What is AGI artificial general intelligence?

Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and future studies.

There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.

  • Reactive machines. …
  • Limited memory. …
  • Theory of mind. …
  • Self-awareness.

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. … For most of its history, AI research has been divided into subfields that often fail to communicate with each other.

The basic elements of artificial intelligence are…

  • Problem Solving mostly deals with constraint satisfaction problems, Solve by Search.
  • Certain knowledge representation, reasoning of it and plan of execution. First order logic is dealt here.
  • Uncertain knowledge representation and its reasoning. …
  • Feature Learning. …
  • Perception, communication and action taken by agents.

Major sub-fields of AI now include: Machine Learning, Neural Networks, Evolutionary Computation, Vision, Robotics, Expert Systems, Speech Processing, Natural Language Processing, and Planning.

A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. … Some argue that advances in artificial intelligence (AI) will probably result in general reasoning systems that lack human cognitive limitations.

The branches of artificial intelligence

  • Computational creativity –
  • Machine learning. Neural networks – Hybrid neural network – …
  • Fuzzy systems –
  • Evolutionary computation, including: Evolutionary algorithms – Genetic algorithm – …
  • Probabilistic methods including: Bayesian network. Hidden Markov model. …
  • Chaos theory.

Google’s artificial intelligence (AI) is much smarter than Apple’s Siri, according to a report from three Chinese researchers. … But while Google’s AI leads the tech pack, it has a long way to go before it comes close to human intelligence; the average 6-year-old has an IQ of 55.5, according to the report.

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion.

There is Artificial intelligence, and there is Artificial general intelligence (AGI). The latter is a branch of the former. Microsoft Cortana is definitely an AI application, like all Natural language processing applications, but it’s not an AGI application, nor it aspires to be.

The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. … Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.

In addition, deep learning is used to detect pedestrians, which helps decrease accidents. Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.

Java, Python, Lisp, Prolog, and C++ are major AI programming language used for artificial intelligence capable of satisfying different needs in development and designing of different software.

LISP is another language used for artificial intelligence development. … LISP, unlike most AI programming languages, is more efficient in solving specific as it adapts to the needs of the solutions a developer is writing. It is highly suitable in inductive logic projects and machine learning.

 

SingularityNET Vision | 1.3 A Robust and Adaptive Software Architecture

In computer science terms, SingularityNET is essentially a distributed computing architecture or making new kinds of smart contracts to facilitate market interactions with AI and machine learning tools. The following design principles are incorporated throughout the design:

  • Interoperability: The network will be able to interface with multiple
    blockchains.
  • Data Sovereignty and Privacy: User data control and sharing comes
    with privacy-enabled controls on top of the network, and access is validated
    through smart contracts and the blockchain.
  • Modularity: Flexible network capabilities make it possible to create custom topologies, AI Agent collaboration arrangements, and failure recovery methods.
  • Scalability: SingularityNET will securely host both private and public
    contracts, so more scalable and resilient applications can be built on top
    of it with near zero transaction costs.

SingularityNET Agents can run in the cloud, on phones, robots, or other embedded devices. Via close collaboration with co-founding firm Hanson Robotics, SingularityNET is designed to foster the development of multiple species of robots as the next-generation interface for delivering AI services and applications, and fostering the emergence of global Artificial General Intelligence.

#WellerAGI

SingularityNet Whitepaper

SingularityNET Vision | 1.2 Acute Market Needs Addressed

SingularityNET meets an acute and accelerating market need. In the current economic and technological context, every business needs AI, but off-the-shelf AIs will rarely match a business’s needs. Only tech giants can hire armies of developers to build custom AIs, and even they have a hard time hiring enough AI experts to meet demand. SingularityNET provides an automated process enabling each business to connect existing AI tools together to build the solution it needs. By providing an easy means of configuring tools, it offers both customization and availability, while reducing the reduplication of effort involved in proprietary development, making the development process more efficient.

Many state of the art AI tools exist only in GitHub repositories created by grad students or independent researchers. This puts them out of the reach of anyone without the skills to install, configure, and run them. Most AI developers are academics, not business people, and have no easily-accessible marketplace to monetize their clever AI code.

In addition to their clever code, machine learning tools require datasets of sufficient size. Creating and managing such large datasets is beyond the means and capabilities of most AI developers, and the closed development model that currently prevails makes it hard for developers to share datasets.

SingularityNET launches these AI tools and datasets onto the marketplace, making them more accessible to end-users and developers, and giving developers a way to monetize their creations.

It is a sharing-economy marketplace for AI, that encourages collaboration between these tools and decentralized sharing of information, democratizing access to the benefits of AI. In accordance with these goals, SingularityNET will be an open network. Anyone can insert an AI Agent as long as the Agent shares information according to the SingularityNET API, and accepts/disburses payment according to SingularityNET’s economic logic. New AI Agents will come from AI software developers who want access to SingularityNET’s market, which will be the hub of open AI services.

We have a situation similar to the ones that spawned the creation of Uber and AirBnB: there is a large unexploited resource, a large market in need of that resource, and we are launching the tool to connect the two. The unexploited resource is AI algorithms and software existing on GitHub and elsewhere, and n need of this resource is the 99% of businesses that can’t afford its own team of AI experts.

SingularityNET Vision | 1.1 Inspiration

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

SingularityNet Whitepaper

I Am Now | A decentralized, open market and inter-network for AIs

December 19, 2017

Abstract

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

SingularityNet Whitepaper

 

SingularityNET Announcements | Invitation: Ben Goertzel: From Here to Human-Level AGI in 4 Simple Steps

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/pozvanka-21-5-2018-ben-goertzel/

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.