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Both generative AI and predictive AI use advanced artificial intelligence algorithms to address complex business and logistical challenges.
Generative AI utilizes sophisticated modeling to add a creative element. Generative AI software creates images, text, video, and software code based on user prompts.
Predictive AI, in contrast, uses large data repositories to recognize patterns across time. Predictive AI applications draw inferences and suggest outcomes and future trends.
Let’s examine generative AI and predictive AI, lay out their use cases, and compare these two powerful forms of artificial intelligence.
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Differences between Generative AI vs. Predictive AI
At their foundation, both generative AI and predictive AI use machine learning. However, generative AI turns machine learning inputs into content, whereas predictive AI uses machine learning to determine the future and boost positive outcomes by using data to better understand market trends.
The chart below illustrates the difference in how they are used. Generative AI generally finds a home in creative fields like art, music, and fashion. Predictive AI is more commonly found in finance, healthcare, and marketing, although there is plenty of overlap.
Among the key differences between generative AI and predictive AI:
| Parameters | Generative AI | Predictive AI |
|---|---|---|
| Objective | Generates new, original content or data | Predicts and analyzes existing patterns or outcomes |
| Function | Creates new information or content | Makes predictions based on existing data |
| Training data | Requires diverse and comprehensive data | Requires historical data for learning and prediction |
| Examples | Text generation, image synthesis | Forecasting, classification, regression |
| Learning process | Learns patterns and relationships in data | Learns from historical data to make predictions |
| Use cases | Creative tasks, content creation | Business analytics, financial forecasting |
| Challenges | May lack specificity in output | Limited to existing patterns, may miss novel scenarios |
| Training complexity | Generally more complex and resource-intensive | Requires less complex training compared to generative models |
| Creativity | Generative AI is creative and produces things that have never existed before | Predictive AI lacks the element of content creation |
| Different algorithms | Generative AI uses complex algorithms and deep learning to generate new content based on the data it is trained on | Predictive AI generally relies on statistical algorithms and machine learning to analyze data and make predictions |
What is Generative AI?
Generative AI is an emerging form of artificial intelligence that generates content. Popular examples of generative AI software include ChatGPT, Midjourney, and Runway. These programs are now used by millions of users to create text, images, video, music, and software code.
Indeed, generative AI functionality is all about creating content. It combines algorithms, deep learning, and neural network techniques to generate content that is based on the patterns it observes in other content. It analyzes vast amounts of patterns in datasets to mimic style or structure to replicate a wide array of contemporary or historical content.
For example, the image below was created using a text-to-image generative AI model. The prompt I fed the system: “a captivating portrait of a mysterious woman in a moonlit forest using a realistic style similar to Vermeer, with soft, golden lighting and a close-up framing.”
For many more examples of how generative AI serves users, see our guide: Generative AI Examples
How Do Generative AI Models Work?
Generative AI models perform several levels beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms, and neural networks, generative AI offers a tech-based foray into the world of creativity.
These models are typically trained on large datasets containing a wide range of information, such as text, images, and audio. By analyzing patterns and relationships within the data, the models can understand the underlying structure and generate new content similar in style and context.
Although the output of generative AI is classified as original material, in reality, it uses machine learning and other AI techniques based on the earlier creativity of others – this is a significant criticism of generative AI. This emerging AI technology taps into massive repositories of content and uses that information to mimic human creativity, which raises a question that is now being debated: Is generative AI a copyright violation?
One common type of generative AI model is the generative adversarial network (GAN). The GAN framework consists of two main components:
- Generator: The generator is responsible for generating new outputs.
- Discriminator: The discriminator acts as a critic to evaluate the generated outputs for authenticity.
The generator and the discriminator are trained simultaneously to improve the generator’s ability to fool the discriminator. To train the GAN, the generator first creates random noise as input and attempts to generate outputs that resemble the data it was trained on. The discriminator then receives real and generated outputs and aims to classify them correctly as real or fake.
The feedback from the discriminator is used to update the generator, encouraging it to improve its outputs and generate content that is increasingly difficult for the discriminator to distinguish from real data.
Benefits of Generative AI
Generative AI offers numerous benefits for those who seek to create content, and its creative potential is seemingly vast.
- Creative writing and art: Generative AI can do things like analyze the entire works of Dickens or Rollins or Hemingway and produce an original novel that seeks to simulate their style and writing patterns. It can produce this same mimicry in images and music. Generative AI excels at creating coherent text and images, aiding in writing, translation, and artistic endeavors.
- Data enhancement: It contributes to data augmentation, personalization, and accessibility, making generative AI a versatile tool for various industries, including healthcare and finance.
Limitations of Generative AI
For all of its benefits, the limitations of generative are quite challenging – including historical biases in supposedly neutral system outputs.
- Vulnerability to adversarial attacks: Generative models can be susceptible to adversarial attacks, where input data is carefully crafted to mislead the model into producing incorrect or unintended outputs.
- Contextual ambiguity: Generative AI models may struggle with understanding and maintaining context over long passages. They can be sensitive to slight changes in input phrasing and might produce inconsistent or contextually inappropriate responses.
- Potential biases: Generative AI models are trained on large datasets, and if these datasets contain biases or prejudices, the AI system may unintentionally replicate them in the generated content.
Generative AI Use Cases
By producing fresh content, generative AI is used to augment but not replace the work of writers, graphic designers, artists, and musicians. It is particularly useful in the business realm in areas like product descriptions, variations to existing designs, or helping commercial artists explore different concepts. Among its most common use cases, generative AI can:
- Text: Generate credible text on various topics. It can compose business letters, provide rough drafts of articles, and compose annual reports.
