There's no doubt that AI is transforming industries across the world, as it changes the way businesses work12. Also, people are increasingly interacting with AI, and it's moving far beyond hypothetical uses and testing. It's embedding itself in our lives in our jobs, writing habits, and the growing number of devices we're relying on in our homes and cars34. As our daily lives continue to further integrate AI into them, we're going to be hearing more and more about the concept of Hybrid AI, or a system that uses several types of AI models to achieve multiple goals and achieve far more than a single AI system in action12. What is hybrid AI, in full detail? This article will explain how it works and why it's important for the future35.

AI and large language models (LLMs) are making massive headlines across the world for several reasons. Not only are millions of people using AI to generate content, solve equations, create art, analyze financial data, facilitate customer service, and more12. While AI can mimic human beings and their conversations, it's important to mention that the technology has a long way to go. Their main limitation is their probabilistic nature—meaning, they can help predict the next word needed due to the massive number of data points they are fed. Thus, AI is still rife with misinformation and misleading information, which AI developers refer to as AI hallucination3. While this may not be a worry for most people writing and perfecting their emails, errors associated with news, healthcare, finance, and other industries can become quite dangerous4. Leading AI innovators are working around the clock to make their systems less prone to errors and misinformation, which brings us to hybrid AI and its growing application in the world5.

Errors in AI are a reality that everyone should be aware of and be concerned about. This is where Hybrid AI is becoming more and more crucial. It works around the clock to combine varying types of AI models to create a more accurate and robust solution. It can bring together the many strengths of AI and move behind the limitations of single systems.12

For example, in the world of healthcare, a generative AI model can help explain a diagnosis in a way that both doctors and patients can understand. However, relying on generative AI alone can lead to misinformation due to lacking, patterned, or biased data. Moving to a Hybrid AI system can allow a healthcare organization to combine a traditional machine learning model that's trained on large amounts of quantified clinical data with a generative AI model that can output accurate information.34 We can also explore the world of education, Hybrid AI can offer personalized learning experiences for students by including data-driven insights with generative AI tools that offer custom educational content. This can give teachers more time to mentor and guide their students, while AI deals with tasks that are highly repetitive and time-consuming.5 It can also be applied to creative fields to help artists, writers, and musicians create better and more inspiring work. In the blending of human thought processes and intuition with AI, Hybrid AI can help open up a time era where human capabilities and creativity are amplified thanks to new technologies.6 One of the most exciting aspects of Hybrid is its ability to enhance human-AI collaboration in ways that we couldn't ever predict. As Hybrid AI systems become more intelligent and prevalent, they are more likely to become key partners for human decision-making, as they work to amplify our innate abilities rather than replace them.78

This approach can go much further than the healthcare world, it can also be highly important in the financial sector to help organizations detect fraud. It can work to combine rule-based algorithms with machine-learning models that can analyze everything from transaction patterns to red-flagging suspicious activities and more.12

Hybrid AI models are also used in manufacturing, as they can help predict equipment maintenance failures to help reduce downtime, improve productivity, and keep things running efficiently. There are typically three types of AI involved in Hybrid AI systems:

• Symbolic AI: This takes logic-based systems that follow a set of rules and work rapidly within structured environments.2

• Machine Learning (ML): Machine learning models are crafted to make predictions or take actions that can be prone to mistakes and biases. They output information based on the data they are trained on.23

• Generative AI: This AI generates new data outputs that can come in the form of text, sound, images, and more. It can also output human-like content (which is commonly used with chatbots), but misinformation and biased information is a part of the game.4

One of the key advantages of hybrid AI is how adaptable it is. In the real world, there isn't a single AI model that can solve every problem. Hybrid AI can provide flexibility so that systems can be tailored to adapt to specific applications by pooling together more than one model that can work together to provide vital information in the most accurate way possible12. This is especially helpful where real-time decision-making comes into play. Consider autonomous vehicles that require hybrid AI systems so that the cars can avoid obstacles and optimize routes. The data must be pushed to cars in real-time, or risk putting people in dangerous situations3. Hybrid models can help address this situation while making AI systems more transparent and easy to audit4. This is also important within industries such as finance and healthcare, where accuracy is a must if AI is to be a part of the equation5.

While the advantages of hybrid AI are quite clear, many changes come with putting them together. The key is working to ensure that various models can communicate seamlessly. Also, they can increase computational power exponentially and decrease latency12. This comes with massive costs, as putting them together is extremely expensive and time-intensive to build3. Thankfully, the increasing availability of cloud computing resources and advanced AI development frameworks is making development more cost-effective and greatly shortening development times45.

The future of hybrid AI is beyond promising, as you can expect for applications across industries—from finance to manufacturing to healthcare and more. By increasingly combining various AI technologies, AI systems will become far more effective and solve numerous complexities that exist in the world.12 As AI technologies like quantum and LLMs continue to evolve, you can expect hybrid AI systems to be amazingly sophisticated. You can also expect AI reasoning to become much more similar to human-like reasoning. Hybrid AI will play a huge role in making this possible. The future of AI lies not in choosing between different models, but in working to integrate them so that they can all work together.34