Adaptive AI brings together a set of methods (i.e. agent-based design) and AI techniques (i.e. reinforcement learning) to enable systems to change and improve over time in response to new data. This process of continual learning and improvement is essential for businesses that need to be able to quickly adapt to changing conditions.
2. Adaptive AI Future
Adaptive AI is a key step in the direction of intelligent machines. The reason why it could outpace traditional machine learning (ML) models in the near future is its fundamental shift in the way AI is trained. Current and future computing needs will be met through a feedback loop.
3. Adaptive AI Examples
Adaptive AI systems are able to continuously retrain models or apply other mechanisms to adapt and learn within runtime and development environments. A traditional AI is able to process new data but it is unable to learn from interacting with this data; adaptive AI learns from new data and is able to adapt its processing accordingly.
Powered through the Equifax Ignite® feedback loop, adaptive AI allows edge applications to adapt and adjust to their workloads based on their requirements and environments. The three main tenets of Adaptive AI are robustness, efficiency, and agility. Robustness is the ability to achieve high algorithmic accuracy despite changes in data distribution. Efficiency is the ability to use computational resources effectively. Agility is the ability to quickly adapt learning models in response to changes.
In conclusion, adaptive AI is outpacing traditional AI with its flexibility and improved learning capabilities.
Adaptive AI’s possible outcomes are more accurate than those of traditional AI because it can learn and adapt to new data and information more effectively. As a result, adaptive AI is better equipped to handle complex tasks and make sustainable decisions.