1. What is artificial intelligence?

2018 ◽  
Author(s):  
Margaret A. Boden
Keyword(s):  
Artificial Intelligence ◽  
Motor Control ◽  
Information Processing ◽  
Virtual Machines ◽  
Life Sciences ◽  
The Other ◽  
Psychological Skills

Artificial intelligence (AI) seeks to make computers do the sorts of things that minds can do, which involves psychological skills such as perception, association, prediction, planning, and motor control. Intelligence is a richly structured space of diverse information-processing capacities. Accordingly, AI uses many different techniques, addressing many different tasks. ‘What is Artificial Intelligence?’ explains the two main aims of AI: one being technological and the other scientific. It looks at AI’s influence on the life sciences and philosophy. Can any AI system possess real intelligence, creativity, or life? It also considers how AI began, virtual machines, the major types of AI, and cybernetics.

Human and Artificial Intelligence: A Clarification

2021 ◽  
pp. 13-48
Author(s):  
Thomas Fuchs
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Human Person ◽  
The Other ◽  
Human Intelligence ◽  
The Difference ◽  
The One ◽  
And Robotics

The advances in artificial intelligence (AI) and robotics are increasingly calling into question the distinction between the simulation and reality of the human person. On the one hand, they suggest a computeromorphic understanding of human intelligence, and on the other, an anthropomorphic view of AI systems. In other words: we increasingly view ourselves as our machines, and conversely, our machines as ourselves. So, what is the difference between human and AI? And can AI achieve consciousness at some point? The chapter argues that an embodied view of consciousness and the person establishes a notion of intelligence that cannot be reduced to information processing.

DEVELOPING THE SCIENTIFIC MAGAZINE IN THE PERIOD OF THE FOURTH INDUSTRIAL REVOLUTION 4.0

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Nguyen Duy Dung
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Industrial Revolution ◽  
Scientific Journal ◽  
Publishing Research ◽  
The Other ◽  
High Tech ◽  
Fourth Industrial Revolution ◽  
Socio Economic Development ◽  
International Scientific Journal

Characteristics of the industrial revolution 4.0 is the wide application of high-tech achievements, especially information technology, digitalization, artificial intelligence, network connections for management to create sudden changes in socio-economic development of many countries. Therefore, to reach the high-tech time, many magazines in Vietnam have changed dramatically, striving to reach the international scientific journal system of ISI, Scopus. The publication of international standard scientific journal will meet the demand of publishing research results of local scientists, on the other hand contribute to strengthening exchange, cooperation, international integration in science and technology.

Does Performing Other Audit Tasks Affect Going-Concern Judgments?

1999 ◽  
Vol 74 (4) ◽  
pp. 493-508 ◽  
Author(s):  
Stephen E. Rau ◽  
Donald V. Moser
Keyword(s):  
Information Processing ◽  
Review Process ◽  
Negative Information ◽  
The Other ◽  
Positive Information ◽  
Going Concern ◽  
Staff Members

This study examines whether personally performing other audit tasks can bias supervising seniors' going-concern judgments. During an audit, the senior performs some audit tasks him/herself and delegates other tasks to staff members. When personally performing an audit task, the senior would focus on the evidence related to that task. We predict that such evidence will have greater influence on the senior's subsequent going-concern judgment. The results of our experiment are consistent with our predictions. When provided with an identical set of information, seniors who performed another audit task for which the underlying facts of the case reflected positively (negatively) on the company's viability, subsequently made going-concern judgments that were relatively more positive (negative). Our results also demonstrate that the well-documented tendency of auditors to attend more to negative information does not always dominate auditors' information processing. Subjects who performed the task for which the underlying facts reflected positively on the company's viability directed their attention to such positive information and, consequently, both their memory and judgments were more positive than those of subjects in the other conditions. Recent findings indicating that biases in seniors' going-concern judgments may not be fully offset in the review process are discussed along with other potential implications of our results.

Activity, Agency, and Inner Speech Pathology

2018 ◽  
Author(s):  
Lauren Swiney
Keyword(s):  
Motor Control ◽  
Sensory Input ◽  
The Other ◽  
Predictive Processing ◽  
Inner Speech ◽  
Speech Pathology ◽  
Active Inference ◽  
Comparator Hypothesis

Over the last thirty years the comparator hypothesis has emerged as a prominent account of inner speech pathology. This chapter discusses a number of cognitive accounts broadly derived from this approach, highlighting the existence of two importantly distinct notions of inner speech in the literature; one as a prediction in the absence of sensory input, the other as an act with sensory consequences that are themselves predicted. Under earlier frameworks in which inner speech is described in the context of classic models of motor control, I argue that these two notions may be compatible, providing two routes to inner speech pathology. Under more recent accounts grounded in the architecture of Bayesian predictive processing, I argue that “active inference” approaches to action generation pose serious challenges to the plausibility of the latter notion of inner speech, while providing the former notion with rich explanatory possibilities for inner speech pathology.

