scholarly journals Unsupervised Novelty Characterization in Physical Environments Using Qualitative Spatial Relations

2021 ◽  
Author(s):  
Ruiqi Li ◽  
Hua Hua ◽  
Patrik Haslum ◽  
Jochen Renz
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Spatial Relations ◽  
State Transitions ◽  
Conceptual Similarity ◽  
Physical Environments ◽  
The World ◽  
Unsupervised Approach ◽  
Action Models ◽  
Unseen Objects

Detecting, characterizing and adapting to novelty, whether in the form of previously unseen objects or phenomena, or unexpected changes in the behavior of known elements, is essential for Artificial Intelligence agents to operate reliably in unconstrained real-world environments. We propose an automatic, unsupervised approach to novelty characterization for dynamic domains, based on describing the behaviors and interactions of objects in terms of their possible actions. To abstract from the variety of realizations of an action that can occur in physical domains, we model states in terms of qualitative spatial relations (QSRs) between their entities. By first learning a model of actions in the non-novel environment from the state transitions observed as the agent interacts with the world, we can detect novelty by the persistent deviations from this model that it causes, and characterize the novelty by new or modified actions. We also present a new method of learning action models from observation, based on conceptual similarity and hierarchical clustering.

scholarly journals Learning Portable Symbolic Representations

2018 ◽  
Author(s):  
Steven James
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Approaches To Learning ◽  
Specific Task ◽  
Levels Of Abstraction ◽  
Independent Manner ◽  
Symbolic Representations ◽  
The World ◽  
Open Question ◽  
Domain Independent

An open question in artificial intelligence is how to learn useful representations of the real world. One approach is to learn symbols, which represent the world and its contents, as well as models describing the effects on these symbols when interacting with the world. To date, however, research has investigated learning such representations for a single specific task. Our research focuses on approaches to learning these models in a domain-independent manner. We intend to use these symbolic models to build even higher levels of abstraction, creating a hierarchical representation which could be used to solve complex tasks. This would allow an agent to gather knowledge over the course of its lifetime, which could then be leveraged when faced with a new task, obviating the need to relearn a model every time a new unseen problem is encountered.

scholarly journals The Technical Buzzword: Artificial Intelligence

2021 ◽  
Vol 1 (4) ◽  
pp. 1-3
Author(s):  
Banya Arabi Sahoo ◽  
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Real World ◽  
Human Being ◽  
Science And Engineering ◽  
The Real ◽  
Advantages And Disadvantages ◽  
Real World Problem ◽  
John Mccarthy ◽  
The World

AI is the incredibly exciting technique to the world. According to John McCarthy it is “The science and engineering of making intelligent machine, especially intelligent computers”. AI is the way of creating extraordinary powerful machine which is similar as human being. The AI is being accomplished by studying how human brain think, how they learn, decide, work, solving the real world problem and after that verify the outcomes and studying it. Primarily you can learn here what AI is and how it works, its types, its history, its agents, its applications, its advantages and disadvantages.

Mind in the World

1970 ◽  
Vol 7 (1) ◽  
pp. 193-204
Author(s):  
Jan Regner
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Philosophical Problem ◽  
Chinese Room ◽  
The Real ◽  
The World

As the title of my article can indicate, the primary aim of this „brief introduction" is to present the concept of intentionality of one of the world's leading philosophers - John R. Searle. Searle is known for his severe criticism of the dominant traditions in the study of mind, both materialist and dualist, and we may also recall his familiar argument called „the Chinese Room" against theories of „artificial intelligence". The concept of intentionality was founded when philosophers attempted to describe and solve the philosophical problem of specific „quasi-relations" between consciousness and objects and the direction of our mind or language to the real world. I am referring to situations in which we say for instance: „A thinks about p", "B maintains that g", „X asks question if y" and so on.

scholarly journals The Technical Buzzword: Artificial Intelligence

2021 ◽  
pp. 1-3
Author(s):  
Banya Arabi Sahoo ◽  
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Real World ◽  
Human Being ◽  
Science And Engineering ◽  
The Real ◽  
Advantages And Disadvantages ◽  
Real World Problem ◽  
John Mccarthy ◽  
The World

AI is the incredibly exciting technique to the world. According to John McCarthy it is “The science and engineering of making intelligent machine, especially intelligent computers”. AI is the way of creating extraordinary powerful machine which is similar as human being. The AI is being accomplished by studying how human brain think, how they learn, decide, work, solving the real world problem and after that verify the outcomes and studying it. Primarily you can learn here what AI is and how it works, its types, its history, its agents, its applications, its advantages and disadvantages.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 5. Introduction to Logic

