The concept of a Technological Singularity is increasingly widely accepted throughout the technology and business worlds. More and more, it is real- ized that within the next few decades there will be a transition to a new society and economy in which machine intelligence is the dominant factor; and novel digital and organic technologies acting on multiple scales will network together to produce emergent “global brain” dynamics of unprecedented complexity and sophistication [Bro97] [Kur06] [Vin93] [Goe07].
Humanity faces many challenges on the path to a positive Singularity; among these is the contemporary global economic system. In numerous respects, today’s standard financial mechanisms and institutions are not up to the task of serving as the economic engine of a smooth transition to a broadly positive Singularity. New, more flexible, open and rapidly adaptive economic structures and dynamics are needed [GGG16].
Blockchain provides a powerful tool for managing transactions in a Singularity- era economy [CB14] ; but blockchain is just a tool, and it must be used in the right way. A blockchain-based framework designed to serve the needs of AI Agents as they interact with each other and with external customers can enable the emergence of a collective intelligence. And it is critical that this framework be designed with positive principles in mind:
- Democratic governance on specific issues – giving the community a voice in the system will tend to make the system act for the benefit of the community;
- Encouraging innovative new Agents to enter the network, and creating the conditions for Agents to act in a manner that feeds the collective intelligence;
- Directing a significant percentage of the network’s efforts toward causes of broad benefit.
SingularityNET has been designed to meet these requirements, via
- Delivering intelligence services to corporations, individuals and organiza- tions;
- Fostering the emergence of increasingly powerful distributed general intelligence;
- Deploying artificial intelligence for ever-increasing benefit of as many humans and other sentient beings as possible.
SingularityNET is explicitly designed both to be highly valuable in the current context, and to lay the groundwork for the emergence of a future self-modifying, decentralized “artificial cognitive organism” with the eventual potential for general intelligence and beneficial ethical characteristics beyond the human level. It is a practical design inspired by long theoretical thinking and prototyping on the part of the founders regarding concepts such as Artificial General Intelligence [Goe16a], Open-Ended Intelligence [WV16] and the Global Brain [Hey07]. #WellerAGI
December 19, 2017
The value and power of Artificial Intelligence is growing dramatically every year, and will soon dominate the internet – and the economy as a whole. However, AI tools today are fragmented by a closed development environment; most are developed by one company to perform one task, and there is no way to plug two tools together. SingularityNET aims to become the key protocol for networking AI and machine learning tools to form a coordinated Artificial General Intelligence.
SingularityNET is an open-source protocol and collection of smart contracts for a decentralized market of coordinated AI services. Within this framework, the benefits of AI become a global commons infrastructure for the benefit of all; anyone can access AI tech or become a stakeholder in its development. Anyone can add an AI/machine learning service to SingularityNET for use by the network, and receive network payment tokens in exchange.
SingularityNET is backed by the SingularityNET Foundation, which operates on a belief that the benefits of AI should not be dominated by any small set of powerful institutions, but shared by all. A key goal of SingularityNET is to ensure the technology is benevolent according to human standards, and the network is designed to incentivize and reward beneficial players. #WellerAGI
Let us invite you to a following talk by Ben Goertzel entitled From Here to Human-Level AGI in 4 Simple Steps taking place on Monday May 21st, 9:30-10:30 a.m.
Venue: Czech Institute of Informatics, Robotics, and Cybernetics (Jugoslávských partyzánů 1580/3, Prague 6) – Red Lecture Room (B-246)
Abstract: AI technology has entered the mainstream of business and society, but there is still a large gap between the current crop of task-specific „narrow AI“ tools and the Artificial General Intelligences (AGIs) envisioned by futurists and SF authors. To get from here to true AGI will require advances in (at least) four different aspects. First, it will require coordination of different AI agents at various levels of specificity into an overall complex, adaptive AI network — which is the problem addressed by the SingularityNET blockchain-based AI framework. Second, it will require bridging of the algorithms used for low-level intelligence such as perception and movement (e.g. deep neural networks) with the algorithms used for high-level abstract reasoning (such as logic engines). Third, it will require embedding of AI systems in physical systems capable of interacting with the everyday human world in richly nuanced ways — such as the humanoid robots being developed at Hanson Robotics. Fourth, it will require the development of more sophisticated methods of guiding abstract reasoning algorithms based on history and context (an area lying at the intersection of AGI and automated theorem proving). Fortunately,while none of them are actually simple, all of these aspects of the AGI problem are topics of active research by outstanding teams around the world, making it plausible that AGI at the human level and beyond will be achieved during our lifetimes.
Dr. Ben Goertzel is one of the world’s foremost experts in Artificial General Intelligence, a subfield of AI oriented toward creating thinking machines with general cognitive capability at the human level and beyond. He also has decades of expertise applying AI to practical problems in areas ranging from natural language processing and data mining to robotics, video gaming, national security and bioinformatics. He has published nearly 20 scientific books and 140+ scientific research papers, and is the main architect and designer of the OpenCog system and associated design for human-level general intelligence.
Ben is the CEO of SingularityNET (a blockchain based AI platform company), and the Chief Scientist of Hanson Robotics, a robotics company that creates the world’s most advanced humanoid robots. Ben also serves as Chairman of the Artificial General Intelligence Society, which hosts the annual AGI research conference series, and the OpenCog Foundation.
Before relocating to Hong Kong in 2011, Dr. Goertzel held executive roles at AI consulting and product development firms in Washington DC (CEO, Chairman and Chief Scientist at Novamente LLC and Biomind LLC) and New York City (CTO at Webmind Inc.). Prior to that, he served as faculty in mathematics at the University of Nevada Las Vegas, in cognitive science as the University of Western Australia, and in computer science at Waikato University in New Zealand, at the City University of New York and at the University of New Mexico in Albuquerque. Dr. Goertzel holds a PhD degree in mathematics from Temple University in Philadelphia, USA.
More information: https://www.ciirc.cvut.cz/
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 consumption: mining 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.
See original article here: https://medium.com/@Francesco_AI/the-convergence-of-ai-and-blockchain-whats-the-deal-60c618e3accc
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.
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
SOL DOMINIQUE I | Dennys W. Sacramento Power Purchase Agreement: https://youtu.be/EMYsIiKZWdk