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 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.

2. General intelligence as the Holy Grail

2018 ◽  
Author(s):  
Margaret A. Boden
Keyword(s):  
Machine Learning ◽  
Knowledge Representation ◽  
Distributed Cognition ◽  
State Of The Art ◽  
General Intelligence ◽  
Frame Problem ◽  
Holy Grail ◽  
Artificial General Intelligence ◽  
The Frame Problem ◽  
Forms Of Knowledge

A host of state-of-the-art AI applications exist, designed for countless specific tasks and used in almost every area of life, by laymen and professionals alike. Many outperform even the most expert humans. In that sense, progress has been spectacular. But the AI pioneers were also hoping for systems with general intelligence. ‘General intelligence as the Holy Grail’ explains why artificial general intelligence is still highly elusive despite recent increases in computer power. It considers the general AI strategies in recent research—heuristics, planning, mathematical simplification, and different forms of knowledge representation—and discusses the concepts of the frame problem, agents and distributed cognition, machine learning, and generalist systems.

scholarly journals MODERN STAGE OF ARTIFICIAL INTELLIGENCE (AI) DEVELOPMENT AND APPLICATION OF AI METHODS AND SYSTEMS IN POWER ENGINEERING

2022 ◽  
pp. 5-20
Author(s):  
Людмила Васильевна Массель
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Round Table ◽  
Energy Sector ◽  
Power Engineering ◽  
General Intelligence ◽  
Digital Twin ◽  
Explainable Ai ◽  
Number Of Publications ◽  
Knowledge Ontology

В статье анализируется ряд публикаций на эту тему, а также обобщаются результаты дискуссий на конференции «Знания, онтологии, теории» (Новосибирск, 8-12 ноября 2021 г.) и Круглом столе в ИСЭМ СО РАН «Искусственный интеллект в энергетике» (22 декабря 2021 г.). Рассматриваются понятия: сильный и слабый ИИ, объяснимый ИИ, доверенный ИИ. Анализируются причины «бума» вокруг машинного обучения и его недостатки. Сравниваются облачные технологии и технологии граничных вычислений. Определяется понятие «умный» цифровой двойник, интегрирующий математические, информационные, онтологические модели и технологии ИИ. Рассматриваются этические риски ИИ и перспективы применения методов и технологий ИИ в энергетике. The article analyzes a number of publications on this topic, and also summarizes the results of discussions at the conference "Knowledge, Ontology, Theory" (Novosibirsk, November 8-12, 2021) and the Round Table at the ISEM SB RAS "Artificial Intelligence in Energy" (December 22 2021). The concepts are considered: artificial general intelligence (AGI), strong and narrow AI (NAI), explainable AI, trustworthy AI. The reasons for the "hype" around machine learning and its disadvantages are analyzed. Compares cloud and edge computing technologies. The concept of "smart" digital twin, which integrates mathematical, informational, ontological models and AI technologies, is defined. The ethical risks of AI and the prospects for the application of AI methods and technologies in the energy sector are considered.

scholarly journals Essential Features in a Theory of Context for Enabling Artificial General Intelligence

2021 ◽  
Vol 11 (24) ◽  
pp. 11991
Author(s):  
Mayank Kejriwal
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Natural Language ◽  
Language Processing ◽  
Representation Learning ◽  
Machine Intelligence ◽  
General Intelligence ◽  
Commonsense Reasoning ◽  
Core Concept

Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI.

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

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 Machine intelligence today: applications, methodology, and technology

2021 ◽  
Vol 44 (2) ◽  
pp. 104-114
Author(s):  
Bernhard G. Humm ◽  
Hermann Bense ◽  
Michael Fuchs ◽  
Benjamin Gernhardt ◽  
Matthias Hemmje ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Best Practices ◽  
Machine Intelligence ◽  
Strong Impact ◽  
Industrial Manufacturing ◽  
Knowledge Based ◽  
Current Trends ◽  
Culture Education

AbstractMachine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning.

PD67 Strengthening And Accelerating Health Technology Assessments Through Artificial Intelligence

2018 ◽  
Vol 34 (S1) ◽  
pp. 154-155
Author(s):  
Randy Goebel ◽  
Mi-Young Kim ◽  
Egon Jonsson ◽  
Ulli Wolfaardt
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Performance ◽  
Data Extraction ◽  
Primary Source ◽  
Research Synthesis ◽  
Health Technology ◽  
Machine Intelligence ◽  
Joint Project ◽  
Synthesis Tool

Introduction:Rising costs and the rapidly increasing volume of findings from research in health care are driving the demand for comprehensive information to inform the allocation of resources. Health technology assessment (HTA) applies rigorous processes to provide high-quality synthesized information to policymakers and healthcare payers. HTA involves combining large amounts of research publications to systematically evaluate the properties, effects, and impacts on a topic of interest.Methods:The time and resources required to complete a full HTA are often demanding. There is an opportunity to apply high-performance computing (inclusive of artificial intelligence and machine learning disciplines) to HTA. This project applied high-computing technology to create a research synthesis tool to support HTA and then developed a service that integrates as much relevant data as possible to strengthen HTA. This was a joint project that combined expertise from the areas of health technology, machine learning, information technology, and innovation.Results:The information gathered for this phased project from HTA subject matter experts and other stakeholders was collated to inform a research synthesis tool and a broader concept of the project.Conclusions:The results of this study will inform the design of a research synthesis tool that covers the entire HTA process (literature search, screening titles and abstracts, data extraction, quality assessment, and analysis). The collaborators included Alberta Innovates, the Alberta Machine Intelligence Institute, the University of Alberta, Cybera, and PolicyWise. Alberta Innovates, which is an accelerator and innovator of research in the province of Alberta, Canada, was the primary source of funding for this project.

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