Adaptive AI: More efficient, resilient, and energy-efficient
- Catherine Nohra China
- Sep 24
- 2 min read
Gartner has named adaptive AI one of the key strategic technology trends to watch. It predicts that organizations that adopt adaptive AI will outperform their competitors by 25% by 2026. B2CLOUD has designed and patented its own algorithmic recommendation method based on adaptive AI.
While “traditional” forms of AI tend to break down when faced with obstacles, adaptive AI algorithms and methods, such as those developed by B2CLOUD for its recommendation method, can modify their behavior based on their experiences. They adjust their results and recommendations when faced with changes in input data or in the context or environment in which they operate.
DYNA: Our effective and resilient adaptive AI recommendation method
Dyna (for Dynamic RecSys) is the method developed at B2CLOUD that learns, adapts, and improves B2B recommendations.
This algorithmic flexibility makes our results more effective and relevant, even in dynamic situations.
Our recommendation system is based on the following method.
Analysis of the incoming query to understand its requirements (criteria, profiles).
Definition of a timelength to determine the learning improvement cycle.
During inference, automatic adaptation of ranking weights and labeled vs. unlabeled data weights, in a process of continuous improvement of rankings and data useful for the final recommendation.
Key strengths:
Continuous improvement
Our system learns from ranking weights and data useful for recommendations, in a continuous learning cycle and according to a defined time length. This avoids massive consumption of computing resources.
Robustness and resilience.
Our system is capable of dynamically adjusting its own weights to evolve based on user data and requests, experience, and the duration of a learning session.
Rankings evolve iteratively during the learning cycle, taking into account changes in specifications, characteristics, and weights of previous data and rankings.
Transfer learning
Our system is capable of exploiting the knowledge (knowledge base, ontology) acquired in the context of a particular task or domain (such as cloud computing) and applying it to another related domain in B2B, or to any other domain whose decision-making processes are impacted by dynamic changes in contexts, environments, or experiences.
Our Adaptive AI recommendation methods are patented.
Comments