Conventional AI is Unintelligent

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

less energy
0 x
less computing resources
0 X
computational efficiency
0 X

Biological Intelligence for Scalable 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 harnesses the power of Biological Intelligence, deploying next-generation foundational models inspired by the dynamic, adaptive nature of life itself. Leveraging liquid neural models that mimic biological brains’ plasticity, we create AI systems that continuously learn, evolve, and adapt to real-world complexities, bridging the gap between simulations and reality.

Evolutionary AI transcends the limitations of static architectures, capturing nuances and context with the flexibility and resilience of Biological Intelligence. This transformative approach unlocks new frontiers in computing, seamlessly integrating AI with real-world complexities.

Comparative Analysis

Our Evolutionary AI vs. Conventional AI

Evolutionary AI

Conventional AI

Learning Paradigm

Dynamic Learning

Dynamic Learning, sometimes referred to as Liquid 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, sometimes referred to as Liquid 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.

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

exploring practical applications

Use Cases

vivum logo large icon

Technology Evolved

Sign up for updates!

© 2024 ViVum Computing. All Rights Reserved.