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With the pursuit of energy efficient and adaptive neural networks as our leading research, here at Microtron AI we have been working towards improved AI models with our several services. We focus on several tools, frameworks, and AI models to build the foundation of a user-friendly interactive AI eco-system.
We are exploring
Verse MVOS uses adaptive, multimodal machine learning that fuses vision, language, biometrics, and spatial data into a unified intelligence. This allows real-time personalization, predictive insights, and sovereign control. Every interaction in Verse learns with you, not against you.
Machine Learning
The sensory layer of Verse technologies allows the OS to “see,” “hear,” and “feel” both the physical and digital worlds. They combine computer vision, spiking neural networks, sensor fusion, and multimodal learning to build a continuous understanding of environments, and interactions.
Perception Systems
Microtron AI’s A-Life research studies how digital organisms, artificial evolution, and self-organizing ecosystems can bring machines closer to life itself — enabling Versona, Verse Realms, and Sensables to evolve, adapt, and interact as living systems rather than static code.
A-Life (Artificial Life)
Verse combines the adaptive learning of neural networks with the structured reasoning of symbolic systems, enabling CALM and Versona to not only perceive and adapt, but also explain, justify, and act with contextual awareness making intelligence both powerful and trustworthy.
Neuro-Symbolic AI
Emotion AI Areas of Research
Embodied AI
Embodied AI is artificial intelligence that exists within and interacts through a body — whether physical (robotics, wearables, IoT) or digital (avatars, Versonas). Unlike traditional AI that’s confined to text or code, embodied AI perceives, moves, and adapts within environments, learning from sensory feedback, context, and real-world interaction.
Self-Reflective Architectures
Self-Reflective Architectures are AI systems designed with the ability to monitor, evaluate, and adapt their own processes — essentially thinking about their own thinking. Instead of just producing outputs, they maintain a meta-cognitive layer that checks for bias, error, or inefficiency and then adjusts strategy in real time.
Quantum Models of Mind
Quantum Models of Mind propose that consciousness and cognition emerge from quantum-level processes — not just classical computation. Instead of treating the mind as a simple information processor, these models suggest thought involves superposition (holding multiple possibilities at once), entanglement (deep interconnectedness between states), and collapse (selecting outcomes based on context and observation).
Verse MVOS Core Research Focuses
Multimodal Learning
Humans naturally process multimodal information all the time (e.g., seeing someone speak and hearing their words simultaneously). Versona Suite strives to mimic this human ability, enabling machines to develop a more holistic and robust understanding of the world by combining insights from different data types.
World Models / Simulation-Based ML
World Models enable AI systems to construct internal simulations of reality, learning not just from raw data but by imagining possible futures. By modeling environments, agents, and outcomes, these systems can plan, predict, and act with foresight instead of reaction. Spatial mapping, optimizing enterprise, and simulating.
Adaptive Agents
Versona Adaptive Agents are intelligent, always-on AI entities designed to automate workflows, streamline communication, and adapt to enterprise and personal needs in real time. Unlike static assistants, these agents continuously learn from interactions, scaling from individual support to full enterprise operations.
The Energy Consumption Crisis
When it comes to saving energy, we can look at spiking neural networks. Spiking neural networks (SNNs) mimic real brain function by sending information only when a neuron's electrical charge hits a specific threshold.
Traditional neural network neurons activate a lot more frequently causing more energy consumption. Chat GPT 4 costs 7,200 MW/h (Mega-Watts per hour) for 150 days to train the AI model, this will be greatly reduced with spiking neural networks. In reference, the average American household consumes 1,214 Watts of power per day.
Spiking Neural Networks (SNNs)
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Verse Adaptive Spatiotemporal Coding is a next-gen intelligence model that learns not just from data, but from patterns across space and time. By encoding how events unfold, overlap, and interact, Verse systems can adapt in real time — whether that’s a Versona responding more naturally, wearables syncing to human rhythms, or MVOS environments shifting to context.
In simple terms: it’s the brain of Verse that codes flow, memory, and adaptation, making AI feel alive in motion.
Adaptive Spatiotemporal Coding
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Liquid Neural Networks are a new class of AI models designed to be adaptive, efficient, and dynamic. Unlike traditional deep learning networks that are fixed once trained, liquid networks continuously adjust their parameters as they process data — meaning they can learn and respond in real time.
Adaptive: They evolve with changing inputs, making them ideal for unpredictable, real-world environments.
Efficient: Require fewer parameters, lowering energy costs and hardware demand.
Liquid Neural Networks
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In Verse MVOS, Hyperdimensional Computing (HDC) acts like a brain for the ecosystem — encoding signals from apps, Versona, and Sensables into high-dimensional patterns, enabling real-time memory, adaptive intelligence, and seamless context transfer across all devices and environments.
This makes Verse MVOS faster, more resilient, and less dependent on heavy cloud models, since intelligence can run directly on edge devices.
It allows your Versona and wearables to recognize patterns, adapt instantly, and carry context everywhere you go in the Verse.