The magical promise of artificial intelligence is that it learns as it goes along and develops additional data insights that enable your organization to make rapid progress.
Now Adaptive AI has been identified by Gartner as one of the top 10 emerging trends for 2023, taking AI capabilities to the next level where it is able to absorb learnings even as it’s being built.
They estimate that by 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the operationalizing AI models by at least 25%.
In a recent Gartner article, they write: “Adaptive AI brings together a set of methods (i.e. agent-based design) and AI techniques (i.e. reinforcement learning) to enable systems to adjust their learning practices and behaviors so they can adapt to changing real-world circumstances while in production.”
What is Adaptive AI?
So, what is Adaptive AI and how does it differ to our current understanding of how AI works?
The key is that Adaptive AI can revise its own code to adjust for changes that weren’t known or predicted when the code was first written, enabling adaptability and resilience to be built into the design so that it can react immediately to changes.
It means that the “learning” phase of a traditional AI system can be bypassed so that the AI is effectively learning whatever is happening.
The value of operationalized AI lies in this ability to rapidly develop, deploy, adapt and maintain AI across different environments in the enterprise.
AI models with this self-adaption built in can develop quicker and with less errors. It creates a faster and more superior user experience by adapting to changing real-world situations.
Altilia at the forefront
Altilia is at the forefront of this approach to optimizing artificial intelligence to take it to the next level.
In our platform, we use reinforcement learning to improve the accuracy of our machine learning models over time.
Also, as a method, we have designed a human-in-the-loop feedback cycle that allows users to trace back the extracted data points to the original source (i.e. the exact position within the document).
This allows them to validate data, and the resulting feedback is taken into consideration to re-train the AI model. In this way the model can improve their accuracy over time and prevent data drifting.
It also means that if the format or layout of the processed documents is gradually modified over time, the algorithm is capable of adapting without the need to refactor our solution.
