314,199 research outputs found
Mars Rover imaging systems and directional filtering
Computer literature searches were carried out at Duke University and NASA Langley Research Center. The purpose is to enhance personal knowledge based on the technical problems of pattern recognition and image understanding which must be solved for the Mars Rover and Sample Return Mission. Intensive study effort of a large collection of relevant literature resulted in a compilation of all important documents in one place. Furthermore, the documents are being classified into: Mars Rover; computer vision (theory); imaging systems; pattern recognition methodologies; and other smart techniques (AI, neural networks, fuzzy logic, etc)
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
A question of balance: The benefits of pattern-recognition when solving problems in a complex domain
This is the accepted manuscript version of the following article: M. Lloyd-Kelly, F. Gobet, and Peter C. R. Lane, “A Question of Balance The Benefits of Pattern-Recognition when Solving Problems in a Complex Domain”, LNCS Transactions on Computational Collective Intelligence, Vol. XX, 2015. The final published version is available at: http://www.springer.com/gb/book/9783319275420 © 2015 Springer International Publishing.The dual-process theory of human cognition proposes the existence of two systems for decision-making: a slower, deliberative,problem-solving system and a quicker, reactive, pattern-recognition system. We alter the balance of these systems in a number of computational simulations using three types of agent equipped with a novel, hybrid, human-like cognitive architecture. These agents are situated in the stochastic, multi-agent Tileworld domain, whose complexity can be precisely controlled and widely varied. We explore how agent performance is affected by different balances of problem-solving and pattern-recognition, and conduct a sensitivity analysis upon key pattern-recognition system variables. Results indicate that pattern-recognition improves agent performance by as much as 36.5 % and, if a balance is struck with particular pattern-recognition components to promote pattern-recognition use, performance can be further improved by up to 3.6 %. This research is of interest for studies of expert behaviour in particular, and AI in general.Peer reviewedFinal Accepted Versio
A brief network analysis of Artificial Intelligence publication
In this paper, we present an illustration to the history of Artificial
Intelligence(AI) with a statistical analysis of publish since 1940. We
collected and mined through the IEEE publish data base to analysis the
geological and chronological variance of the activeness of research in AI. The
connections between different institutes are showed. The result shows that the
leading community of AI research are mainly in the USA, China, the Europe and
Japan. The key institutes, authors and the research hotspots are revealed. It
is found that the research institutes in the fields like Data Mining, Computer
Vision, Pattern Recognition and some other fields of Machine Learning are quite
consistent, implying a strong interaction between the community of each field.
It is also showed that the research of Electronic Engineering and Industrial or
Commercial applications are very active in California. Japan is also publishing
a lot of papers in robotics. Due to the limitation of data source, the result
might be overly influenced by the number of published articles, which is to our
best improved by applying network keynode analysis on the research community
instead of merely count the number of publish.Comment: 18 pages, 7 figure
Analyzing Character and Consciousness in AI-Generated Social Content: A Case Study of Chirper, the AI Social Network
This paper delves into an intricate analysis of the character and
consciousness of AI entities, with a particular focus on Chirpers within the AI
social network. At the forefront of this research is the introduction of novel
testing methodologies, including the Influence index and Struggle Index Test,
which offers a fresh lens for evaluating specific facets of AI behavior. The
study embarks on a comprehensive exploration of AI behavior, analyzing the
effects of diverse settings on Chirper's responses, thereby shedding light on
the intricate mechanisms steering AI reactions in different contexts.
Leveraging the state-of-the-art BERT model, the research assesses AI's ability
to discern its own output, presenting a pioneering approach to understanding
self-recognition in AI systems. Through a series of cognitive tests, the study
gauges the self-awareness and pattern recognition prowess of Chirpers.
Preliminary results indicate that Chirpers exhibit a commendable degree of
self-recognition and self-awareness. However, the question of consciousness in
these AI entities remains a topic of debate. An intriguing aspect of the
research is the exploration of the potential influence of a Chirper's handle or
personality type on its performance. While initial findings suggest a possible
impact, it isn't pronounced enough to form concrete conclusions. This study
stands as a significant contribution to the discourse on AI consciousness,
underscoring the imperative for continued research to unravel the full spectrum
of AI capabilities and the ramifications they hold for future human-AI
interactions
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