AI tools and systems that can learn to solve problems without human intervention have proven to be useful developments thus far, but many companies use a hybrid approach known as hybrid AI, which you can benefit from. Hybrid AI is the latest evolution that merges non-symbolic AI, such as machine learning and deep learning systems, with symbolic AI or the embedding of human intelligence.
As digital transformation initiatives drive the mainstream growth of AI, it is critical to select the appropriate AI tools or methods for the job. In numerous instances, a blending of both will be required. This is where hybrid artificial intelligence applications come into play.
The most common definition of hybrid AI is a combination of symbolic and non-symbolic AI, but the definition should include expertise. By incorporating expert context into good algorithms, these algorithms become far more effective and powerful in solving real-world problems.
Hybrid AI use cases
Here’s an example of how hybrid AI is used in web search. When a user types “1GBP to USD,” the search engine detects the currency conversion problem (symbolic AI) and uses machine learning to retrieve, rank, and convert web results (non-symbolic AI) before exhibiting and providing a widget to run.
Weather, travel and sports results are just a few of the query classes handled by both symbolic and non-symbolic AI. Self-driving cars are a major area of current development. To make real-time decisions, self-driving cars must understand basic rules and process environmental signals.
People who used deep learning to develop computer vision and language processing capabilities are now rethinking their implementation in light of hybrid AI. This is because some of these applications use the underlying data and knowledge base to capture bias and identification signals. Hybrid AI is also being used by insurance companies.
You can use deep learning to “check” if the airbag was deployed or what part of the vehicle was damaged by taking a customer photo of the accident. In many cases, this data is not directly available, so we generate it using a deep computer vision model. Traditional symbolic models that do not permit the use of photos allow you to use the same symbols as if the data had been collected manually.
Deep learning models can learn to perform simpler tasks like airbag detection and human detection in such hybrid AI applications, leaving complex inferences to traditional models that are more controllable by humans.
In-home insurance use cases, models that warn customers about the most likely risks to their assets or recommend how AI handles claims based on the magnitude of the damage seen in the photo could exist. So far, the two most significant advantages are a more reliable and simple-to-understand model and more data for modeling.
Intelligent AI hybrid systems are capable of resolving a wide range of complex problems involving inaccuracy, uncertainty, ambiguity, and high dimensionality. Instead of learning everything from data, it combines knowledge and data to solve the problem.
Challenges with hybrid AI
To determine indoor travel, this type of problem necessitates on-the-fly humans obtaining weather forecasts and combining them with actual data such as location, wind speed, wind direction, and temperature. The logic of such a decision is straightforward. This actual context is what is missing.
Some people mistakenly believe that purchasing a graph database essentially provides a context for AI. Most businesses are unaware of the intellectual, computational, carbon, and financial challenges involved in transforming real-world chaos into contexts and connections suitable for machine learning.
Why will the use of hybrid AI grow?
All of this interconnectivity generates an unprecedented amount of data. As organizations digitize, the use of AI grows, allowing them to accomplish more in less time. This could be to improve the customer experience, lower operating costs, or boost sales and profitability. Success, on the other hand, usually results from a clear understanding of the problem and the application of appropriate data and techniques to achieve the desired results.
Hybrid AI is a workaround. Deep learning, despite its immense power, does not appear to be universally superior. Techniques are frequently combined to take advantage of the strengths and weaknesses of each approach, depending on the specific problem to be solved and the constraints imposed.