scholarly journals What can computational models learn from human selective attention? A review from an audiovisual unimodal and crossmodal perspective

2019 ◽  
Author(s):  
Di Fu ◽  
Cornelius Weber ◽  
Guochun Yang ◽  
Matthias Kerzel ◽  
Weizhi Nan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Selective Attention ◽  
Computational Modeling ◽  
Cognitive Neuroscience ◽  
Real World ◽  
Information Acquisition ◽  
Essential Role ◽  
Computational Models ◽  
The Past ◽  
Shed Light

Selective attention plays an essential role in information acquisition and utilizationfrom the environment. In the past 50 years, research on selective attention has beena central topic in cognitive science. Compared with unimodal studies, crossmodalstudies are more complex but necessary to solve real-world challenges in both humanexperiments and computational modeling. Although an increasing number of findingson crossmodal selective attention have shed light on humans’ behavioral patterns andneural underpinnings, a much better understanding is still necessary to yield the samebenefit for intelligent computational agents. This article reviews studies of selectiveattention in unimodal visual and auditory and crossmodal audiovisual setups from themultidisciplinary perspectives of psychology and cognitive neuroscience, and evaluatesdifferent ways to simulate analogous mechanisms in computational models and robotics.We discuss the gaps between these fields in this interdisciplinary review and provideinsights about how to use psychological findings and theories in artificial intelligence fromdifferent perspectives.

scholarly journals What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective

2019 ◽  
Author(s):  
Di Fu ◽  
Cornelius Weber ◽  
Yang Guochun ◽  
Matthias Kerzel ◽  
Weizhi Nan ◽  
...  
Keyword(s):  
Selective Attention ◽  
Intelligent Agents ◽  
Information Acquisition ◽  
Computational Models ◽  
The Past ◽  
Human Experiments ◽  
Neural Underpinnings ◽  
Computational Intelligent ◽  
And Robotics ◽  
Shed Light

Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for computational intelligent agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

Artificial Intelligence (AI): Revolutionizing Digital Marketing

2021 ◽  
pp. 183933492110376
Author(s):  
Patrick van Esch ◽  
J. Stewart Black
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Real World ◽  
Research Process ◽  
Digital Marketing ◽  
Customer Acquisition ◽  
Special Edition ◽  
Practical Application ◽  
The Past ◽  
Acquisition Costs

Artificial intelligence (AI)-enabled digital marketing is revolutionizing the way organizations create content for campaigns, generate leads, reduce customer acquisition costs, manage customer experiences, market themselves to prospective employees, and convert their reachable consumer base via social media. Real-world examples of organizations who are using AI in digital marketing abound. For example, Red Balloon and Harley Davidson used AI to automate their digital advertising campaigns. However, we are early in the process of both the practical application of AI by firms broadly and by their marketing functions in particular. One could argue that we are even earlier in the research process of conceptualizing, theorizing, and researching the use and impact of AI. Importantly, as with most technologies of significant potential, the application of AI in marketing engenders not just practical considerations but ethical questions as well. The ability of AI to automate activities, that in the past people did, also raises the issue of whether marketing professionals will embrace AI as a means to free them from more mundane tasks to spend time on higher value activities, or will they view AI as a threat to their employment? Given the nascent nature of research on AI at this point, the full capabilities and limitations of AI in marketing are unknown. This special edition takes an important step in illuminating both what we know and what we yet need to research.

INTEGRATING DISTRIBUTED EXPERTISE

1992 ◽  
Vol 01 (03n04) ◽  
pp. 393-410 ◽  
Author(s):  
EVANGELOS SIMOUDIS ◽  
MARK ADLER
Keyword(s):  
Problem Solving ◽  
Expert Systems ◽  
Real World ◽  
Computational Models ◽  
Knowledge Based Systems ◽  
Computing Environment ◽  
The Past ◽  
Knowledge Based ◽  
Narrow Scope ◽  
Distributed Expertise

Over the past ten years a myriad of knowledge-based expert systems have been developed and deployed. These systems have a narrow scope and usually operate in stand-alone mode. They also follow different implementation philosophies and use a variety of reasoning methods. To address problems of wider scope, researchers have developed systems that utilize either centralized or distributed computational models. Each of these systems is homogeneous, and due to the way developed, prohibitively expensive for real-world settings. In this paper we present OMNI, a framework for integrating existing knowledge-based systems in a way that they can cooperate during problem-solving while they remain distributed over a computing environment.

Fifty Years of Research on Self-Replication: An Overview

1998 ◽  
Vol 4 (3) ◽  
pp. 237-257 ◽  
Author(s):  
Moshe Sipper
Keyword(s):  
Cellular Automata ◽  
Computer Science ◽  
Real World ◽  
Computational Models ◽  
Computer Programs ◽  
Point Of View ◽  
New Materials ◽  
The Past ◽  
Real World Applications ◽  
Self Replication

The study of artificial self-replicating structures or machines has been taking place now for almost half a century. My goal in this article is to present an overview of research carried out in the domain of self-replication over the past 50 years, starting from von Neumann's work in the late 1940s and continuing to the most recent research efforts. I shall concentrate on computational models, that is, ones that have been studied from a computer science point of view, be it theoretical or experimental. The systems are divided into four major classes, according to the model on which they are based: cellular automata, computer programs, strings (or strands), or an altogether different approach. With the advent of new materials, such as synthetic molecules and nanomachines, it is quite possible that we shall see this somewhat theoretical domain of study producing practical, real-world applications.

