Predictive AI vs. generative AI

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. This is why a lot of businesses are excited to start using AI to their advantage.

Both gen AI and predictive AI have significant differences and use cases. As AI evolves, distinguishing between these different types helps clarify their distinct capabilities.

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These 2 forms of AI are able to simulate and, more importantly, augment human intelligence. They are machine learning (ML) systems capable of acquiring knowledge and applying insights to support problem solving.

Similarly to how we collect countless memories and stories over our lifetimes, models are trained on vast amounts of data to create an output. Just like when we create art or write a story or develop a new code algorithm, our creations are likely heavily influenced by what we have seen, heard, or learned in the past. The same goes for AI models when they recognize and replicate patterns to provide an output.

Generative AI

Generative AI uses existing data, like texts or developer code, to create something new based on the prompt it’s given. For example, generative AI can be trained on literature to respond to a user prompt with an original story. It aims to provide an answer, complete a sentence, or generate a translation, based on the information it is provided. It may take several prompts and tweaks to achieve your ideal result, which is where methods like fine-tuning and retrieval-augmented generation (RAG) come into play.

Gen AI models use deep learning, an ML technique for analyzing and interpreting large amounts of data. Further, these models use neural networks, a way of processing information that mimics biological neural systems—just like the connections in our brains. Neural networks are how AI can draw connections among seemingly unrelated sets of information.

Predictive AI

Statistical predictions are nothing new, and you'll find them in familiar places like the recommendation engines of streaming video and music services. But now, advances in machine learning allow the technology to work better and faster.

With vast amounts of specific data (sometimes called “big data”), machine learning software can connect patterns, historical events, and often real-time information to predict future outcomes with extremely high accuracy. To achieve this, predictive AI uses advanced statistical methods to store large data sets and connect their patterns over time. This large, diverse sample size of data allows the model to make extremely accurate predictions about future events. These functionalities are what set the model apart from typical human intelligence. 

Both of these AI models are capable of helping businesses save time, lower costs, and optimize resources. Where these benefits differ are their results and explainability.

Gen AI works best with large amounts of data in order to deliver a brand new piece of creative content. The accuracy of this data does not necessarily hinder the machine from delivering an answer. 

On the other hand, predictive AI works best with high quality data in order to deliver accurate predictions. Sensibly, training the ML software on highly accurate data will lead to predictions with higher accuracy.

While generative AI’s process makes it nearly impossible to understand how it came to its decision, predictive AI’s output is created strictly based on the statistics and data it was given. In certain scenarios, this can help users work backwards to understand how the model reached its answer.

A common risk of generative AI is the potential for copyright infringement or plagiarism. When creating a new piece of content—be it writing, music, or art—the model can create outputs that unintentionally resemble material that already exists. This can be risky if it resembles data that is already owned by someone else. An open source model that provides the data the model was trained on can help lower this risk.

Another common risk is hallucinations. When AI models are not confident in an answer, they use the information they have and provide what they can. Sometimes, the machine doesn’t have enough information to provide a correct output. For example, when they are trained to predict what should come next in a sequence, they will fill gaps with inaccurate information due to a lack of data. 

A noteworthy risk of predictive AI is the possibility for bias. While bias can show up in gen AI as well, its impact on predictive AI can lead to severely inaccurate quantitative results. Training a predictive AI model requires high quality data and labeling to support accurate predictions. If some of the data given is old, out of date, or biased, it can skew its accuracy. For example, a predictive AI algorithm may reflect racial or other social biases contained in its data sets when making predictions. This can cause real-world harms, such as introducing biases into credit approvals or hiring processes. 

Another common risk to take into consideration with predictive AI is the lack of certainty. Although this artificial intelligence is capable of making correct predictions, it cannot be certain. There will always be some type of risk when depending on predictive AI as a fact.

Remember, more data is not always better. It’s more important that the data used to train a model is of high quality.

Generative AI has become an increasingly popular tool to generate code, expedite in-depth analysis, and streamline repetitive tasks. Some of the well-known generative AI apps to emerge in recent years include ChatGPT and DALL-E from OpenAI, GitHub CoPilot, Microsoft’s Bing Chat, Google’s Gemini, Midjourney, Stable Diffusion, and Adobe Firefly.

Here are a few different use cases for gen AI.

Code generation and completion: Some generative AI tools can take a written prompt and output computer code on request to assist software developers. It can also help with code explanation by helping level-up junior developers through examples and explanations of senior level code.

Data augmentation: Generative AI can create a large amount of synthetic data when using real data is impossible or not preferable. Synthetic data can be useful if you want to train a model to understand healthcare data without including personal patient information. It can also be used to stretch a small or incomplete data set into a proportional and larger sample size.

Writing: Gen AI systems are good at mimicking human writing. It can respond to prompts for content creation on practically any topic. It can support writing on a wide range of requests— from an email to your boss to the next chapter in your novel. 

Speech and music generation: Using written text and sample audio of a person’s voice, gen AI vocal tools can create narration or singing that mimic the sounds of real humans. Other tools can create artificial music from prompts or samples.

Video and image generation: Gen AI image tools can create pictures in response to prompts for countless subjects and styles. The more detailed the prompt, the better the image or video quality becomes. Some AI tools, like Generative Fill in Adobe Photoshop, can add new elements to existing works, like adding Winnie the Pooh into a famous Van Gogh.

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Predictive AI can provide a faster and more accurate picture of what the year may hold in your specific industry, so you can plan accordingly. That’s why predictive AI is becoming popular among businesses—to find out how they can prepare for future events in order to build on their growth intelligently and efficiently. 

Here are a few use cases of predictive AI.

Financial services: Predictive AI can be used to foresee beneficial opportunities or potential pitfalls—including risks in financial investments, banking, or insurance. If the prediction is made soon enough, businesses have more time to make a change to protect their assets and their customers.

Retail: Predictive AI can predict events—even human behavior. Retail businesses can use this ML tool to predict what (or how, or when) customers are more likely to purchase their products. This can streamline supply chain, marketing, and staffing plans and processes.

Healthcare: Predictive AI can help with early disease detection when similar patterns repeat themselves between different patients or based on patterns within a single patient’s medical history. By analyzing patient data and historical contexts, risks and likelihoods can be measured in order to identify and treat health issues earlier. 

Supply chain: Predictive AI can track patterns of inventory to determine when certain products will ebb and flow during the week, month, or year. It can also predict travel times in order to better protect goods that require a certain temperature such as frozen foods or pharmaceuticals. 

Red Hat® AI is our portfolio of AI products built on solutions our customers already trust. This foundation helps our products remain reliable, flexible, and scalable.

The Red Hat AI portfolio helps organizations:

• Adopt and innovate with AI quickly.
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With Red Hat AI, you get access to platforms that offer both generative and predictive AI capabilities. In addition, our consultants can offer hands-on support for your unique enterprise use cases when building and deploying AI applications alongside critical workloads.

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