Numerous methods for generative AI appear to have predictive capabilities. A poetry or song’s next lyric can be suggested using conversational AI chatbots like ChatGPT. Using descriptions in natural language, software such as DALL-E or Midjourney can produce realistic visuals or creative art. You can suggest the next few lines of code with code completion tools like GitHub Copilot.
However, predictive AI differs from generative AI. Despite being a less well-known strategy, predictive intelligence is a valuable tool for enterprises and belongs to its own class of artificial intelligence. In this section, we will look at the two technologies and their main distinctions.
What is Generative AI?
When a user prompts or requests anything from artificial intelligence, generative AI (gen AI) creates original content text, images, music, video, software code, or other formats in response.
Massive amounts of raw data are used to train Gen AI models. After that, these models make use of the correlations and patterns that have been stored in their training data to comprehend user requests and provide fresh information that is pertinent to the original data while maintaining certain differences.
A foundation model is a kind of deep learning model that “learns” to produce statistically likely outputs when given instructions. Foundation models are the basis for most generative AI models. A popular foundation model for text creation is large language models (LLMs), although there are additional foundation models for other kinds of content generation.
What is predictive AI?
Finding patterns in data and projecting future results are made possible by predictive AI, which combines machine learning algorithms with statistical analysis. To accurately estimate the most likely future event, outcome, or trend, it draws conclusions from historical data.
In corporate forecasting, predictive AI models are commonly utilised to project sales, estimate product or service demand, personalise customer experiences, and optimise logistics. They also improve the speed and accuracy of predictive analytics. Recommending the best course of action for their company, businesses may rely on predictive AI to guide them.
Generative AI vs predictive AI
Although they are separate from one another, both generative and predictive AI are included in the category of AI. The two AI systems differ in the following ways:
Training or input data
Millions of sample videos make up the vast datasets used to train generative AI. Datasets that are more focused and smaller can be used as input for predictive AI.
Output
Predictive AI predicts future events and outcomes, whereas generative AI generates original material. Both AI systems use some degree of prediction to produce their outputs.
The architectures and algorithms
These architectures are used by the majority of generative AI models:
- A desired output is revealed by training the algorithm to iteratively diffuse the noise after first adding noise to the training data until it becomes random and unrecognisable. This is how diffusion models operate.
- The generator and discriminator neural networks are the two components of generative adversarial networks (GANs), which create new content and assess its quality and correctness. The model is encouraged to provide outputs of ever-higher quality by these hostile artificial intelligence algorithms.
- To identify the most crucial information in a sequence, transformer models make use of the attention concept. After that, transformers analyse complete data sequences concurrently, encoding the training data into embeddings or hyperparameters that describe the data and its context and capturing the context of the data within the sequence.
- In order to produce new sample data, variational autoencoders, or VAEs, are generative models that learn compressed representations of their training set.
These machine learning models and statistical techniques are used by numerous predictive AI models in the interim.
- Clustering is a technique used to identify underlying patterns in data by grouping comparable observations or data points into groups or clusters.
- Decision trees apply a splitting method known as “divide and conquer” to achieve the best categorisation results. The output of several decision trees is combined by random forest techniques to produce a single outcome.
- Variable correlations are found using regression models. To illustrate a linear relationship between two variables, consider linear regression.
- Using a chronologically arranged representation of historical data, time series algorithms forecast future trends.
Explainability and interpretability
As it’s frequently impossible or difficult to comprehend the decision-making processes underlying the outcomes of most generative AI models, they lack explainability. On the other hand, because they are based on data and statistics, predictive AI forecasts can be understood better. However, making sense of these estimates still requires human judgement, and a mistaken reading could result in taking the wrong action.
Generative AI vs Predictive AI use cases
A number of factors influence the decision to utilise AI. Nicholas Renotte, chief AI engineer at IBM Client Engineering, states in an IBM AI Academy video on choosing the best AI use case for your company that “deciding on the best use case for gen AI, AI, and machine learning tools ultimately requires paying attention to numerous moving parts.” Ensure that the appropriate problem is being solved by the greatest available technology.
In choosing between generative and predictive AI, the same principle applies. In order to decide whether to employ general artificial intelligence (gen AI) for your use case or another AI technique or tool, Renotte advises businesses deploying AI to carefully consider their use cases. In most cases, a general artificial intelligence (Gen AI) solution is not necessary to develop a financial prediction, for instance, as models may accomplish the same task at a far lower cost.
Applications of generative AI
Gen AI offers many diverse application cases because to its superiority in content production. With further technological advancements, more may appear. The following are some industries where generative AI solutions can be used:
- Customer support: Companies can offer real-time assistance, individualised responses, and take action on a customer’s behalf by utilising chatbots and virtual agents driven by Gen AI.
- Video games: Gen AI models can help create lifelike characters, dynamic animations, realistic surroundings, and striking visual effects for virtual simulations and video games.
- Medical Imaging Systems: To further protect patient privacy, generative AI can produce synthetic data for training and testing purposes. To speed up the drug discovery process, Gen AI can even suggest completely new compounds.
- Generative AI is capable of creating captivating images and persuasive sales language that is tailored to the specific interests of each target audience in marketing and advertising.
- Programming: Debugging and testing stages can be automated, and the authoring of new code can be accelerated with code generation tools.
Use scenarios of predictive AI
Retail, e-commerce, banking, and manufacturing are the industries that employ predictive AI the most. A handful of applications utilising predictive AI are as follows:
- Predictive AI algorithms help financial institutions forecast stock prices, market trends, and other economic variables.
- By spotting suspicious transactions, banks utilise predictive AI to detect fraud in real time.
- Inventory control: By projecting sales and demand, predictive AI may help organisations plan and manage inventory levels.
- Customised recommendations: Predictive AI models can help make better customer experience recommendations based on consumer behaviour data trends.
- Optimisation of logistics and operations, production schedules, resource allocation, and task scheduling are all possible with the use of predictive artificial intelligence (AI).
Learn how your business can benefit from generative and predictive AI
It’s not necessary to choose between these two technologies. Businesses can implement both generative and predictive AI, leveraging them strategically to enhance their operations.
