Steering the mind share: technology companies, policy and Artificial Intelligence research in universities

2021 ◽  
pp. 1-13
Author(s):  
Kalervo N. Gulson ◽  
P. Taylor Webb
Keyword(s):  
Artificial Intelligence ◽  
Artificial Intelligence Research ◽  
The Mind

scholarly journals The Future of Intelligence: The Central Meaning-Making Unit of Intelligence in the Mind, the Brain, and Artificial Intelligence

2021 ◽  
Author(s):  
Andreas Demetriou ◽  
hudson golino ◽  
George Charilaos Spanoudis ◽  
Nikolaos Makris ◽  
Samuel Greiff
Keyword(s):  
Artificial Intelligence ◽  
Meaning Making ◽  
Mental Content ◽  
Future Research ◽  
General Intelligence ◽  
Artificial Intelligence Research ◽  
Central Meaning ◽  
The Mind ◽  
Core Meaning ◽  
The Brain

This paper focuses on general intelligence, g. We first point to broadly accepted facts about g: it is robust, reliable, and sensitive to learning. We then summarize conflicting theories about its nature and development (Mutualism, Process Overlap Theory, and Dynamic Mental Field Theory) and suggest how future research may resolve their disputes. A model is proposed for g involving a core meaning-making mechanism, noetron, drawing on Alignment, Abstraction, and Cognizance, perpetually generating new mental content. Noetron develops through several levels of control: episodic attentional inferential truth epistemic control in infancy, preschool, childhood, adolescence, and adulthood, respectively. Finally, we propose an agenda for future brain, assuming a brain noetron, and artificial intelligence research, assuming an artificial noetron, that might uncover the underlying brain mechanisms of g and generate artificial general intelligence.

scholarly journals The Future of Intelligence: The Central Meaning-Making Unit of Intelligence in the Mind, the Brain, and Artificial Intelligence

2021 ◽  
Author(s):  
Andreas Demetriou ◽  
hudson golino ◽  
Hudson Golino
Keyword(s):  
Artificial Intelligence ◽  
Meaning Making ◽  
Mental Content ◽  
Future Research ◽  
General Intelligence ◽  
Artificial Intelligence Research ◽  
Central Meaning ◽  
The Mind ◽  
Core Meaning ◽  
The Brain

This paper focuses on general intelligence, g. We first point to broadly accepted facts about g: it is robust, reliable, and sensitive to learning. We then summarize conflicting theories about its nature and development (Mutualism, Process Overlap Theory, and Dynamic Mental Field Theory) and suggest how future research may resolve their disputes. A model is proposed for g involving a core meaning-making mechanism, noetron, drawing on Alignment, Abstraction, and Cognizance, perpetually generating new mental content. Noetron develops through several levels of control: episodic attentional inferential truth epistemic control in infancy, preschool, childhood, adolescence, and adulthood, respectively. Finally, we propose an agenda for future brain, assuming a brain noetron, and artificial intelligence research, assuming an artificial noetron, that might uncover the underlying brain mechanisms of g and generate artificial general intelligence.

scholarly journals RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Elin Trägårdh ◽  
Pablo Borrelli ◽  
Reza Kaboteh ◽  
Tony Gillberg ◽  
Johannes Ulén ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Medicine ◽  
Artificial Intelligence Research

Analysis of the Status Quo of Artificial Intelligence Research in Shandong Province, China

2019 ◽  
Author(s):  
Mingjun Zhang ◽  
Statyvka Yurii Ivanovych ◽  
Shiwei Zhu ◽  
Junfeng Yu ◽  
Beibei Xu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Status Quo ◽  
Shandong Province ◽  
Artificial Intelligence Research ◽  
The Status

scholarly journals Strategy selection as rational metareasoning

2016 ◽  
Author(s):  
Falk Lieder ◽  
Tom Griffiths
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cost Benefit ◽  
Human Reasoning ◽  
Strategy Selection ◽  
Heuristics And Biases ◽  
Rational Model ◽  
The Mind ◽  
Over Time ◽  
Human Rationality

Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy’s performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decision-making to problem-solving and arithmetic by capturing the variability of people’s strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and four new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the two poles of the debate about human rationality by integrating heuristics and biases with learning and rationality.

scholarly journals A checklist to protect human rights in artificial-intelligence research

Nature ◽  
2017 ◽  
Vol 552 (7685) ◽  
pp. 334-334
Author(s):  
Sumathy Ramesh
Keyword(s):  
Artificial Intelligence ◽  
Human Rights ◽  
Artificial Intelligence Research
Sign in / Sign up

Export Citation Format

Share Document