174,286 research outputs found
Iris Codes Classification Using Discriminant and Witness Directions
The main topic discussed in this paper is how to use intelligence for
biometric decision defuzzification. A neural training model is proposed and
tested here as a possible solution for dealing with natural fuzzification that
appears between the intra- and inter-class distribution of scores computed
during iris recognition tests. It is shown here that the use of proposed neural
network support leads to an improvement in the artificial perception of the
separation between the intra- and inter-class score distributions by moving
them away from each other.Comment: 6 pages, 5 figures, Proc. 5th IEEE Int. Symp. on Computational
Intelligence and Intelligent Informatics (Floriana, Malta, September 15-17),
ISBN: 978-1-4577-1861-8 (electronic), 978-1-4577-1860-1 (print
Long-Term Image Boundary Prediction
Boundary estimation in images and videos has been a very active topic of
research, and organizing visual information into boundaries and segments is
believed to be a corner stone of visual perception. While prior work has
focused on estimating boundaries for observed frames, our work aims at
predicting boundaries of future unobserved frames. This requires our model to
learn about the fate of boundaries and corresponding motion patterns --
including a notion of "intuitive physics". We experiment on natural video
sequences along with synthetic sequences with deterministic physics-based and
agent-based motions. While not being our primary goal, we also show that fusion
of RGB and boundary prediction leads to improved RGB predictions.Comment: Accepted in the AAAI Conference for Artificial Intelligence, 201
Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management
Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence
Introduction to the Minitrack on Artificial Intelligence-based Assistants
Artificial Intelligence (AI) has received much attention due to the recent progress in several technological areas such as image detection, translation, and decision support. Established businesses and many start-up businesses are eagerly discussing how they can gain a competitive advantage from complementing their products, services and processes with AI. In fact, based on the research in the AI domain since several decades, a broad variety of promising application fields were suggested where AI might add business value. Meanwhile, applications are not limited to simple structured problems, but even applications higher complexities are feasible, which require higher levels of “intelligence”. To avoid discussions on the ambivalent notion of “intelligence”, it shall refer to tasks involving perception, processing, action and learning. Many applications are possible along these activities, in particular a user’s interaction via natural language
The SP theory of intelligence: benefits and applications
This article describes existing and expected benefits of the "SP theory of
intelligence", and some potential applications. The theory aims to simplify and
integrate ideas across artificial intelligence, mainstream computing, and human
perception and cognition, with information compression as a unifying theme. It
combines conceptual simplicity with descriptive and explanatory power across
several areas of computing and cognition. In the "SP machine" -- an expression
of the SP theory which is currently realized in the form of a computer model --
there is potential for an overall simplification of computing systems,
including software. The SP theory promises deeper insights and better solutions
in several areas of application including, most notably, unsupervised learning,
natural language processing, autonomous robots, computer vision, intelligent
databases, software engineering, information compression, medical diagnosis and
big data. There is also potential in areas such as the semantic web,
bioinformatics, structuring of documents, the detection of computer viruses,
data fusion, new kinds of computer, and the development of scientific theories.
The theory promises seamless integration of structures and functions within and
between different areas of application. The potential value, worldwide, of
these benefits and applications is at least $190 billion each year. Further
development would be facilitated by the creation of a high-parallel,
open-source version of the SP machine, available to researchers everywhere.Comment: arXiv admin note: substantial text overlap with arXiv:1212.022
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