With the integration of AI in enterprise workflows, balancing high performance, cost-efficiency, and safety is crucial.
zherOai X360r
Why Ultra-low and Low Energy
Distributed AI Data Centers?
Distributed AI Data Centers are spread out across different locations, potentially across regions or even countries and can easily scale to accommodate growing data storage and processing needs, and they offer flexibility in terms of location and infrastructure choices.
By placing AI Data Centers closer to end-users, the time it takes for data to travel is minimized, resulting in faster application performance and responsiveness.
Distributed AI Data Centers use a distributed network of servers to cache content closer to users, improving website loading times.
Multiple Distributed AI Data Centers ensure that if one fails, others can take over, minimizing downtime and data loss.
The distributed nature makes them more resilient to natural disasters or other disruptions, as the failure of a single location will not necessarily impact the entire system.
Distributed AI Data Centers enable businesses to reach new markets and provide services in previously underserved regions.
Smaller, localized Distributed AI Data Centers are deployed at the edge of the network (e.g., near cell towers or IoT devices) to process data closer to the source, reducing latency for real-time applications.
Multiple AI Data Centers enable businesses to store and protect sensitive data locally and respect local privacy and data protection rules.
Distributed AI Data Centers enable selective protection of sensitive data and increased cyber defense measures based on local privacy and data protection rules.
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our zherOai HybridQ operating system is quantum-ready
zherOai HybridQ
Hybrid Classical Quantum Operating System
Optimizing ultra-cold matter computing with ultra-low energy classical computing
We have been selected by Cambridge University (UK) and IBM Quantum Research (Switzerland) for our experimental study in Quantum Neural Networks encoding medical data with a complex structure
in 40 experimental and theoretical studies (24 July 2023)
IBM Research, Rüschlikon, Switzerland Research Directions in Quantum Technology
The state of quantum computing applications in health and medicine in 40 experimental and theoretical studies (24 July 2023)
Processing Data With Complex Structure - Experimental Study
Heidari, N., Olgiati,S., Meloni, D., Pirovano, F.,
Noorani, A., Slevin, M. and Azamfirei, L., 2021.
AI built for privacy
From AI to Private AI
Customer 127e6fbfe24a750e72930c 220a8e138275656b8e5d8 f48a98c3c92df2caba935
ESG
AI designed to aid workers,
not to replace jobs
Environmental aspect: greenhouse gas emissions, deforestation, reforestation, pollution mitigation, energy efficiency and water management.
Social aspect: risk and return assessments directly through results in enhancing bias mitigation.
Governance aspect: corporate governance such as preventing bribery, corruption, cybersecurity and privacy practices, and management structure.
AI’s biggest risk is not progress, it’s energy.
Large Data Centres require more than 100 megawatts (MW) of power capacity, enough to power around 80,000 households (Energy Innovation)
How Much Energy Do Data Centers Really Use?
In the coming decade, the rapidly growing demand for computational-intensive applications like Artificial Intelligence and the the rollout of 5G will outpace the efficiency gains that have kept data center energy use in check.
Investments in next-generation computing technologies will be required to avoid potentially steep energy use growth later this decade.
ISO/IEC DIS 4879
QUANTUM COMPUTING STANDARD
Our Quantum computing algorithms are compliant with the preliminary releases of the ISO/IEC DIS 4879 quantum computing standard and are designed to improve the sustainability of Artificial Intelligence models and reduce their energy consumption.
Our Approach
Minimum Optimal Data
We reduce energy consumption by minimizing the size of the feature space and optimizing its performance
We Augment Human Intelligence with Artificial Intelligence
We do not substitute humans with machines, but we improve human learning by augmenting human intelligence with artificial intelligence.
We Make Artificial Intelligence Understandable by Humans
We work to make artificial intelligence interpretable and understandable by humans, and its results explainable.
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