Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence

Johnson, Marina; Albizri, Abdullah; Harfouche, Antoine; Fosso-Wamba, Samuel

doi:10.1016/j.ijinfomgt.2022.102479

Cited by 53 publications

(25 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to create the target variable (i.e., data annotation) and make the models explicable, SAI takes into consideration human-domain expertise. DAI has a significant function to play in the study of cancer [16].…”

Section: Ai For Cancer Researchmentioning

confidence: 99%

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

Sebastian

Peter

2022

Life

View full text Add to dashboard Cite

The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment.

show abstract

Section: Ai For Cancer Researchmentioning

confidence: 99%

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

Sebastian

Peter

2022

Life

View full text Add to dashboard Cite

show abstract

“…The opportunity space of AI in disruptive businesses at all levels including operational, strategic contributing to competitive advantage and enhanced performances are immense (Borges, Laurindo, Spínola, Gonçalves, and Mattos, 2021;Johnson, Albizri, Harfouche, and Fosso-Wamba, 2022;Werle and Laumer, 2022). Scholars argue that AI dominance is evident in shaping the businesses and marketplace (Makridakis, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The intention of family businesses (Borges et al, 2021;Dwivedi et al, 2021;Johnson et al, 2022;Werle and Laumer, 2022). Scholars argue that AI dominance is evident in shaping the businesses and marketplace (Makridakis, 2017).…”

Section: Introductionmentioning

confidence: 99%

The influence of digital entrepreneurship and entrepreneurial orientation on intention of family businesses to adopt artificial intelligence: examining the mediating role of business innovativeness

Upadhyay

Al-Debei

et al. 2022

IJEBR

View full text Add to dashboard Cite

PurposeThis study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation.Design/methodology/approachThe study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data.FindingsThe proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business.Research limitations/implicationsThe study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship.Originality/valueTheoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.

show abstract

“…At present, because artificial intelligence are involved mostly a certain professional field, mostly simulating the primary cognitive thinking ability such as perception and memory of human intelligence, and the academic community generally defines this level of artificial intelligence as weak artificial intelligence or special artificial intelligence; in all fields, artificial intelligence that simulates higher thinking such as associations, decisions, and emotions of human intelligence is defined as strong artificial intelligence or artificial general intelligence, which is also called human-like intelligence.after the implementation of artificial general intelligence, artificial intelligence that exceeds the level of human intelligence in some areas is called super artificial intelligence 11 .at present, the drawbacks of special artificial intelligence are gradually emerging, and artificial general intelligence has gradually become a new hot spot 12 .with the new development of neuroscience 13 and cognitive science 14 , there have been research on the hardware direction of simulating brain neurons and structures, and the software aspect has generally followed the three major research methods of special artificial intelligence, such as the NARS system [15][16][17] .but progress is still not optimistic, and there are no public reports of the formation of artificial general intelligence.from the perspectives of philosophy, physics, neuroscience, cognitive science, linguistics, computer science, etc., based on the principles of universal generated philosophy, this paper proposes a new a model of generated artificial general intelligence, explains its philosophical principles, construction models, implementation models, and attaches examples. This will be a cognitive revolution for mankind, the first time for mankind to create a new silicon-based life, and a philosophical revolution for the consciousness.…”

Section: Introductionmentioning

confidence: 99%

Generated art if icial general intelligence --the philosophical principle of artificial general intelligence and give an example

Ma¹

2022

Preprint

View full text Add to dashboard Cite

In recent years, with the development of brain science, neuroscience and cognitive science, artificial intelligence technology has made a series of achievements.however, it still fails to achieve the human level of universal artificial intelligence, and the cognitive structure and consciousness are still unsolved mysteries.this paper integrates the evolutionary lawsof the universe, life and thinking, summarizes a modelof generated general intelligence and reveal its philosophical principle and algorithm structure, thencalculates the functions of thinking and consciousnessone by one.the results show that the model and its based principles and algorithms conform to the essential characteristics of biology, physics, neuroscience, cognitive science and philosophy of intelligent species. It is a first implementation modelof artificialgeneral intelligence that truly simulates human intelligence.this paper reveals the essence of cognition, thinking and consciousness, which is the first time to find the operation mode of the last mysterious zone thathuman brain, and also the first answer to the origin of philosophy and the mystery of human mind since human history.

show abstract

Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence

Cited by 53 publications

References 67 publications

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

The influence of digital entrepreneurship and entrepreneurial orientation on intention of family businesses to adopt artificial intelligence: examining the mediating role of business innovativeness

Generated art if icial general intelligence --the philosophical principle of artificial general intelligence and give an example

Contact Info

Product

Resources

About