TECHNOLOGY
Evolutionary AI
Harnessing the Power of Biological Intelligence for Dynamic, Explainable AI
Dynamic Neural Models:
Mimic real intelligence
ViVum pioneers the development of dynamic neural models that mimic the fluidity and adaptability of Biological Intelligence. By leveraging techniques such as Liquid Time-Constant Networks (LTCs), Reservoir Models, Continuous Time Recurrent Neural Networks (CTRNNs), and Ordinary Differential Equations (ODEs), we create AI systems that can map human-like perception and decision-making to machines and autonomous systems.
Inspired by the brain’s intricate workings, our dynamic neural models offer a natural and efficient approach to AI. They excel at processing temporal and sequential data, enabling real-time adaptation and context-aware decision-making
Adaptive Learning
Continuously update knowledge based on new experiences.
Temporal Awareness
Seamlessly integrate and process information over time.
Contextual Understanding
Interpret data within the context of the environment.
Efficient Computation
Perform complex tasks with minimal memory consumption.
what makes us different
Biological Intelligence: The Key to Scalable AI
Energy Efficiency
Mimicking the brain's energy-efficient structure and function, our Evolutionary AI optimizes energy usage for complex data processing.
Scalability
Efficiently scale to meet computational demands by deploying our Dynamic Neural Models on your existing hardware in under two weeks, optimizing resource utilization without costly supercluster CPUs or GPUs.
Adaptability
Evolutionary AI enables systems to adapt to changing conditions or tasks. Evolvable FPGAs with reconfigurable interconnects and programmable logic blocks allow for adaptive architectures and parameters, enhancing performance and robustness.
Biological Computing
Mimicking the brain's structure enables our systems to continuously learn from time-varying data and adapt in real-time for cognitive tasks, enhanced by evolutionary algorithms' optimized learning and adaptive architectures.
Exploring the Depths
Deep Learning vs Dynamic Learning
Deep Learning
Dynamic Learning
Learning Paradigm
Offline training using backpropagation on static data sets. Requires retraining for adaptation.
Online, continuous learning from time-varying data. Dynamic networks (liquid models) adapt in real-time without retraining.
Network Dynamics
Fixed, layered architecture with static connections. Relies on weight updates during training.
Fluid, adaptive architecture with dynamic connections. Neuron states evolve based on differential equations.
Memory and Computation
Stores knowledge in fixed weights. Computationally intensive training, but efficient inference.
Maintains a dynamic memory through state changes. Computationally efficient for both learning and inferencing.
Temporal Processing
Struggles with long-term dependencies without explicit recurrent architectures like LSTM or GRU.
Inherently captures temporal patterns and long-term dependencies through liquid state dynamics.
Use Cases
Image and Speech Recognition, Natural Language Processing, Recommendation Systems, Fraud Detection, Medical Diagnosis, Predictive Analytics
Autonomous Vehicles, Advanced Robotics, Edge Computing, Sensor Fusion, Anomaly Detection, Predictive Maintenance, Real-Time Decision Making
Applications
Excels in static, well-defined domains like image classification, NLP, and recommendation systems.
Thrives in dynamic, evolving environments such as robotics, autonomous systems, and real-time anomaly detection.
Learning Paradigm
DEEP
Offline training using backpropagation on static data sets. Requires retraining for adaptation.
DYNAMIC
Online, continuous learning from time-varying data. Dynamic networks (liquid models) adapt in real-time without retraining.
Network Dynamics
DEEP
Fixed, layered architecture with static connections. Relies on weight updates during training.
DYNAMIC
Fluid, adaptive architecture with dynamic connections. Neuron states evolve based on different equations.
Memory and Computation
DEEP
Stores knowledge in fixed weights. Computationally intensive training, but efficient inference.
DYNAMIC
Maintains a dynamic memory through state changes. Computationally efficient for both learning and inference.
Temporal Processing
DEEP
Struggles with long-term dependencies without explicit recurrent architectures like LSTM or GRU.
DYNAMIC
Inherently captures temporal patterns and long-term dependencies through liquid state dynamics.
Applications
DEEP
Excels in static, well-defined domains like image classification, NLP, and recommendation systems.
DYNAMIC
Thrives in dynamic, evolving environments such as robotics, autonomous systems, and real-time anomaly detection.
Use Cases
DEEP
Image and Speech Recognition, Natural Language Processing, Recommendation Systems, Fraud Detection, Medical Diagnosis, Predictive Analytics
DYNAMIC
Autonomous Vehicles, Advanced Robotics, Edge Computing, Sensor Fusion, Anomaly Detection, Predictive Maintenance, Real-Time Decision Making
Understanding Insights
Explainable AI
Safety and Security
Evolutionary AI offers a more transparent and interpretable approach to AI compared to conventional deep learning and reinforcement learning models, which are often described as opaque “black boxes.” By employing techniques such as rule extraction, decision trees, and attention-gated routing, Evolutionary AI provides human-readable explanations for its predictions, making it easier to debug, audit, and trust. This transparency is crucial for high-stakes applications in domains like autonomous systems, robotics, and military applications. The lack of explainability in deep learning models hinders their adoption. It’s difficult to trace how specific inputs lead to particular outputs, limiting accountability and transparency.