scholarly journals The Transformation Of Marketing By Ai

2021 ◽  
Vol 23 (08) ◽  
pp. 391-410
Author(s):  
K V Bhanu Kiran ◽  
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Multimedia Social Networks ◽  
Crucial Part ◽  
Vast Area

From the ages, marketing has been a tough nut to crack as to how thoughtful to deal with the customer’s needs and to reach the specified products to them. Marketing is a vast area to deal with which is a crucial part of any business. In this decade we have a significant innovation to manage such issues effectively, which is Artificial Intelligence. Artificial Intelligence is quite possibly the most brilliant region of science today and can undoubtedly be utilized in the acts of marketing. Platforms for multimedia (social networks, news, images, video, Newsletters, infographics, podcasts, blogs, e-books.) are no longer accessible today are not just for the contact between users or users and companies, but also for companies to guide all aspects of business, collect and identify data of paramount importance. The artificial intelligence marketing technique has become climacteric for companies to find consumer conduct and needs. In this paper, I will walk you through the artificial intelligence marketing technique which transformed marketing into a whole new level. By the end of this paper, you will have a brief idea of how marketing has changed by knowing consumer conduct and needs using artificial intelligence.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

scholarly journals Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 539
Author(s):  
Robin Cohen ◽  
Karyn Moffatt ◽  
Amira Ghenai ◽  
Andy Yang ◽  
Margaret Corwin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Networking ◽  
Older Adult ◽  
Online Social Networks ◽  
Social Behaviour ◽  
Data Science ◽  
Science Studies ◽  
Trust Modeling ◽  
The Future

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare.

scholarly journals A Novel Social Situation Analytics-Based Recommendation Algorithm for Multimedia Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 117749-117760 ◽  
Author(s):  
Zhiyong Zhang ◽  
Ranran Sun ◽  
Kim-Kwang Raymond Choo ◽  
Kefeng Fan ◽  
Wei Wu ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Situation ◽  
Recommendation Algorithm ◽  
Multimedia Social Networks

scholarly journals From the Golem's Jewish Myth to IBM's responsive Watson: where are we going?

2018 ◽  
Vol 21 ◽  
pp. 118
Author(s):  
Jose Luiz Goldfarb ◽  
Odecio Souza
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Social Networks ◽  
21St Century ◽  
Collective Intelligence ◽  
Spike Jonze

Since data mining uses notions from areas such as cybernetics and artificial intelligence, it is worth evoking here ages-old fears elicited by the idea of automatons created to help humans, but which eventually turned against their creators. Examples might range from the Jewish myth of the Golem to the more famous Frankenstein, Hal from Stanley Kubrick’s 2001: A Space Odyssey (1968), and the more recent Her, by Spike Jonze (2013). In this discussion we pay special attention to the fact that in the 21st-century it seems to be less a matter of creating an individual cybernetic creature, than of the rise of social networks, which are alluded by many as collective intelligence. Such collective intelligence might involve, for instance, the responsive ability of IBM’s Watson.

Reports on the 2013 AAAI Fall Symposium Series

AI Magazine ◽  
2014 ◽  
Vol 35 (2) ◽  
pp. 69-74
Author(s):  
Gully Burns ◽  
Yolanda Gil ◽  
Yan Liu ◽  
Natalia Villanueva-Rosales ◽  
Sebastian Risi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Social Networks ◽  
Big Data ◽  
Computational Social Science ◽  
Social Contagion ◽  
Washington Dc ◽  
Web Analytics ◽  
Symbolic Representations ◽  
Symposium Series

The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15–17, at the Westin Arlington Gateway in Arlington, Virginia near Washington DC USA. The titles of the five symposia were as follows: Discovery Informatics: AI Takes a Science-Centered View on Big Data (FS-13-01); How Should Intelligence be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or — ? (FS-13-02); Integrated Cognition (FS-13-03); Semantics for Big Data (FS-13-04); and Social Networks and Social Contagion: Web Analytics and Computational Social Science (FS-13-05). The highlights of each symposium are presented in this report.

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