Four (not entirely) easy steps to General artificial intelligence (AIS)

“For 15 years, since I first introduced the term “artificial General intelligence” (AGI), the area AI has advanced considerably. Today we have driverless cars, automated facial recognition and image capture, machine translation and expert players in the face of AI, and much more,” says Ben Goertzel, CEO decentralized network SingularityNET. Hereinafter in the first person.

However, these achievements essentially remains in the sphere of “narrow AI” — artificial intelligence, which executes the tasks, based on specifically designated data or rules, or in carefully designed learning situations. AI that can work, in General, in case of emergency and to confront the world as Autonomous agents, remain part of the future.

General artificial intelligence (AIS): what is it?

The question remains: what do we need to turn the tools of modern narrow artificial intelligence, which joins the business and society, in the same General artificial intelligence, a dream which futurists and science fiction writers?

Despite the huge diversity of perspectives and lack of lack of technical and conceptual ideas on the way to the OII, among the experts there is nothing like an agreement on this issue.

The varied landscape of proto-AIS

For example, the main founder of Deep Mind of Demis, Hassabis long been a fan of relatively strongly brain inspired approaches to AIS and continues to publish work in this direction. On the other hand, the OpenCog project-oriented AIS, a co-founder which I began in 2008, uses less brain-oriented approach – it includes neural networks, thus powerfully drawing on symbolic and logical representation and probabilistic insights, as well as the evolutionary training program.

What I’m getting at, it’s the fact that once we have several working approaches to manned flight – the planes, helicopters, rockets, etc., can be just as much work approaches to AIS, some of which are largely inspired by biology than others. And, like the Wright brothers, today’s pioneers, they are guided largely by experimentation and intuition, partly because we don’t know yet useful theoretical laws of General intelligence to guide the engineering approach to AIS movement theories; theories, they evolve organically as you practice.

Four (not entirely) easy steps to the AIS

I believe that these four steps are, in principle, attainable for the rest of my life, maybe even in the next 5-10 years. Over each of these steps employs a team of the smartest people in the world, including but in no case not limited to my own teams in SingularityNET, HansonRobotics and OpenCog.

The good news is that we do not need radically new, better hardware, and radically different algorithms or sensors or actuators. We just need to use our computers and algorithms are more reasonable, performing the following steps.

#1. To make cognitive synergy practical

Today we have many powerful algorithms AI, but we don’t use them together in a rather complicated way, so we lose a lot of synergetic intelligence that could emerge from a conscientious of their use. Conversely, the various components of the human brain are configured to work together with interesting feedback and interaction. We need to create a system that will provide a rich and full coordination between the different agents AI at different levels in a single, complex, adaptive network of artificial intelligence.

In the OpenCog architecture, for example, we are trying to implement this by creating different algorithms for learning and reasoning, working together Atomspace Hypergraph that allows you to create hybrid networks of symbolic and podsolnyh segments. Our engine probabilistic logic that processes facts and beliefs, our engine of evolutionary learning program that handles practical knowledge, our deep neural networks that process the perception – they all work together, updating one set of nodes and links of the hypergraph.

On another level, the web AI on the blockchain SingularityNET, we are working on cognitive synergy, allowing different agents AI use different internal algorithms to query each other and exchange information and results. The idea is that a network of agents AI, using a special token for exchange of values can create a common cognitive economy of the minds with intellect of the highest level, which is beyond the intelligence of individual agents. It is a modern implementation of the idea of AI pioneer Marvin Minsky of intelligence as a “society of minds” on the blockchain.

#2. To connect character and positvely AI

I believe that they can most effectively be achieved by the combination of algorithms that use low-level intelligence, such as perception and movement (e.g. deep neural networks), with algorithms that are used to abstract high-level (logical engine).

Deep neural networks have achieved remarkable success in recent years in the treatment of various types of data including images, video, audio, and, to a lesser extent, text. However, it becomes obvious that the immediate architecture of the networks is not very well treated with abstract knowledge.

My own intuition tells me that the shortest way to they will be to use deep neural networks where they are most suited, and make them a hybrid, to give more like methods in AI logical systems, so they can handle the more advanced aspects of humanoid consciousness.

#3. The architecture of the whole organism

People– not only the consciousness of the mind but also the body, the body, therefore, the achievement of human-level AIS will require the inclusion of AI systems in a physical system that can interact with the everyday human world in subtle moments.

“The architecture of the whole organism” (WHOA!) – a lovely phrase, represented by my colleague in the field of robotics David Hanson. Currently we are working with the wonderful robotic creation, “Sofia”, software which I translated into a platform for experimentation with artificial intelligence Open Cog and SingularityNET.

General intelligence requires humanoid body, and the body does not require. However, if we want to create the AIS, which, in particular, shows a human-like knowledge and can understand people, that they would need to have a unique combination of cognition, emotion, socialization, perception and movement, which characterizes human reality. Obviously the best way for they to obtain this kind of feelings reside in the body, which will at least roughly resemble a human.

The need for architecture whole body is connected with the importance of experiential learning for the AIS. In the mind of a human child all kinds of data are mixed in complex ways, and the goals and objectives must fit within the category structure and the dynamics of the world. Even the difference between myself and others need to understand. Ultimately, they will have to hold such kind of training for myself.

Although provide the AIS data from the texts and databases are not so wrong, you need to create a system that will interact with the world, to perceive it and explore autonomously and create its own model of itself and the world. The semantics of all that she learns, therefore, will be based on her own observations. If it is to learn something abstract, like a language or mathematics, she will have to justify the semantics of these disciplines in your own life, and abstraction.

Experiential learning does not require robotics. But the robotics one-piece body provides extremely natural way beyond the modern educational processes, for example, experiential learning AI.

#4. Scalable meta-learning

They must not only learn but also “learn to learn”. OOI will have to apply their own algorithms for reasoning and learning recursively to itself, to automatically improve their own functionality.

Ultimately, the ability to apply learning to improve learning must allow AIS to move far beyond human capabilities. Currently, meta-learning remains difficult, but critical task. In SingularityNET, for example, we start to use OpenCog artificial intelligence for pattern recognition in personal efficiency time to improve their own performance.

In the direction of friendly AIS

If my point of view they hold, as only one of these four aspects will move beyond the current state, we get it — AIS of human level and beyond.

I find this prospect extremely exciting and a little frightening. I also know that some observers, including great men like Stephen Hawking and Elon Musk, expressed exactly the opposite way: more fear than admiration. I think almost everyone who is serious about the development of AIS, puts a lot of effort to mitigate the associated risks.

One of the conclusions to which I came in the course of my work on AI and robotics, is the following: if we want our AIS could absorb and understand human culture and value, the best approach will be to include these AIS in social and emotional contexts with people. I feel that we are doing the right thing in our work with “Sofia” at the Hanson Robotics; in recent experiments, we used “Sophia” as a guide for meditation.

In the last few years I also became interested in the work to ensure that AI has evolved to become egalitarian and distributed throughout the economy in the world, and not settled in large corporations or military governments. Simply put, I would prefer that the supermind has become a friendly, loving AI, not a robot-killer advertising engine or hedge Fund AI. With this motivation I started the project SilgularityNET— to use the AI and the blockchain together and create an open market where anyone on the planet could use the world’s most powerful AI in any order. If they be born of the “economy of consciousnesses” of this nature, he is most likely to have appropriate ethical and inclusive thinking.

We go to unknown territory, not just intellectually and technologically but also socially and philosophically. Let’s do our best to the next stage of our collective journey proceeded wisely and cooperatively, and reasonably exciting.

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