Core Technologies
Multi-agent AI
Multi-agent AI leverages LLMs to facilitate communication between autonomous agents, enhancing coordination and optimizing complex problem solving across diverse systems.
Generative AI
Generative AI, in particular Large Language Models (LLMS) make it possible to utilize general world knowledge, orchestrate multiple models and technologies, and interface with Decision AI systems through natural language.
Surrogate Optimization
Surrogate Optimization uses models to simulate complex environments, enabling safe and efficient strategy optimization. This approach to solve real-world problems, such as healthcare interventions.
Evolutionary Computation
Evolutionary computation is an optimization method that mimics natural selection, helping discover innovative strategies for decision-making in complex environments.
Deep Learning
Deep learning is another foundation for AI research with the Evolutionary AI team. The main focus of our recent research is on optimizing deep learning architectures for performance and computational cost, utilizing of multiple datasets through multitask learning, and discovering useful neural network building blocks.
Neuroevolution
Neuroevolution combines evolutionary principles with deep learning to optimize neural networks. Our recent advancements focus on making AI more adaptive and efficient.
Trustworthy AI
Trustworthy AI ensures AI models are safe, explainable, and built with accountability for confidence in every decision.