The convergence of AI and Blockchain: what’s the deal? by Francesco Corea

How AI can change Blockchain

Although extremely powerful, a blockchain has its own limitations as well. Some of them are technology-related while others come from the old-minded culture inherited from the financial services sector, but all of them can be affected by AI in a way or another:

  • Energy consumptionmining is an incredibly hard task that requires a ton of energy (and then money) to be completed (O’Dwyer and David Malone, 2014). AI has already proven to be very efficient in optimizing energy consumption, so I believe similar results can be achieved for the blockchain as well. This would probably also result in lower investments in mining hardware;
  • Scalability: the blockchain is growing at a steady pace of 1MB every 10 minutes and it already adds up to 85GB. Satoshi (2008) first mentioned “blockchain pruning” (i.e., deleting unnecessary data about fully spent transactions in order to not hold the entire blockchain on a single laptop) as a possible solution but AI can introduce new decentralized learning systems such as federated learning, for example, or new data sharding techniques to make the system more efficient;
  • Security: even if the blockchain is almost impossible to hack, its further layers and applications are not so secure (e.g., the DAO, Mt Gox, Bitfinex, etc.). The incredible progress made by machine learning in the last two years makes AI a fantastic ally for the blockchain to guarantee a secure applications deployment, especially given the fixed structure of the system;
  • Privacy: the privacy issue of owning personal data raises regulatory and strategic concerns for competitive advantages (Unicredit, 2016). Homomorphic encryption (performing operations directly on encrypted data), the Enigma project (Zyskind et al., 2015) or the Zerocash project(Sasson et al., 2014), are definitely potential solutions, but I see this problem as closely connected to the previous two, i.e., scalability and security, and I think they will go pari passu;
  • Efficiency: Deloitte (2016) estimated the total running costs associated with validating and sharing transactions on the blockchain to be as much as $600 million a year. An intelligent system might be eventually able to compute on the fly the likelihood for specific nodes to be the first performing a certain task, giving the possibility to other miners to shut down their efforts for that specific transaction and cut down the total costs. Furthermore, even if some structural constraints are present, a better efficiency and a lower energy consumption may reduce the network latency allowing then faster transactions;
  • Hardware: miners (and not necessarily companies but also individuals) poured an incredible amount of money into specialized hardware components. Since energy consumption has always been a key issue, many solutions have been proposed and much more will be introduced in the future. As soon as the system becomes more efficient, some piece of hardware might be converted (sometimes partially) for neural nets use (the mining colossus Bitmain is doing exactly this);
  • Lack of talent: this is leap of faith, but in the same way we are trying to automate data science itself (unsuccessfully, to my current knowledge), I don’t see why we couldn’t create virtual agents that can create new ledgers themselves (and even interact on it and maintain it);
  • Data gates: in a future where all our data will be available on a blockchain and companies will be able to directly buy them from us, we will need help to grant access, track data usage, and generally make sense of what happens to our personal information at a computer speed. This is a job for (intelligent) machines.

So WTF is Settlement Quality AI Data (SQAID) for microgrids?!

I’ve been racking my brain searching for the bridge between my profession as a Senior Energy Professional in the energy and environmental sector and my new ventures in artificial general intelligence (AGI). And, I’ve discovered the answer…Settlement Quality AI Data (SQAID) for microgrids.

Here’s a bit of history that might help the understanding.

I’ve been active in the California energy sector for since 1995 and have guided public and private organizations through the intricate process of implementing energy and environmental programs. These programs include advanced metering infrastructures, competitive green power supply, distributed and utility scale generation, performance contracting agreements and solar power purchase agreements.

As the Vice President of Sales & Energy Services for Commonwealth Energy Corporation, I managed the San Diego Association of Governments (SANDAG) account and sold over 100 MW of renewable electricity services to their SANDAG members.

When I first arrived at Commonwealth the market was in chaos and the California Energy Crisis looming. As an Energy Service Provider, we were mandated by the California Public Utility Commission to comply and to seamlessly integrate all policy and activities with the Investor Owned Utilities (IOUs) and the California Independent System Operator (California ISO).

At the same time, the IOUs began building Advanced metering infrastructures (AMIs) throughout the state. An AMI is an integrated system of smart meters, communications networks, and data management systems that enables two-way communication between utilities, service providers and customers.

As per the California ISO, “Metering and telemetry ensure operational accuracy. Accurate metering of electricity generated or consumed provides key data inputs for accurate settlement calculations. Direct measurement of a generator or load participant through telemetry allows the ISO to manage and monitor power generation in real-time.”

Therefore the functions of the Meter Service Providers (MSPs) and Meter Data Management Agents (MDMA) were crucial to provide Schedule Coordinators (SCs) with…Settlement Quality Meter Data (SQMD).

Meaning all meter data went through a vigorous Validation, Editing and Estimation (VEE) process and then parceled for various functions related to customer billing, load and financial settlement, load profile creation and procurement forecasting, demand response programs, etc.

So WTF is Settlement Quality AI Data (SQAID) for microgrids?!

By definitiion, microgrids are modern, small-scale versions of the centralized electricity system. They are a localized group of electricity sources and loads that normally operates connected to and synchronous with the traditional centralized electrical grid (macrogrid), but can also disconnect to “island mode” — and function autonomously as physical and/or economic conditions dictate.

SCADA is a control system architecture that uses computers, networked data communications and graphical user interfaces for high-level process supervisory management, but uses other peripheral devices such as programmable logic controllers and discrete PID controllers to interface to the microgrid.

The SCADA computer system handles operator interfaces that enable monitoring and the issuing of process commands, such as controller set point changes. However, the real-time control logic or controller calculations are performed by networked modules that are connected to field sensors and actuators.

So Why is any of this Important?!

So imagine the value created by SQAID, instantaneously read from Smart AI meters, devices and sensors by an AI Schedule Coordinator which controls microgrid operations and facilitates authority having jurisdiction compliance requirements. There could be billions of energy dollar savings created by the use of a common SQAID algorithm.

Imagine an AI Agent using SQAID and acting in the capacity of the traditional MSP, MDMA, SC and SCADA process. And served from a microgrid, guaranteeing reliability, health and life safety, security, and most importantly human and AI comfort and long-term sustainability.

Instantaneously!

About the Author

Patrick A. Weller is known for his authorship of the Smart Modernization and Retrofit Technology Solutions (SMARTS) Program recognized by the American Recovery and Reinvestment Act of 2009. His historic monetization of ARRA investment capital was achieved by using an ironclad investment grade audit and a standard of care that withstood the “test of scrutiny” of the federal, state, local government and Investor Owned Utilities.

Related online information:

City of Grover Beach Goes Green: https://www.grover.org/ArchiveCenter/ViewFile/Item/111

Datapult AMI: https://www.elp.com/articles/2001/07/lge-enertech-selects-datapult-as-provider-of-energy-monitoring-services.html

SOL DOMINIQUE I | Dennys W. Sacramento Power Purchase Agreement: https://youtu.be/EMYsIiKZWdk

References: California ISO: http://www.caiso.com/Pages/default.aspx; Wikipedia https://en.wikipedia.org/wiki/Microgrid