scholarly journals AI and the Singularity: A Fallacy or a Great Opportunity?

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 73 ◽  
Author(s):  
Adriana Braga ◽  
Robert Logan
Keyword(s):  
Artificial Intelligence ◽  
Wishful Thinking ◽  
Human Intelligence ◽  
General Intelligence ◽  
Great Opportunity ◽  
Artificial General Intelligence

We address the question of whether AI, and in particular the Singularity—the notion that AI-based computers can exceed human intelligence—is a fallacy or a great opportunity. We have invited a group of scholars to address this question, whose positions on the Singularity range from advocates to skeptics. No conclusion can be reached as the development of artificial intelligence is still in its infancy, and there is much wishful thinking and imagination in this issue rather than trustworthy data. The reader will find a cogent summary of the issues faced by researchers who are working to develop the field of artificial intelligence and in particular artificial general intelligence. The only conclusion that can be reached is that there exists a variety of well-argued positions as to where AI research is headed.

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 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

The role of artificial intelligence in robotics

2014 ◽  
Vol 41 (2) ◽  
pp. 119-123 ◽  
Author(s):  
Robert Bogue
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Human Intelligence ◽  
General Intelligence ◽  
Rapid Progress ◽  
Significant Progress ◽  
Development Activity ◽  
Content Type ◽  
Insight Into

Purpose – This paper aims to provide an insight into the use of artificial intelligence (AI) in robotics. Design/methodology/approach – Following an introduction to AI, this paper provides an overview of the application of AI to robotics. Mobile robots are then discussed, together with the various AI techniques employed and under development. The application of the OpenCog artificial general intelligence architecture is then considered and the paper concludes with a brief discussion. Findings – This shows that many AI concepts are being applied to humanoid, mobile and other classes of robots. Significant progress has been made and many innovative AI strategies are being studied which often seek to emulate aspects of human intelligence. Much development activity is being driven by military interests but as yet, the level of intelligence exhibited by the most advanced robots is at best equivalent to that of a very young child. Several academics argue that more rapid progress will arise from a closer integration of AI and robotic research. Originality/value – This article discusses the role of AI in robotics and provides details of number of robotic developments involving a range of AI concepts.

scholarly journals The Case for General Collective Intelligence Rather than Artificial General Intelligence being the Most Important Human Innovation in the History and Future of Mankind

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Economic Impact ◽  
Collective Intelligence ◽  
Human Intelligence ◽  
General Intelligence ◽  
Human Beings ◽  
Artificial General Intelligence ◽  
Proposed Model ◽  
History Of ◽  
Hidden Patterns ◽  
Entire Planet

Artificial General Intelligence, that is an Artificial Intelligence with the ability to redesign itself and other technology on its own, has been called “mankind’s last invention”, since it may not only remove the necessity of any human invention afterwards, but also might design solutions far too complex for human beings to have the ability to contribute to in any case. Because of this, if and when AGI is ever invented, it has been argued by many that it will be the most important innovation in the history of the mankind up to that point. Just as nature’s invention of human intelligence might have transformed the entire planet and generated a greater economic impact than any other innovation in the history of the planet, AGI has been suggested to have the potential for an economic impact larger than that resulting from any other innovation in the history of mankind. This paper explores the case for General Collective Intelligence being a far more important innovation than AGI. General Collective Intelligence has been defined as a solution with the capacity to organize groups of human or artificial intelligences into a single collective intelligence with vastly greater general problem solving ability. A recently proposed model of GCI not only outlines a model for cognition that might also enable AGI, but also identifies hidden patterns in collective outcomes for groups that might make GCI necessary in order to reliably achieve the benefits of AGI while reliably avoiding the potentially catastrophic costs of AGI.

Attention, Consciousness, and Linguistic Cooperation with AI

2021 ◽  
pp. 1-17
Author(s):  
Carlos Montemayor
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Problem Solving ◽  
Joint Attention ◽  
Phenomenal Consciousness ◽  
General Intelligence ◽  
Collective Rationality ◽  
Satisfactory Solution ◽  
Epistemic Agency ◽  
Artificial General Intelligence

Contemporary debates on Artificial General Intelligence (AGI) center on what philosophers classify as descriptive issues. These issues concern the architecture and style of information processing required for multiple kinds of optimal problem-solving. This paper focuses on two topics that are central to developing AGI regarding normative, rather than descriptive, requirements for AGIs epistemic agency and responsibility. The first is that a collective kind of epistemic agency may be the best way to model AGI. This collective approach is possible only if solipsistic considerations concerning phenomenal consciousness are ignored, thereby focusing on the cognitive foundation that attention and access consciousness provide for collective rationality and intelligence. The second is that joint attention and motivation are essential for AGI in the context of linguistic artificial intelligence. Focusing on GPT-3, this paper argues that without a satisfactory solution to this second normative issue regarding joint attention and motivation, there cannot be genuine AGI, particularly in conversational settings.

scholarly journals What to expect when you’re expecting robots: Futures, expectations, and pseudo-artificial general intelligence in UK news

Journalism ◽  
2020 ◽  
pp. 146488492094753
Author(s):  
J Scott Brennen ◽  
Philip N Howard ◽  
Rasmus K Nielsen
Keyword(s):  
Artificial Intelligence ◽  
Social Integration ◽  
Critical Discourse Analysis ◽  
News Media ◽  
Critical Discourse ◽  
General Intelligence ◽  
Journalism Studies ◽  
Artificial General Intelligence ◽  
Sociology Of Expectations ◽  
The World

Drawing on scholarship in journalism studies and the sociology of expectations, this article demonstrates how news media shape, mediate, and amplify expectations surrounding artificial intelligence in ways that influence their potential to intervene in the world. Through a critical discourse analysis of news content, this article describes and interrogates the persistent expectation concerning the widescale social integration of AI-related approaches and technologies. In doing so, it identifies two techniques through which news outlets mediate future-oriented expectations surrounding AI: choosing sources and offering comparisons. Finally, it demonstrates how in employing these techniques, outlets construct the expectation of a pseudo-artificial general intelligence: a collective of technologies capable of solving nearly any problem.

scholarly journals Defining Functional Models of Artificial Intelligence Solutions to Create a Library that an Artificial General Intelligence can use to Increase General Problem Solving Ability

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Large Scale ◽  
Building Blocks ◽  
General Intelligence ◽  
Functional Decomposition ◽  
Artificial General Intelligence ◽  
Functional Models ◽  
The Impact

The AI industry continues to enjoy robust growth. With the growing number of AI algorithms, the question becomes how to leverage all these models intelligently in a way that reliably converges on AGI. One approach is to gather all these models ingo a single library that a system of artificial intelligence might use to increase it's general problem solving ability. This paper explores the requirements for building such a library, the requirements for that library to be searchable for AI algorithms that might have the capacity to significantly increase impact on any given problem, and the requirements for the use of that library to reliably converge on AGI. This paper also explores the importance to such an effort of defining a common set of semantic functional building blocks that AI models can be represented in terms of. In particular, how that functional decomposition might be used to organize large scale cooperation to create such an AI library, where that cooperation has not yet proved possible otherwise. And how such collaboration, as well as how such a library, might significantly increase the impact of each AI and AGI researcher’s work.

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