The Task of the Creation of Artificial General Intelligence and the Problem of Consciousness

2021 ◽  
Vol 64 (1) ◽  
pp. 13-44
Author(s):  
David I. Dubrovsky
Keyword(s):  
Artificial Intelligence ◽  
Practical Implementation ◽  
Successful Implementation ◽  
General Intelligence ◽  
Cognitive Architectures ◽  
Subjective Reality ◽  
Intelligence System ◽  
Artificial General Intelligence ◽  
Leading Role ◽  
Wide Range

The article discusses the task of creation of Artificial General Intelligence (AGI), that is, an artificial intelligence system that approaches the functional capabilities of natural intelligence. Emphasis is made on the leading role of Sberbank in updating and organizing a special research program in order to develop this problem, which is of strategic importance for Russia. It has been established that the successful implementation of this program presupposes solving fundamental methodological issues that require input of philosophers - specialists in the field of epistemology and methodology of science. We show the relations of the concepts of “artificial” and “natural,” “strong” and “weak,” “general” and “narrow” intelligence. The article reveals the theoretical difficulties associated with a clear definition of the properties of general intelligence and the ways of its practical implementation. The author draws attention to the importance of studies of consciousness for the development of general artificial intelligence and comes to the conclusion that the priority issues are using the results of the phenomenological analysis of subjective reality, its value-semantic and op-erational structures. The paper discusses these issues in detail, as they are of direct relevance to the construction of new cognitive architectures. The latter make it possible, to go beyond the limits of “narrow” artificial intelligence and create AGI with a high degree of autonomy and independent solutions to a wide range of problems in different environments. It demonstrates the limitation of Turing's operationalist methodology that excludes the use of the results of special studies of consciousness as a subjective reality. Application of such results is associated with the development of post-Turing methodology, which opens up significant opportunities for creating an AGI.

Game AGI beyond Characters

2016 ◽  
pp. 266-293 ◽  
Author(s):  
Alexander Zook
Keyword(s):  
Artificial Intelligence ◽  
Ad Hoc ◽  
General Intelligence ◽  
Game Playing ◽  
Cognitive Architectures ◽  
Artificial General Intelligence ◽  
Player Experiences

Artificial General Intelligence has traditionally used games as a testbed to develop domain-agnostic game playing techniques. Yet games are about more than winning. This chapter reviews recent efforts that have broadened the ways Artificial Intelligence (AI) is used in games, covering: modeling and managing player experiences, creating novel game structures based in interacting with AI, and enabling AI agents to make games. Many of the techniques used to address these challenges have been ad hoc approaches to solving specific problems. This chapter discusses open challenges in each of these areas and the potential for cognitive architectures to provide unified techniques that address these challenges.

scholarly journals Artificial Intelligence and the Limitations of Information

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 332 ◽  
Author(s):  
Paul Walton
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Processing ◽  
General Intelligence ◽  
Selection Pressures ◽  
Artificial General Intelligence ◽  
Relationship Of ◽  
The Relationship ◽  
The Way

Artificial intelligence (AI) and machine learning promise to make major changes to the relationship of people and organizations with technology and information. However, as with any form of information processing, they are subject to the limitations of information linked to the way in which information evolves in information ecosystems. These limitations are caused by the combinatorial challenges associated with information processing, and by the tradeoffs driven by selection pressures. Analysis of the limitations explains some current difficulties with AI and machine learning and identifies the principles required to resolve the limitations when implementing AI and machine learning in organizations. Applying the same type of analysis to artificial general intelligence (AGI) highlights some key theoretical difficulties and gives some indications about the challenges of resolving them.

scholarly journals Deliberative Agents in Dynamic Environments, Using Jason and NARS

2021 ◽  
Author(s):  
valeria seidita ◽  
francesco lanza ◽  
Patrick Hammer ◽  
Antonio Chella ◽  
Pei Wang
Keyword(s):  
Dynamic Environments ◽  
General Intelligence ◽  
Multi Agent Systems ◽  
Bdi Agents ◽  
Agent Systems ◽  
Intelligence System ◽  
Artificial General Intelligence ◽  
Reasoning System ◽  
Multi Agent ◽  
High Level

This work explore the possibility to combine the Jason reasoning cycle with a Non-Axiomatic Reasoning System (NARS) to develop multi-agent systems that are able to reason, deliberate and plan when information about plans to be executed and goals to be pursued is missing or incomplete. The contribution of this work is a method for BDI agents to create high-level plans using an AGI (Artificial General Intelligence) system based on non-axiomatic logic.

scholarly journals General Problem-Solving Ability in Natural Systems as a Model for Solving Problems from Sustainable and Self-Financing Development in Africa to Artificial General Intelligence

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Sustainable Development ◽  
Problem Solving ◽  
General Problem ◽  
Tree Seedlings ◽  
General Intelligence ◽  
The Sustainable Development ◽  
Natural Systems ◽  
Artificial General Intelligence ◽  
Single Leaf ◽  
Wide Range