- Images: Output realistic images from text prompts, create new scenes, and simulate a new painting.
- Video: Compile video content from text automatically and put together short videos using existing images.
- Music: Compile new musical content by analyzing a music catalog and rendering a new composition.
- Product design: Can be fed inputs from previous versions of a product and produce several possible changes that can be considered in a new version.
- Personalization: Personalize experiences for users such as product recommendations, tailored experiences, and new material that closely matches their preferences.
What is Predictive AI?
Predictive AI uses statistical algorithms to analyze data and make predictions about future events. It is sometimes also called predictive analytics and may sometimes be loosely termed as machine learning.
Predictive AI studies historical data, identifies patterns, and makes predictions about the future that can better inform business decisions. Predictive AI’s value is shown in the ways it can detect data flow anomalies and extrapolate how they will play out in the future in terms of results or behavior. When used properly, it enhances business decisions by identifying a customer’s purchasing propensity as well as upsell potential and can offer enormous competitive advantages.
To learn more about how AI can serve businesses, see our in-depth overview: AI Detector Tools
How Do Predictive AI Models Work?
Predictive AI models leverage historical data, patterns, and trends to make informed predictions about future events or outcomes. These models analyze past data, identifying patterns or relationships within that data, and then use this information to generate predictions about future outcomes.
Building a predictive AI model requires a business to collect and preprocess the data. This involves gathering relevant data from various sources and cleaning it by handling missing values, outliers, or irrelevant variables. The data is then split into training and testing sets, with the training set used to train the model and the testing set used to evaluate its performance.
Once the data is ready, the predictive AI model can be trained. Various machine learning algorithms, such as linear regression, decision trees, and neural networks, can be used. The choice of algorithm depends on the nature of the data and the type of prediction being made.
During training, the model learns the relationships and patterns in the data by adjusting its internal parameters. It tries to minimize the difference between its predicted outputs and the actual values in the training set. This process is often iterative, where the model repeatedly adjusts its parameters based on the error it observes until it reaches an optimal state.
The accuracy and performance of predictive AI models largely depend on the quality and quantity of the training data. Models trained on more diverse and representative data tend to perform better in making predictions. Additionally, the choice of algorithm and the parameters set during training can impact the model’s accuracy.
Benefits of Predictive AI
For businesses that are experts at using predictive AI, the benefits range from understanding future trends to getting more value from each data repository they have.
Future trends: Predictive AI can enlighten management on future trends, opportunities, and threats. It can be used to recommend products, upsell, improve customer service, and fine-tune inventory levels.
Better accuracy: Predictive AI adds a deeper dimension and greater accuracy to management processes. When used optimally, it increases the chance of success and positive business outcomes, particularly in the area of forward-looking inventory management.
More value from data: Through accurate predictions and improved decision-making, predictive AI can help organizations glean far more value from the data by providing more information about each sector of that data.
Limitations of Predictive AI
Despite its benefits, predictive AI can’t yet predict the future with 100% accuracy, and some businesses have been frustrated by this challenge.
- Data quality and availability: Predictive AI heavily relies on data availability and quality. If the data used for training the AI system is incomplete, inaccurate, or biased, it can lead to flawed predictions.
- Ethical concerns: Using predictive AI raises ethical concerns, particularly regarding privacy, bias, and discrimination. Does a given company have the right to glimpse future trends about certain consumers?
- Interpretability: Many predictive models, especially complex ones like deep neural networks, lack interpretability. It may be challenging to understand how the AI arrives at its predictions or to identify any underlying biases in its decision-making process.
- Resource Intensive: Training and deploying sophisticated predictive models can be computationally expensive and resource-intensive, limiting their practicality in certain environments.
To understand how today’s leading gen AI companies approach the market, read our guide: Generative AI Companies: Top 12 Leader
Predictive AI Use Cases
Predictive AI is finding innumerable use cases across a wide range of industries. If managers knew the future, they would always take appropriate steps to capitalize on how things were going to turn out. Anything that improves the likelihood of knowing the future has high value in business. Predictive AI use cases include financial forecasting, fraud detection, healthcare, and marketing. Predictive AI can:
Financial services: Enhances financial forecasts. By pulling data from a wider data set and correlating financial information with other forward-looking business data, forecasting accuracy can be greatly improved.
Fraud detection: Spot potential fraud by sensing anomalous behavior. In banking and e-commerce, there might be an unusual device, location, or request that doesn’t fit with the normal behavior of a specific user. A login from a suspicious IP address, for example, is an obvious red flag.
Healthcare: Find use cases such as predicting disease outbreaks, identifying higher-risk patients, and spotting the most successful treatments.
Marketing: More closely define the most appropriate channels and messages to use in marketing. It can provide marketing strategists with the data they need to write impactful campaigns and thereby bring about greater success.
Bottom Line: Generative AI vs. Predictive AI
As AI evolves, the distinction between generative AI and predictive AI is likely to fade. AI systems that seamlessly merge generative AI and predictive AI are emerging. Instead of using one set of algorithms to predict and another to create, advanced AI systems combine both and can deliver both types of results.
By combining the algorithms that identify trends and forward-looking correlations with those that recombine those patterns into new creations, the value of AI will be improved even further.
While there are certainly differences between generative AI and predictive AI, these distinctions are far from rigid. Each contains similar elements to the other. It is how they are put to use that brings about the different outcomes. As AI evolves, both generative AI and predictive AI will play a role in reshaping the future.
To learn more about how generative AI can benefit users, see our guide: The Benefits of Generative AI.