scholarly journals Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Pierre Auloge ◽  
Julien Garnon ◽  
Joey Marie Robinson ◽  
Sarah Dbouk ◽  
Jean Sibilia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Interventional Radiology ◽  
Clinical Practice ◽  
Medical Students ◽  
Medical Curriculum ◽  
New Technology ◽  
Large Population ◽  
The Other ◽  
Clinical Phase ◽  
Preclinical Phase

Abstract Objectives To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

The perception of humanness in conversational journalism: An algorithmic information-processing perspective

2021 ◽  
pp. 146144482199380
Author(s):  
Donghee Shin
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Cognitive Model ◽  
Human Beings ◽  
User Perception ◽  
User Experiences ◽  
Step Flow ◽  
Algorithmic Information ◽  
Information Processing Perspective ◽  
Practical Implications

How much do anthropomorphisms influence the perception of users about whether they are conversing with a human or an algorithm in a chatbot environment? We develop a cognitive model using the constructs of anthropomorphism and explainability to explain user experiences with conversational journalism (CJ) in the context of chatbot news. We examine how users perceive anthropomorphic and explanatory cues, and how these stimuli influence user perception of and attitudes toward CJ. Anthropomorphic explanations of why and how certain items are recommended afford users a sense of humanness, which then affects trust and emotional assurance. Perceived humanness triggers a two-step flow of interaction by defining the baseline to make a judgment about the qualities of CJ and by affording the capacity to interact with chatbots concerning their intention to interact with chatbots. We develop practical implications relevant to chatbots and ascertain the significance of humanness as a social cue in CJ. We offer a theoretical lens through which to characterize humanness as a key mechanism of human–artificial intelligence (AI) interaction, of which the eventual goal is humans perceive AI as human beings. Our results help to better understand human–chatbot interaction in CJ by illustrating how humans interact with chatbots and explaining why humans accept the way of CJ.

The 6C model for accurately capturing the patient’s medical history

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Taro Shimizu
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Patient Safety ◽  
Information Processing ◽  
Safety Concern ◽  
Data Gathering ◽  
Diagnostic Errors ◽  
Accurate Diagnosis ◽  
History Taking ◽  
Novel Approach

Abstract Diagnostic errors are an internationally recognized patient safety concern, and leading causes are faulty data gathering and faulty information processing. Obtaining a full and accurate history from the patient is the foundation for timely and accurate diagnosis. A key concept underlying ideal history acquisition is “history clarification,” meaning that the history is clarified to be depicted as clearly as a video, with the chronology being accurately reproduced. A novel approach is presented to improve history-taking, involving six dimensions: Courtesy, Control, Compassion, Curiosity, Clear mind, and Concentration, the ‘6 C’s’. We report a case that illustrates how the 6C approach can improve diagnosis, especially in relation to artificial intelligence tools that assist with differential diagnosis.

AI technologies and their impact on supply chain resilience during COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Modgil ◽  
Shivam Gupta ◽  
Rébecca Stekelorum ◽  
Issam Laguir
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Information Processing ◽  
Supply Chains ◽  
Failure Modes ◽  
Stress Test ◽  
Weak Links ◽  
Content Type ◽  
Supply Chain Resilience

PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

Evoked Potentials by Letters in Printed and Script Forms

1984 ◽  
Vol 59 (1) ◽  
pp. 227-232 ◽  
Author(s):  
Luciano Mecacci ◽  
Dario Salmaso
Keyword(s):  
Information Processing ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Adult Male ◽  
Visual Information ◽  
The Other ◽  
Behavioral Data ◽  
Evoked Responses ◽  
Amplitude Component ◽  
Male Subjects

Visual evoked potentials were recorded for 6 adult male subjects in response to single vowels and consonants in printed and script forms. Analysis showed the vowels in the printed form to have evoked responses with shorter latency (component P1 at about 133 msec.) and larger amplitude (component P1-N1) than the other letter-typeface combinations. No hemispheric asymmetries were found. The results partially agree with the behavioral data on the visual information-processing of letters.

Invisible Architectures

2000 ◽  
Vol 13 (1) ◽  
pp. 121-136 ◽  
Author(s):  
Hans-Jörg Rheinberger
Keyword(s):  
Nineteenth Century ◽  
Seventeenth Century ◽  
Historical Development ◽  
Basic Problem ◽  
Life Sciences ◽  
The Other ◽  
Modern Life ◽  
Historical Process ◽  
Strategy I ◽  
The Invisible

The ArgumentIn this essay I will sketch a few instances of how, and a few forms in which, the “invisible” became an epistemic category in the development of the life sciences from the seventeenth century through the end of the nineteenth century. In contrast to most of the other papers in this issue, I do not so much focus on the visualization of various little entities, and the tools and contexts in which a visual representation of these things was realized. I will be more concerned with the basic problem of introducing entities or structures that cannot be seen, as elements of an explanatory strategy. I will try to review the ways in which the invisibility of such entities moved from the unproblematic status of just being too small to be accessible to the naked or even the armed eye, to the problematic status of being invisible in principle and yet being indispensable within a given explanatory framework. The epistemological concern of the paper is thus to sketch the historical process of how the “unseen” became a problem in the modern life sciences. The coming into being of the invisible as a space full of paradoxes is itself the product of a historical development that still awaits proper reconstruction.

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