suggest completely novel compounds, hastening the process of finding new drugs.

Use cases for predictive AI#

The primary industries using predictive AI include manufacturing, e-commerce, retail, and finance. A few instances of predicted AI applications are as follows:

  1. Financial forecasting: To predict market movements, stock prices, and other economic aspects, financial organizations employ predictive AI models.

  2. Fraud detection: Predictive artificial intelligence is used by banks to identify potentially fraudulent transactions in real time.

  3. Inventory management: Predictive AI can assist businesses in planning and managing inventory levels by forecasting sales and demand.

Conclusion#

It's not necessary to pick one of these two technologies over the other. Businesses can use both predictive and generative AI, using them strategically to enhance their operations.