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Where Networks Collide

It is our good fortune to live in a time where we are so interconnected. 46.1% [1] of the world's population has access to the internet and that percentage will continue to rise with each year. The rush to bring internet access to those who still do not have has become a point of focus for tech giants Facebook, Google, and Space X [2,3]. Through the spreading of the internet, the world as a whole can increase its sharing of knowledge. On top of this increase in sharing, the growth of worldwide knowledge will follow as more and more users come online.

It takes very little effort to reach out to a friend over sms, voice your opinions on social media, or purchase the latest in consumer technology. All of these actions are the result of investing in massive infrastructure. By taking advantage of the internet, personal information, and usage history, companies are able to provide an incredible amount of value to users inside the human network. I will define the human network as a network in which humans are directly interacting with one another or with a machine/system regardless of the medium.

Machine networks and artificial intelligence are also growing. These networks have been created as spinoffs of the human network. They are by design integrated with the human network, but they have the ability to act autonomously and facilitate the communication/interaction between machines.

With the example of a Point-of-Sale system with connected inventory automation we can imagine a scenario in which these two networks work well together. If you and 500 other people order a medium black coffee daily at a cafe who is implementing this system, that cafe's machine would theoretically be able to calculate how many lbs of coffee beans they are using to meet the demand of 501 medium black coffees. After a obtaining an arbitrary amount of transaction data and coffee bean usage data the system would be able to predict the amount of coffee beans that the cafe would need to purchase and at what frequency in order to utilize all of the coffee beans before expiration. If the system was granted permission to order the beans automatically then it would be interacting with the API of the coffee bean distributor or roaster to place orders establishing a machine network. The machine network could then be further extended to involve your personal spending history if you were to use a connected platform to pay with for example Apple pay.

Another example of machine network-human network interaction is the smart phone. The permissions granted to applications on our devices may seem intrusive or unnecessary, but they bring with them an impressive level of cohesion and ease of use. For many users, the perceived benefits outweigh the potentially negative impacts of allowing a large amount of 3rd parties access to your personal information.

The issue with current efforts to curb GHG emissions is that many of them fail to take advantage of machine networks to their fullest extent. When examining certain aspects of environmental protection efforts machine networks can interact with the Environment in a far more efficient way than humans can. GNX is building upon the assumption that a machine-environment network will always be more efficient in achieving intended goals when compared to a human-environment network.




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