An Exploration into the Nature of the Making of Human and Artifcial Intelligence and the Qur’anic Persepctive

1992 ◽  
Vol 9 (4) ◽  
pp. 465-481
Author(s):  
Mahmoud Dhaouadi
Keyword(s):  
Artificial Intelligence ◽  
Applied Research ◽  
Human Intelligence ◽  
Short Term ◽  
Information Process ◽  
Cultural Symbols ◽  
Ongoing Controversy ◽  
Basic And Applied Research

The ongoing controversy over artificial and human intelligence ischaracterized by open disagreement. Some researchers believe that artificialintelligence has the potential to become equal to or even superior tohuman intelligence, while others say that such a development is impossible.The thesis of this paper is that the gap between human and artificialintelligence is bound to remain considerable, both in the short term andin the long term. The concepts of human cultural symbols and theQur’anic vision of human intelligence are intduced in support of thisthesis. Humanity’s ability to manipulate cultural symbols, upon which thephenomenon of human intelligence depends, is a uniquely human characteristic.And this uniqueness, according to the Qur’an, is the direct resultof a divine decision, not of evolution. As such an ability and many of themysteries of that power, are hardly accessible to humans, how wouldhuman researchers be able to include them in the design of artificial intelligencemachines?In the last two decades, research in the field of artificial intelligence(hereinafter referred to as AI) has made considerable headway on both thetheoretical and the applied levels. The input into the field has not beenrestricted only to cybernetics and information process experts; neurophysiologists,cognitive psychologists, philosophers, and sociologists’ havealso been interested in human intelligence (hereinafter referred to as HI)and AI. As AI infrastructures and authority continue to expand in modemand postmodem societies, specialists in other areas will have to becomeinvolved.For scientists, basic and applied research into A1 constitute an exciting ...

ConsciousControlFlow(CCF): Conscious Artificial Intelligence Based on Needs

2021 ◽  
pp. 2150013
Author(s):  
Hongzhi Wang ◽  
Bozhou Chen ◽  
Yueyang Xu ◽  
Kaixin Zhang ◽  
Shengwen Zheng
Keyword(s):  
Artificial Intelligence ◽  
Computational Model ◽  
Short Term Memory ◽  
Special Functions ◽  
Long Term Memory ◽  
Hierarchy Of Needs ◽  
Short Term ◽  
Term Memory

The major criteria to distinguish conscious Artificial Intelligence (AI) and non-conscious AI is whether the conscious is from the needs. Based on this criteria, we develop ConsciousControlFlow(CCF) to show the need-based conscious AI. The system is based on the computational model with a short-term memory (STM) and long-term memory (LTM) for consciousness and the hierarchy of needs. To generate AI based on real needs of the agent, we developed several LTMs for special functions such as feeling and sensor. Experiments have demonstrated that the agents in the proposed system behave according to the needs, which coincides with the prediction.

Involving farmers in agricultural research: A farmer's perspective

1990 ◽  
Vol 5 (4) ◽  
pp. 174-177 ◽  
Author(s):  
Kay Thornley
Keyword(s):  
Applied Research ◽  
Systems Approach ◽  
Agricultural Research ◽  
Research Priorities ◽  
Academic Emphasis ◽  
Personal Interest ◽  
Political Processes ◽  
Basic And Applied Research ◽  
On Farm

AbstractFarmer involvement in agricultural research is limited by inadequate funding, institutional policies and hierarchies, disciplinary specialization, and incompatible personalities. Additional barriers include academic emphasis on carefully controlled experiments, research priorities driven by personal interest, and farmers' reluctance to disclose trade secrets. Priorities for research conducted with public funds should be identified through a democratic process involving representatives from all sectors of agriculture. A broad, multidisciplinary, systems approach to agricultural research is needed; and farmers and researchers should consider long-term implications of projects. Better balance needs to be achieved between basic and applied research, and both should encourage innovation within the context of democratically determined research priorities. Opportunities abound for involving farmers in research as providers and recipients of information, as participants in determining priorities and ensuring practicality of methods, as collaborators and/or subjects for on-farm investigations, and as project evaluators. Farmers also need to take more initiative in getting involved in the political processes that set the stage for agricultural research.