Sven Otto Hörstadius. 18 February 1898 — 16 June 1996

2000 ◽  
Vol 46 ◽  
pp. 243-256 ◽  
Author(s):  
C.-O. Jacobson
Keyword(s):  
Developmental Biology ◽  
Essential Role ◽  
Early Embryo ◽  
Egg Cell ◽  
Special Importance ◽  
Biology Textbooks ◽  
The Past ◽  
Head Development ◽  
Embryo Cells ◽  
Shed Light

During the past few decades, developmental biology has become one of the most exciting areas of science. This is largely a result of the rapidly growing knowledge of molecular biology, partly resulting from the achievements of earlier generations of embryologists. One of the men of special importance among the embryologists of the twentieth century was Sven Hörstadius. Most of the content in today's biology textbooks describes results from the post – Watson–Crick era. However, few texts exclude figures and facts from Sven Hörstadius's dissertation of exactly seventy years ago. His experiments on sea urchin larvae shed light on a couple of the most central findings in developmental biology, namely that the uneven distribution of the egg–cell contents gives rise to early embryo cells with shifting qualities, and that communication between these cells has an essential role in the differentiation process. In the same way, his achievements on the neural crest and its impact on head development in vertebrates have been of lasting importance.

Cognitive Neuroscience of Causal Reasoning

2017 ◽  
Author(s):  
Joachim T. Operskalski ◽  
Aron K. Barbey
Keyword(s):  
Cognitive Neuroscience ◽  
Causal Reasoning ◽  
Computational Models ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Human Cognition ◽  
Composition Theory ◽  
Neuroscience Research ◽  
Experimental Findings ◽  
Shed Light

The era of functional neuroimaging promised to shed light on dark corners of the brain’s inner workings, breathing new life into subfields of psychology beset by controversy. Although revelations from neuroscience provide the foundation for current views on many aspects of human cognition, there continue to be areas of study in which a mismatch between the questions asked by psychologists and neuroscientists renders the implications of neuroscience research unclear. Causal reasoning is one such topic, for which decades of cognitive neuroscience findings have revealed a heterogeneity of participating brain regions and networks across different experimental paradigms. This chapter discusses (i) three cognitive and computational models of causal reasoning (mental models, causal models, and force composition theory), (ii) experimental findings on causal judgment and reasoning using cognitive neuroscience methods, and (iii) the need for a multidisciplinary approach to understanding the nature and mechanisms of causal reasoning.

scholarly journals Kinetic Monte Carlo Modeling of Graphene Sheet Growth During CVD

2022 ◽  
Author(s):  
Hironori Kondo
Keyword(s):  
Monte Carlo ◽  
Graphene Sheet ◽  
Real World ◽  
Computational Models ◽  
Kinetic Monte Carlo ◽  
Chemical Vapor ◽  
Kinetic Monte Carlo Method ◽  
Research Fields ◽  
Graphene Synthesis ◽  
Shed Light

Graphene is a material of key interest across several research fields. Bulk graphene synthesis, however, has long remained a challenge for larger-scale projects and real-world manufacturability. This work seeks an improved understanding of graphene sheet growth via computational modeling, with the objective of maximizing grain size. To this end, the kinetic Monte Carlo method is used to simulate chemical vapor deposition under various configurations of carbon flow and graphene seeding. Ultimately, both quantitative and qualitative results are obtained to shed light on graphene growth mechanisms, with insights into real-world synthesis and future computational models.

Modeling and using context

1998 ◽  
Vol 13 (2) ◽  
pp. 185-194 ◽  
Author(s):  
PATRICK BRÉZILLON ◽  
MARCOS CAVALCANTI
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real World ◽  
Point Of View ◽  
Theoretical Level ◽  
The Past ◽  
System Building ◽  
Real World Applications ◽  
Different Types ◽  
Model Context

The first International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-97) was held at Rio de Janeiro, Brazil on February 4–6 1997. This article provides a summary of the presentations and discussions during the three days with a focus on context in applications. The notion of context is far from defined, and is dependent in its interpretation on a cognitive science versus an engineering (or system building) point of view. However, the conference makes it possible to identify new trends in the formalization of context at a theoretical level, as well as in the use of context in real-world applications. Results presented at the conference are ascribed in the realm of the works on context over the past few years at specific workshops and symposia. The diversity of the attendees' origins (artificial intelligence, linguistics, philosophy, psychology, etc.) demonstrates that there are different types of context, not a unique one. For instance, logicians model context at the level of the knowledge representation and the reasoning mechanisms, while cognitive scientists consider context at the level of the interaction between two agents (i.e. two humans or a human and a machine). In the latter case, there are now strong arguments proving that one can speak of context only in reference to its use (e.g. context of an item or of a problem solving exercise). Moreover, there are different types of context that are interdependent. This makes it possible to understand why, despite the consensus on some context aspects, agreement on the notion of context is not yet achieved.

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