Natural systems have demonstrated the ability to solve a wide range of adaptive problems as well as the ability to self-assemble in a self-sustaining way that enables them to exponentially increase impact on outcomes related to those problems. In the case of photosynthesis nature solved the problem of harnessing the energy in sunlight and then leveraged self-assembling and self-sustaining processes so that exponentially increasing impact on that problem is reliably achievable. Rather than having to budget a given amount of resources to create a mature tree, where those resources might not be reliably available, tree seedlings self-assemble in a self-sustaining way from very few resources to grow from having the capability of photosynthesis accompanying a single leaf, to the capability of photosynthesis accompanying what might be millions of leaves. If the patterns underlying this adaptive problem-solving could be abstracted so that they are generally applicable, they might be applied to social and other problems occurring at scales that currently are not reliably solvable. One is the Sustainable Development Goals (SDGs) funding gap. The funding believed to be required to address the SDGs is difficult to estimate, and may be anywhere between $2 trillion and $6 trillion USD per year. However, bridging the gap between the funding required to meet these goals and the funding available to do so is universally acknowledged to be a difficult and unsolved problem. This paper explores how abstracting the pattern for general problem-solving ability that nature has used to solve the problem of exponentially increasing impact on collective problems, and that nature has proven to be effective for billions of years, might be reused to solve “wicked problems” from implementing an Artificial General Intelligence (AGI) to funding sustainable development at the scale required to transform Africa and the world.

scholarly journals MACHINE INTELLIGENCE. ESSAYS ON THE THEORY OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

2019 ◽  
Author(s):  
Сергей Шумский ◽  
Sergey Shumskiy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Revolution ◽  
Machine Intelligence ◽  
General Intelligence ◽  
New Science ◽  
Artificial General Intelligence ◽  
Science Of Mind ◽  
Artificial Brains

This book is about the nature of mind, both human and artificial, from the standpoint of the theory of machine learning. It addresses the problem of creating artificial general intelligence. The author shows how one can use the basic mechanisms of our brain to create artificial brains of future robots. How will this ever-stronger artificial intelligence fit into our lives? What awaits us in the next 10-15 years? How can someone who wants to take part in a new scientific revolution, participate in developing a new science of mind?

scholarly journals Conceptual Commitments of the LIDA Model of Cognition

2013 ◽  
Vol 4 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Stan Franklin ◽  
Steve Strain ◽  
Ryan McCall ◽  
Bernard Baars
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Cognitive Functions ◽  
Current Knowledge ◽  
Action Selection ◽  
Theoretical Framework ◽  
General Intelligence ◽  
Artificial General Intelligence ◽  
Level Model ◽  
The Mind

Abstract Significant debate on fundamental issues remains in the subfields of cognitive science, including perception, memory, attention, action selection, learning, and others. Psychology, neuroscience, and artificial intelligence each contribute alternative and sometimes conflicting perspectives on the supervening problem of artificial general intelligence (AGI). Current efforts toward a broad-based, systems-level model of minds cannot await theoretical convergence in each of the relevant subfields. Such work therefore requires the formulation of tentative hypotheses, based on current knowledge, that serve to connect cognitive functions into a theoretical framework for the study of the mind. We term such hypotheses “conceptual commitments” and describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model. Our intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents.

STATE POLICY OF ARTIFICIAL INTELLIGENCE DEVELOPMENT IN RUSSIA: AN ANALYSIS OF STRATEGIC GOALS

2020 ◽  
Vol 26 (8) ◽  
pp. 69-76
Author(s):  
V. Blanutsa ◽  
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
State Policy ◽  
Time Lag ◽  
National Strategy ◽  
General Intelligence ◽  
Artificial General Intelligence ◽  
Strategic Goals ◽  
The Creation ◽  
Intelligence Development

The state policy of artificial intelligence development in Russia is based on the national strategy approved in 2019 and valid until 2030. To understand the specifics of Russian policy, a national strategy was chosen as the object of research, and the subject of research was declared and latent strategic goals. The study is aimed at assessing the degree of correspondence between the strategic goals of state policy and modern concepts of artificial intelligence development. For the automatic analysis of the texts of the national strategy, similar foreign documents and the global array of publications, content analysis was used. The eight largest bibliographic databases have identified many original scientific articles on artificial intelligence. Content analysis of this array made it possible to identify six approaches (algorithmic, test, cognitive, landscape, explanatory and heuristic) to the construction of a concept for the development of artificial intelligence. The latter approach is the most end-to-end, allowing generalizing the rest of the approaches. Further analysis was carried out on the basis of a heuristic approach, within which the concepts of narrow, general and super intelligence are highlighted. The text of the national strategy was analyzed for compliance with the three concepts. It was found that the goals announced in the national strategy refer to the concept of artificial narrow intelligence. Analysis of the frequency of occurrence of terms in the strategy revealed latent goals (access to big data and software) that belong to the same concept. The study of the context of several cases of mentioning artificial general intelligence in the strategy only confirmed the general focus on the development of artificial narrow intelligence. The leading countries in the analyzed area are characterized by a strategic focus on the development of technologies for artificial general intelligence and scientific research on artificial superintelligence. The approximate time lag of the Russian strategy from the creation of artificial general intelligence has been determined. To overcome this lag and Russia occupy a leading position in the world, it was proposed to develop a new national strategy for the creation of artificial superintelligence technologies in the period up to 2050

Sign in / Sign up

Export Citation Format

Share Document