More than 20 years ago, a computer scientist named David Allen Cohen wrote his Master’s Thesis at the University of Colorado Boulder on the use of Artificial Intelligence and machine learning for energy prediction and control applications.

This was Cohen’s first published foray into the world of artificial intelligence (AI) and its application to the energy sector.

Fast forward to 2003 and Cohen applied the advanced AI concepts to pioneer the industry’s first autonomous intelligent agent-based software platform called GridAgents™, which was a platform that allowed decentralized nodes to utilize software agents that could self-configure, coordinate using peer-to-peer messaging, and autonomously complete complex tasks based on AI and machine learning.

This was the first step in a pathway that would lead to Cohen joining forces with global utility companies and specifically Con Edison to model the entire Manhattan electrical grid, emulating a process through which the platform could self-monitor and automatically control the Electrical Grid in a decentralized way. This was an example of some of the earliest transactive energy applications in the form of automated demand response, advanced distribution automation, and concepts now known as the GridEdge with the ability to integrate distributed energy resources (DERs) into grid operations. GridAgents were also used in the earliest forms of what are now known as Nanogrids and Microgrids, and Smart City managed services in global projects. These applications were very early applications of Machine-to-Machine (M2M) applications and the emerging Industry 4.0 and the Machine Economy.

So what does this have to do with blockchain technology?

Well, while Cohen was able to achieve the above with custom designed AI software and applications, in the last few years he identified a number of elements that were lacking from the system and that – if this sort of system was to be optimized and adapted to modern-day integration – would need to be improved to meet the control needs of a more decentralized system like the electric grid which some call the “largest machine in the world“.

These were properties such as consensus, immutability and the ability to operate in trustless environments for example.

Evolving Blockchain technologies can help to fill these gaps.

Cohen recognized the potential for blockchain technology to remedy the issues that were inherent to his first iteration of AI technology and set out to find the perfect iteration of this technology to integrate with his own software and to also provide additional trust and security for cyber-physical system applications.

While conceptually applicable, however, he found certain shortcomings in the then-current types and executions of the tech. He needed something which would support super fast decentralized transactions without the limitations of the blockchain that underpins bitcoin. He looked at various advancing but different blockchain architectures such as Hyperledger and IOTA but initially only needed the underlying consensus algorithms that were commercially viable and ready to deploy to enterprise customers.

He found it, almost by accident.

A friend introduced him to two people, both veterans of the computer science space – Mance Harmon and Leemon Baird.

Prior to the introduction, these latter two individuals had teamed up to found Swirlds, a commercial entity set up to develop what Harmon and Baird had coined a Hashgraph. A Hashgraph is designed to be a superior consensus mechanism/data structure alternative to blockchain and, when Cohen was introduced to it, he thought it might be the perfect fit for the AI technology he had spent years trying to perfect.

In his own words:

“I needed to know more so I did a deep dive and in fact, after studying the Hashgraph technology, I became so interested that I joined Hashgraph.”

So not only did Cohen think that Hashgraph was a great fit for his attempts to bring about the next generation of AI technology, a generation that could underpin a world in which billions of devices were connected to one another, learning and acting autonomously to advance and reduce inefficiency in practically unlimited real-world use-cases, he actually joined the team that was set up to drive the commercialization and subsequent adoption of Hashgraph as a technology.

And after an extended stealth period, during which Swirlds has perfected the Hashgraph and has conducted numerous tests to verify its claims that this really could be the future of blockchain technology and – in turn – the internet, the company has now come out of stealth mode and is introducing the Hashgraph to the technology sector in its first form factor as private permission-based networks with just the consensus algorithms in place.

Where Cohen and the team at Swirlds will take Hashgraph remains to be seen. The system is already in use at CULedger, which is running a pilot initiative that is investigating the viability of a private, permissioned distributed ledger (DLT) that can be used by credit unions. It is also being tested as one of the consensus algorithms for a new venture that he launched called dcntral.ai (www.dcntral.com). Dcntral™ is developing a blockchain-based software platform that enables true cybersecure exchange of value and automated transactions for the machine economy and while using Hashgraph as the foundation will eventually become blockchain agnostic and support transactions via cryptocurrencies.

Time will tell whether this technology really can outpace blockchain technology in terms of global integration but, from a technical perspective, it certainly looks as though it has the potential to do just that.

Read about what sets Hashgraph aside from blockchain here.

Image courtesy of O’Reilly Conferences via Flickr

Disclaimer: This article should not be taken as, and is not intended to provide, investment advice. Please conduct your own thorough research before investing in any cryptocurrency.