New directions for artificial intelligence: human, machine, biological, and quantum intelligence

2021 ◽  
Author(s):  
Li Weigang ◽  
Liriam Michi Enamoto ◽  
Denise Leyi Li ◽  
Geraldo Pereira Rocha Filho
Keyword(s):  
Artificial Intelligence ◽  
New Directions

Strategy and dialectics: Rejuvenating a long-standing relationship

2018 ◽  
Vol 17 (1) ◽  
pp. 133-144 ◽  
Author(s):  
Moshe Farjoun
Keyword(s):  
Artificial Intelligence ◽  
Theoretical Models ◽  
Social Processes ◽  
The State ◽  
Strategy Research ◽  
The Past ◽  
New Directions ◽  
Core Questions ◽  
Relational Process

This essay broadens the conversation on the state of organizational contradictions and paradox research by turning to dialectics—a time-honored, living perspective on social processes and relations, which continues to influence our understanding of the past, present, and future. Dialectics distinctive relational process worldview sets it apart from approaches stressing equilibrium, linearity, and coherence, making it highly relevant to a world in flux. I propose that dialectics is already present in strategy research and in contemporary business, and can become even more central to strategy, addressing core questions in the field and propelling it in new directions. Strategy scholars can draw on dialectics principles as a generative tool kit to construct new theories and managerial tools. Dialectics can also be used as a theoretical lens to understand emerging empirical phenomena such as the rapid advent of artificial intelligence. Finally, dialectics critical stance and philosophical grounding makes it a particularly attractive perspective for challenging existing theoretical models and for considering alternatives.

scholarly journals Artificial intelligence in recommender systems

2020 ◽  
Author(s):  
Qian Zhang ◽  
Jie Lu ◽  
Yaochu Jin
Keyword(s):  
Artificial Intelligence ◽  
Recommender Systems ◽  
Technological Development ◽  
Research Directions ◽  
Research Issues ◽  
Service Support ◽  
Machine Learning Methods ◽  
New Directions ◽  
Fuzzy Techniques ◽  
New Research

Abstract Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

scholarly journals Irresponsible Research and Innovation? Applying Findings from Neuroscience to Analysis of Unsustainable Hype Cycles

2018 ◽  
Vol 10 (10) ◽  
pp. 3472 ◽  
Author(s):  
Stephen Fox
Keyword(s):  
Artificial Intelligence ◽  
Responsible Research And Innovation ◽  
Technological Innovations ◽  
Exploratory Research ◽  
Human Beings ◽  
Negative Consequences ◽  
Research And Innovation ◽  
Irresponsible Behavior ◽  
New Directions ◽  
Responsible Research

The introduction of technological innovations is often associated with suboptimal decisions and actions during cycles of inflated expectations, disappointment, and unintended negative consequences. For brevity, these can be referred to as hype cycles. Hitherto, studies have reported hype cycles for many different technologies, and studies have proposed different methods for improving the introduction of technological innovations. Yet hype cycles persist, despite suboptimal outcomes being widely reported and despite methods being available to improve outcomes. In this communication paper, findings from exploratory research are reported, which introduce new directions for addressing hype cycles. Through reference to neuroscience studies, it is explained that the behavior of some adults in hype cycles can be analogous to that of irresponsible behavior among adolescents. In particular, there is heightened responsiveness to peer presence and potential rewards. Accordingly, it is argued that methods applied successfully to reduce irresponsible behavior among adolescents are relevant to addressing hype cycles, and to facilitating more responsible research and innovation. The unsustainability of hype cycles is considered in relation to hype about artificial intelligence (AI). In particular, the potential for human-beneficial AI to have the unintended negative consequence of being fatally unbeneficial to everything else in the geosphere other than human beings.

Advances in irrigation/fertigation techniques in greenhouse soilless culture systems (SCS)

2021 ◽  
pp. 249-276
Author(s):  
Georgios Nikolaou ◽  
◽  
Damianos Neocleous ◽  
Evangelini Kitta ◽  
Nikolaos Katsoulas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Management System ◽  
Culture System ◽  
Irrigation Management ◽  
Monitoring Systems ◽  
Soilless Culture ◽  
Input Use ◽  
New Directions

The objective of this chapter is to describe the most common soilless culture system (SCS) irrigation and fertigation methods. The chapter summarizes common types of irrigation/fertigation system and types of management system. It then discusses the shift to real-time plant-based sensing and monitoring systems together with models to analyze this data and translate it into irrigation management decisions. A case study is included to illustrate these systems in practice. The chapter concludes by identifying new directions in the application of internet of things (IoT), artificial intelligence and phyto-sensing technologies to optimize input use.

scholarly journals Towards Discovering Employee-Worker Interactions - Aspects of Conclusion of Psychological Contract

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Anna Rogozińska-Pawełczyk
Keyword(s):  
Artificial Intelligence ◽  
Psychological Contract ◽  
New Technologies ◽  
Exchange Relationship ◽  
Employment Relationships ◽  
Subjective Beliefs ◽  
New Directions ◽  
Relationship Of ◽  
The Relationship

The psychological contract refers to presumed and subjective beliefs in relation to the exchange relationship, considered mainly between employees and employers. An immanent part of the psychological contract is its subjectivity and the relationship of exchange of expectations, promises or commitments of both parties to the employment relationship. The conditions in which modern organisations have to operate justify the use of the psychological contract for the analysis of employment relationships, but do not yet take into account the emerging new form of relationship at the workplace. Currently, thanks to the development of new technologies, including artificial intelligence, the role of robots in the workplace is growing. The aim of the article is to outline the framework for building the involvement of employees in technologically, socially and emotionally advanced forms of artificial intelligence. The manifestations of workers' interactions with social robots within the framework of a contractual partnership will be defined. To this end, the arguments for the possibility of concluding a psychological contract between a human and a robot based on the theory of exchange and the standard of reciprocity, which can set new directions for research in this area, are reviewed.

scholarly journals Quantum Mathematics in Artificial Intelligence

2021 ◽  
Vol 72 ◽  
pp. 1307-1341
Author(s):  
Dominic Widdows ◽  
Kirsty Kitto ◽  
Trevor Cohen
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Automated Reasoning ◽  
Knowledge Bases ◽  
Vector Spaces ◽  
Density Matrices ◽  
New Directions ◽  
The Common ◽  
Scalar Products ◽  
Word Senses

In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, decision making, and and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.

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