The Enterprise Neurosystem has a charter to provide sensor networks, AI models and software applications, IT and communications infrastructure, and a higher order AI cross-correlation engine in a single global architecture to help monitor and manage climate change events. We provide our software free of charge , and have a five year development roadmap to fully deploy and support this infrastructure.
In essence, we are technology builders with a singular focus
We anticipate deploying hundreds of non-intrusive sensor networks across a multitude of species in nature. This includes forests and fungi, ant colonies, beehives, mussels, birds, kelp, coral reefs, and various mammal and plant species that can be monitored for real-time climate impact and predictive analysis. When integrated with sensors like satellites, atmospheric and water quality indicators, underground monitoring and other systems, a comprehensive multi-layered neurology for the planet is formed. One that allows deeper analysis of climate change patterns, leads to new discoveries and corrective actions, and fosters a deeper understanding of humanity’s role in the preservation of our environment.
Our projects include:
Donated by IBM, the Enterprise Neurosystem provides access to AI software analyzing acoustic variance and predictabilities. Enterprise Neurosystem participants use this across varying projects. Surprisingly, the EN Team discovered that Bee wing frequencies inside of hives closely resemble wave frequency patterns produced by Tokomak Fusion Research at Stanford SLAC.
Managed bee populations are declining rapidly at a rate of 45% per year in the USA alone[J2] , where 20% is the average, and they are considered critical for almonds, blueberries, apples and other crops. And beehives are unique climate sensors – bees generate unique sonic signatures when in distress or planning a migration, pollutants can be captured in honey and propolis, and beehives are located in agricultural areas where methane can be measured, and in rural locations where this analysis is infrequent at best.
In our community, we have a number of scientists who specialize in the management and preservation of bee populations, and AI engineers and hardware designers who are collaborating with this team to deploy a multi-function sensor that detects ultrasonic cues, pollutants in hive materials, and gas level detection. Dennis O’Connell of Yahoo, Ryan Coffee of Stanford SLAC, Leo Hoarty of Vizio AI and Noah Wilson-Rich of The Best Bees Company have been leading this project’s development team. The first version of this sensor has been deployed in urban and rural environments with both mobile network and satellite link access, with a real time data feed and analysis on a mobile phone application. The AI Acoustics beehive software was donated to the community by IBM Research, for free distribution to the world.
Mycorrhizal Network Sensors
These are fungal networks that intertwine with the root systems of plants and trees, to create a symbiotic and mutually supportive network in nature. These systems can extend hundreds of miles beneath a jungle or remote forest. Plants in harsh environments depend on these fungal networks for health and nutrients, and recent discoveries have indicated measured and repeated electrical spike patterns in the fungus that may be akin to morse code, and could potentially represent a form of communication. Thus, the fungus may enable a communications network between plants, and further analysis and interpretation is required. Our sensor team designed an underground monitor and will offer AI models to detect and cross-correlate these electrical pulses, and look for environmental cues that match the pulse patterns.
Mussel Farms and Beds
Mussels are natural sensors in waterways, and are frequently dissected by scientists and health officials to understand water quality, pollution and the presence of toxic bacteria. This is generally conducted in the interest of public health and consumption, but also indicates a strong potential as a local environmental sensor. Scientists have begun development in earnest of robotic mussels that can detect pollutants, oxygen levels, chemical markers and bacteria. The Enterprise Neurosystem is working with a group of mussel scientists in the US and New Zealand to provide AI software and sensors to deploy in the riverways of the world.
Satellite Data Networking
The community has a number of academics and professionals from satellite startups in Chile, Canada and the United States. Subprojects include imagery analysis for ocean pollutants and beach plastics, methane plume analysis, gravimetric of mass change-measurement, and other layers of real-time and historical data that will contribute to the complete data picture of the planet. Coverage ranges from 9×9 meter to 200×200 mile spans across the globe. These projects can provide multi-dimensional, interleaved images of the globe’s health in near real-time.
Catfish and Tilapia Farming
The Enterprise Neurosystem is working with fish farmers in Ghana to help them monitor and manage tilapia and catfish populations, which have been impacted by climate change and other factors. The community is building general purpose sensors and AI software to analyze the environmental conditions and fish populations, and to help optimize these environments for maximum agricultural resiliency. By working directly at the community level, our community can provide immediate assistance for climate events, and help generate a network effect in multiple regions.
Telecommunications networks will represent the primary transmission mode for the sensor data, in addition to satellite communications with more remote areas. Ravi Sinha, VP of Technology Development and Solutions at Reliance Jio (India) leads the research team that includes engineers from Verizon and other telecom firms, working on AI-enhanced 5G and eventually 6G frameworks to enable rapid and reliable data transmission in the most remote regions of the world.
Led by Heiko Ludwig of IBM Research, the Enterprise Neurosystem is studying the latest advances in federated AI frameworks. These frameworks can greatly enhance the collective large-scale architectures that can enable the Enterprise Neurosystem’s cross-correlation capability while maintaining data security and privacy. The team is conducting its first proof of concept utilizing multiple layers of natural and satellite sensor data, within a methodology that allows AI training across decentralized machines while preserving local data and privacy.
Human Brain Communication Networks
Dr. Avniel Ghuman of the University of Pittsburgh Medical School has joined the community, and is known for MEG (Magnetoencephalography) research in the areas of human brain networking, information flow and sensory interpretation. The community is exploring ways to leverage his research and the communication patterns discovered therein – and replicate them in our field architectures, both from an AI and network communications perspective.
Sensor network and AI architecture example
The yellow circles are individual sensor networks, which can have thousands of nodes. They can be associated with the networks indicated above, or in jungles, rivers, fish farms, ant populations, plant species and so on. A first tier of AI analytics software linked to each network will identify patterns in real time data. They will then forward the observed patterns and anomalies to a second level of AI regional analysis, who will begin to apply these findings in a larger data analysis construct. This will eventually lead to countrywide analysis of all sensors in a single AI architecture, and provides a holistic view of the climate change impact on a multitude of species, natural resources and agricultural environments. In turn, this will eventually lead to larger scale AI analysis, to measure the cross-border effects of pollution and climate change on the entire planet.