Fostering CRM Through Artificial Intelligence

2022 ◽  
pp. 70-91
Author(s):  
Nidhi Shridhar Natrajan ◽  
Sanjeev Kumar Singh ◽  
Rinku Sanjeev
Keyword(s):  
Artificial Intelligence ◽  
New Technology ◽  
Strong Support ◽  
Current Approach ◽  
Organizational Capabilities ◽  
Human Intelligence ◽  
Successful Implementation ◽  
New Paradigm ◽  
Use Of Technology

The use of technology has always provided competitive advantage to organizations. The current approach of adapting a new technology is the long-term planning. AI has become a new paradigm of enhancing organizational capabilities. AI is not a substitute of human intelligence but a strong support in terms of process automation. Apart from this, the decision-making process also gets streamlined. The success of business is when the customer is happy. To create and retain customer loyalty, effective CRM is required. The current chapter focuses on adoption of AI for efficient CRM and the factors for its successful implementation.

scholarly journals Cost-Effectiveness of Robotics and Artificial Intelligence in Healthcare

2019 ◽  
Vol 87 (2) ◽  
pp. 49-51
Author(s):  
Caroline Rose Piccininni
Keyword(s):  
Artificial Intelligence ◽  
Cost Effectiveness ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Decision ◽  
Medical Procedures ◽  
Short Term ◽  
Diagnosis And Prognosis ◽  
Widespread Implementation

New technologies, especially those based in robotics and artificial intelligence, have potential to vastly change how healthcare is delivered from managing patient information to diagnosis and prognosis to performing medical procedures. Such technologies are constantly being developed, trialed, and implemented and thus, many have yet to be investigated in terms of long-term costs and effects. Using robot-assisted prostatectomy as an example, this article explores how new technologies are often associated with high initial costs and positive short-term effects but their long-term cost-effectiveness remains unknown. This idea has important implications for widespread implementation of new technologies and for clinical decision making.

scholarly journals The First microRTS Artificial Intelligence Competition

AI Magazine ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Santiago Ontañón ◽  
Nicolas A. Barriga ◽  
Cleyton R. Silva ◽  
Rubens O. Moraes ◽  
Levi H. S. Lelis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Computational Intelligence ◽  
Learning To Learn ◽  
Short Term ◽  
Partial Observability

This article presents the results of the first edition of the microRTS (μRTS) AI competition, which was hosted by the IEEE Computational Intelligence in Games (CIG) 2017 conference. The goal of the competition is to spur research on AI techniques for real-time strategy (RTS) games. In this first edition, the competition received three submissions, focusing on address- ing problems such as balancing long-term and short-term search, the use of machine learning to learn how to play against certain opponents, and finally, dealing with partial observability in RTS games.

scholarly journals Do company-owned academic patents influence firm performance? Evidence from the Italian industry

2021 ◽  
Author(s):  
Giovanni Cerulli ◽  
Giovanni Marin ◽  
Eleonora Pierucci ◽  
Bianca Potì
Keyword(s):  
Applied Research ◽  
Knowledge Spillovers ◽  
Economic Incentives ◽  
Fixed Cost ◽  
Short Term ◽  
Academic Knowledge ◽  
Negative Effect ◽  
Academic Patents ◽  
Basic And Applied Research ◽  
University Industry

AbstractWe document that firms holding academic patents in their portfolios perform better in terms of market power since they benefit from academic knowledge spillovers generated by academic patents. On the other hand, we detect a negative effect on firms’ short-term profitability imputable to a larger fixed cost associated to the acquisition and exploitation of these patents. In terms of policy, our analysis suggests focusing on company-owned academic patents. A set of economic incentives dedicated to university–industry knowledge transfer through academic patents could support integration between basic and applied research.

scholarly journals OpenUP Measuring Research Impact: Concepts, Methods, Limitations and Solutions

