Current models like ChatGPT Guzzle Energy
Consuming up to 20GW - that’s not smart; it’s unsustainable
Autonomous systems don’t really have Autonomy
Conventional AI struggles with unpredictability. True autonomy remains elusive
Robotic humanoids look, feel, and move Robotically
Lacking the grace and adaptability of their biological counterparts
It Doesn't Have To Be This Way
VIVUM'S EVOLUTIONARY AI
Reduces power, boosts performance, and dynamically adapts to novel scenarios
COMPARED TO CONVENTIONAL AI
Transforming the landscape of AI by making it more accessible explainable efficient powerful
OUR FOCUS
Launch, implement and propagate a computing paradigm that benefits not just humanity but all life on our planet. The Earth demands It.
Our Evolutionary AI is powered by a next generation library of foundational models that draw inspiration from the dynamic, adaptive nature of biological systems. By developing liquid neural models that mimic biological brains’ plasticity, we create AI systems that continuously learn, evolve, and adapt to real-world complexities. We bridge the gap between training / simulations AND the real world.
Unlike traditional, static AI architectures, Evolutionary AI models capture nuances, context, and temporal dependencies with remarkable flexibility and resilience. This transformative technology holds the key to unlocking new frontiers in computing.
Comparative Analysis
Our Evolutionary AI vs. Conventional AI
Evolutionary AI
Conventional AI
Learning Paradigm
Dynamic Learning
Dynamic Learning enables AI models to continuously learn and adapt in real-time, without the need for offline retraining or simulations. Leverages techniques like Liquid Time-Constant Networks (LTCs), Continuous Time Recurrent Neural Networks (CTRNNs), and reservoir models to create highly adaptive, efficient systems.
Deep and Reinforcement Learning
Deep learning and reinforcement learning, the most widely-used approaches in modern AI, train large neural networks on vast datasets. They often require extensive offline training (and simulation) and lack real-time adaptability, making them less suitable for dynamic environments. Updating the models with new data typically involves time-consuming and resource-intensive retraining.
Computational Basis
Actual Neural Circuitry
Bio-inspired algorithms that mimic the principles of natural evolution and the brain’s plasticity.
Synthetic Neural Networks
Artificial networks composed of interconnected nodes, organized into hierarchical layers, that learn from data.
Data Processing
Continuous and Dynamic
Online learning from high-velocity, high-volume data streams in real-time. Evolutionary AI is designed to process data continuously, updating its models without the need for batch processing or offline training.
Discrete and Static
Offline training on large, static datasets that have been carefully curated and annotated. Deep learning models are extremely data-hungry, often requiring millions of labeled examples to achieve state-of-the-art performance. Once trained, these models can be deployed for efficient inference on new data but struggle to adapt to changing environments.
Explainability
Transparent and Interpretable
Evolutionary AI offers greater transparency and interpretability through techniques like rule extraction, decision trees, and attention-gated routing. These mechanisms provide human-readable explanations for the model’s predictions, making it easier to debug, audit, and trust. Crucial for high-stakes applications in autonomous systems and robotics.
Black Box Models
Conventional deep learning models are notoriously opaque and difficult to interpret, often described as “black boxes.” Due to their complex, nonlinear structure, it can be challenging to trace how specific inputs lead to particular outputs. This lack of explainability hinders adoption in mission-critical applications where accountability and transparency are paramount.
Key Advantages
Adaptability, Efficiency, Real-Time Learning
Evolutionary AI excels in dynamic environments, continuously learning and adapting to new data patterns without the need for retraining. Its brain-inspired computational basis enables highly efficient processing, making it suitable for edge devices and energy-constrained applications. Ideal for autonomous systems, robotics, and sensor fusion.
High Performance on Specific Tasks
Deep learning has achieved state-of-the-art results on a wide range of tasks, particularly in areas like computer vision, natural language processing, and pattern recognition. Well-established frameworks, tools, and large pre-trained models are readily available, making it easier to develop and deploy deep learning solutions for specific applications.
Use Cases
Autonomous Vehicles, Advanced Robotics, Edge Computing, Sensor Fusion, Anomaly Detection, Predictive Maintenance, Real-Time Decision Making, High Frequency / Volume Networks
Image and Speech Recognition, Natural Language Processing, Recommendation Systems, Fraud Detection, Medical Diagnosis, Predictive Analytics
Comparative Analysis
Our Evolutionary AI vs. Conventional AI
Evolutionary AI (E-AI)
Conventional AI (AI)
Learning Paradigm
E-AI
Dynamic Learning
Dynamic Learning enables AI models to continuously learn and adapt in real-time, without the need for offline retraining. Leverages techniques like Liquid Time-Constant Networks (LTCs), Continuous Time Recurrent Neural Networks (CTRNNs), and reservoir models to create highly adaptive, efficient systems.
AI
Deep & Reinforcement Learning
Deep learning and reinforcement learning, the most widely-used approaches in modern AI, train large neural networks on vast datasets. They often require extensive offline training and lack real-time adaptability, making them less suitable for dynamic environments. Updating the models with new data typically involves time-consuming and resource-intensive retraining.
Computational Basis
E-AI
Actual Neural Circuitry
Bio-inspired algorithms that mimic the principles of natural evolution and the brain’s plasticity.
AI
Synthetic Neural Networks
Artificial networks composed of interconnected nodes, organized into hierarchical layers, that learn from data.
Data Processing
E-AI
Continuous and Dynamic
Online learning from high-velocity, high-volume data streams in real-time. Evolutionary AI is designed to process data continuously, updating its models without the need for batch processing or offline training.
AI
Discrete and Static
Offline training on large, static datasets that have been carefully curated and annotated. Deep learning models are extremely data-hungry, often requiring millions of labeled examples to achieve state-of-the-art performance. Once trained, these models can be deployed for efficient inference on new data but struggle to adapt to changing environments.
Explainability
E-AI
Transparent and Interpretable
Evolutionary AI offers greater transparency and interpretability through techniques like rule extraction, decision trees, and attention-gated routing. These mechanisms provide human-readable explanations for the model’s predictions, making it easier to debug, audit, and trust. Crucial for high-stakes applications in autonomous systems and robotics.
AI
Black Box Models
Conventional deep learning models are notoriously opaque and difficult to interpret, often described as “black boxes.” Due to their complex, nonlinear structure, it can be challenging to trace how specific inputs lead to particular outputs. This lack of explainability hinders adoption in mission-critical applications where accountability and transparency are paramount.
Key Advantages
E-AI
Adaptability, Efficiency, Real-Time Learning
Evolutionary AI excels in dynamic environments, continuously learning and adapting to new data patterns without the need for retraining. Its brain-inspired computational basis enables highly efficient processing, making it suitable for edge devices and energy-constrained applications. Ideal for autonomous systems, robotics, and sensor fusion.
AI
High Performance on Specific Tasks
Deep learning has achieved state-of-the-art results on a wide range of tasks, particularly in areas like computer vision, natural language processing, and pattern recognition. Well-established frameworks, tools, and large pre-trained models are readily available, making it easier to develop and deploy deep learning solutions for specific applications.
Applications
E-AI
Autonomous Vehicles, Advanced Robotics, Edge Computing, Sensor Fusion, Anomaly Detection, Predictive Maintenance, Real-Time Decision Making
AI
Image and Speech Recognition, Natural Language Processing, Recommendation Systems, Fraud Detection, Medical Diagnosis, Predictive Analytics