Discourse ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 109-117
Author(s):  
O. M. Polyakov
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Logic ◽  
Natural Language ◽  
Semantic Interpretation ◽  
General View ◽  
World Model ◽  
Semantic Approach ◽  
Natural Languages ◽  
The World ◽  
Linguistic Aspect

Introduction. The article continues the series of publications on the linguistics of relations (hereinafter R–linguistics) and is devoted to an introduction to the logic of natural language in relation to the approach considered in the series. The problem of natural language logic still remains relevant, since this logic differs significantly from traditional mathematical logic. Moreover, with the appearance of artificial intelligence systems, the importance of this problem only increases. The article analyzes logical problems that prevent the application of classical logic methods to natural languages. This is possible because R-linguistics forms the semantics of a language in the form of world model structures in which language sentences are interpreted.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of logic and semantics, the formulated concept of the interpretation operator is used.Results and discussion. The problems that arise when studying the logic of natural language in the framework of R–linguistics are analyzed. These issues are discussed in three aspects: the logical aspect itself; the linguistic aspect; the aspect of correlation with reality. A very General approach to language semantics is considered and semantic axioms of the language are formulated. The problems of the language and its logic related to the most General view of semantics are shown.Conclusion. It is shown that the application of mathematical logic, regardless of its type, to the study of natural language logic faces significant problems. This is a consequence of the inconsistency of existing approaches with the world model. But it is the coherence with the world model that allows us to build a new logical approach. Matching with the model means a semantic approach to logic. Even the most General view of semantics allows to formulate important results about the properties of languages that lack meaning. The simplest examples of semantic interpretation of traditional logic demonstrate its semantic problems (primarily related to negation).

EXPLORING HEALTHCARE ARCHITECTURE THROUGH THE MEDIUM OF FILM: Motives and Techniques

2014 ◽  
Vol 8 (1) ◽  
pp. 29
Author(s):  
Stephen Verderber
Keyword(s):  
Real World ◽  
The Real ◽  
Healthcare Settings ◽  
Interdisciplinary Field ◽  
Body Of Knowledge ◽  
Behavior Setting ◽  
Physical Environments ◽  
Scant Attention ◽  
History Of

The interdisciplinary field of person-environment relations has, from its origins, addressed the transactional relationship between human behavior and the built environment. This body of knowledge has been based upon qualitative and quantitative assessment of phenomena in the “real world.” This knowledge base has been instrumental in advancing the quality of real, physical environments globally at various scales of inquiry and with myriad user/client constituencies. By contrast, scant attention has been devoted to using simulation as a means to examine and represent person-environment transactions and how what is learned can be applied. The present discussion posits that press-competency theory, with related aspects drawn from functionalist-evolutionary theory, can together function to help us learn of how the medium of film can yield further insights to person-environment (P-E) transactions in the real world. Sampling, combined with extemporary behavior setting analysis, provide the basis for this analysis of healthcare settings as expressed throughout the history of cinema. This method can be of significant aid in examining P-E transactions across diverse historical periods, building types and places, healthcare and otherwise, otherwise logistically, geographically, or temporally unattainable in real time and space.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

scholarly journals Handrails through the Swamp? A Pilot to Test the Integration and Implementation Science Framework in Complex Real-World Research

2021 ◽  
Vol 13 (10) ◽  
pp. 5491
Author(s):  
Melissa Robson-Williams ◽  
Bruce Small ◽  
Roger Robson-Williams ◽  
Nick Kirk
Keyword(s):  
Pilot Study ◽  
Case Studies ◽  
Implementation Science ◽  
Real World ◽  
Environmental Problems ◽  
Environmental Research ◽  
Integrative Framework ◽  
The World ◽  
Science Framework ◽  
Real World Problems

The socio-environmental challenges the world faces are ‘swamps’: situations that are messy, complex, and uncertain. The aim of this paper is to help disciplinary scientists navigate these swamps. To achieve this, the paper evaluates an integrative framework designed for researching complex real-world problems, the Integration and Implementation Science (i2S) framework. As a pilot study, we examine seven inter and transdisciplinary agri-environmental case studies against the concepts presented in the i2S framework, and we hypothesise that considering concepts in the i2S framework during the planning and delivery of agri-environmental research will increase the usefulness of the research for next users. We found that for the types of complex, real-world research done in the case studies, increasing attention to the i2S dimensions correlated with increased usefulness for the end users. We conclude that using the i2S framework could provide handrails for researchers, to help them navigate the swamps when engaging with the complexity of socio-environmental problems.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

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