2018 ◽  
Author(s):  
Mappet Walker ◽  
Stephanie Oeben ◽  
Richard Walker
Keyword(s):  
Return On Investment ◽  
Applied Research ◽  
Basic Research ◽  
Research Impact ◽  
Financial Health ◽  
Short Term ◽  
Policy Makers ◽  
Scientific Impact ◽  
Research Metrics

Watch the VIDEO.The immediate Return on Investment (ROI) for basic scientific research is scientific impact – improvements in knowledge of our physical, biological and social world. The Return on Investment (ROI) for applied research and the long-term Return on Investment for basic research is societal impact (i.e. impact on health, environment, the economy, etc.). However, many of these impacts are hard to measure and may only be apparent decades after the original investment. This creates demand from funders and policy makers for metrics that predict impacts before they can be measured. Here we define a preliminary conceptual framework describing the chain of events leading from the outputs of basic research  (publications, data, software, cell lines, equipment, methodologies, theories etc.) to the outputs of applied research (products, treatments, technology components etc.) to societal, financial, health and environmental impact. We go on to discuss how these impacts are currently measured in the short term (days and weeks), the medium term (years) and the long term (decades), and to identify the main providers of impact metrics. We highlight the different ways in which research metrics are used by different categories of user (researchers, institutions, national and European policymakers). Finally, we discuss the limitations of current metrics and possible solutions.

scholarly journals The Impact of Big Data Processing Framework for Artificial Intelligence within Corporate Marketing Communication

2018 ◽  
Vol 7 (4.34) ◽  
pp. 384
Author(s):  
Muhamad Fazil Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Positive Impact ◽  
Marketing Communication ◽  
Short Term ◽  
The Impact ◽  
Processing Framework

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.   

scholarly journals Sustainable landscape development and value rigidity: the Pirsig‘s monkey trap

2015 ◽  
Vol 40 ◽  
pp. 1-19 ◽  
Author(s):  
Giovanni Zurlini ◽  
Irene Petrosillo ◽  
András Bozsik ◽  
Jon Cloud ◽  
Roberta Aretano ◽  
...  
Keyword(s):  
Applied Research ◽  
Stable Condition ◽  
Sustainable Land Use ◽  
Short Term ◽  
Societal Values ◽  
Basic And Applied Research ◽  
Sustainability Problems ◽  
The Cost ◽  
Sustainable Landscape ◽  
Global Transition

New broader, adaptable and accommodating sets of themes have been proposed to help to identify, understand and solve sustainability problems. However, how this knowledge will foster decisions that lead to more desirable outcomes and analyses necessary to transition to sustainability remains a critical theoretical and empirical question for basic and applied research. We argue that we are still underestimating the tendency to lock into certain patterns that come at the cost of the ability to adjust to new situations. This rigidity limits the ability of persons, groups, and companies to respond to new problems, and can make it hard to learn new facts because we pre-select facts as important, or not, in line with our established values. Changing circumstances demand to reappraise values like in the case of Pirsig's monkey and its rice. There is an urgent need to go beyond such local, static and short-term conceptions, where landscape sustainability has been incorrectly envisioned as a durable, stable condition that, once achieved, could persist for generations. We argue that to manage a global transition toward more environmentally efficient and, therefore, more sustainable land-use we have to reappraise societal values at the root of overregulation and rigidity.

scholarly journals Cell graph neural networks enable the digital staging of tumor microenvironment and precise prediction of patient survival in gastric cancer

2021 ◽  
Author(s):  
Yanan Wang ◽  
Yu Guang Wang ◽  
Changyuan Hu ◽  
Ming Li ◽  
Yanan Fan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Tumor Microenvironment ◽  
Spatial Patterns ◽  
Patient Survival ◽  
Staging System ◽  
Short Term ◽  
Precise Prediction ◽  
Cell Graph

ABSTRACTGastric cancer is one of the deadliest cancers worldwide. Accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a novel graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver-Operating Characteristic curve (AUROC) of 0.960±0.01, and 0.771±0.024 to 0.904±0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the ‘digital-grade’ cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (p-value < 0.0001), significantly outperforming the AJCC 8th edition Tumor-Node-Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.

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