Longdom Conferences

Speaker

Nov 12-13, 2021    Las Vegas, USA
International Conference on

Robotics and Artificial Intelligence

Metin Argan
11:40 PM-12:10 PM

Metin Argan

Eskisehir Technical University Turkey

Title: Understanding social media users' reactions to artificial intelligence (AI) in advertising: A qualitative study on leisure services

Abstract:

The advancement of technology, such as artificial intelligence (AI), enables new and efficient ways for customers to bring solutions to their personalized preferences in the leisure industry. AI is associated with almost all products or services including leisure, such as travel, vacation, and entertainment. AI can reason with people's social media comments and posts and may reveal their needs, preferences, trends and values. AI has opened up a great opportunity for advertisers to send personalized messages to consumers. While AI is having a major influence in leisure services, consumer reactions to AI advertising have been understudied in the literature. To enhance understanding about the effect of AI advertising messages, this qualitative study included in-depth interviews that investigates consumer reactions to AI-based advertisement messages among social media users. A total of 23 interviews were conducted with social media users aged 18 and over, using purposive sampling method. When determining the sample size in this study, researchers applied saturation as a guiding principle during data collection (Glaser & Strauss, 2017). Qualitative data analysis procedures, as suggested by Braun and Clarke (2006), were applied to this study. Findings indicate that attractiveness, spending time, entertainment, and compatibility in customization are significant factors in determining customer satisfaction and purchase intention. Furthermore, current study participants indicate that price, quality, payment opportunities provided by AI advertisements are highly significant. The results of the current study suggest that artificial intelligence based advertising message was a significant variable, especially when time scarcity, personalization and indecision were high. Knowing the factors that explain consumer reactions to personalized promotional posts in leisure services, such as travel, vacation and entertainment can help in planning strategies for gaining effective results among millennials. Based on findings, managerial implication for customized AI advertisements are formulated.

Biography:

Metin Argan is a Professor of Marketing at the Eskisehir Technical University in Turkey. His research interests primarily relate to sports marketing, fan behavior, leisure, and health care. He also published books, such as sports marketing, entertainment marketing, sport sponsorship management, and recreation management. He has also contributed chapters to several academic books such as sports marketing, and customer relationship management in retailing. He has published refereed articles and books and gave numerous presentations at conferences around the globe. He is a reviewer for many academic journals. Currently, he is investigating e-sports, artificial reality, augmented reality in sports and leisure.

Useful Links

News

Artificial Intelligence and Machine Learning: Policy Paper

2020-08-13

Artificial intelligence is a technology that is already impacting how users interact with, and are affected by the Internet. In the near future, its impact is likely to only continue to grow. AI has the potential to vastly change the way that humans interact, not only with the digital world, but also with each other, through their work and through other socioeconomic institutions – for better or for worse. If we are to ensure that the impact of artificial intelligence will be positive, it will be essential that all stakeholders participate in the debates surrounding AI. In this paper, we seek to provide an introduction to AI to policymakers and other stakeholders in the wider Internet ecosystem.

This robot doesn't need any electronics

2020-12-31

Engineers have created a four-legged soft robot that doesn't need any electronics to work. The robot only needs a constant source of pressurized air for all its functions, including its controls and locomotion systems. The team, led by Michael T. Tolley, a professor of mechanical engineering at the Jacobs School of Engineering at UC San Diego, details its findings in the Feb. 17, 2021 issue of the journal Science Robotics. "This work represents a fundamental yet significant step towards fully-autonomous, electronics-free walking robots," said Dylan Trotman, a Ph.D. student in Tolley's research group and the paper's first author. Applications include low-cost robotics for entertainment, such as toys, and robots that can operate in environments where electronics cannot function, such as MRI machines or mine shafts. Soft robots are of particular interest because they easily adapt to their environment and operate safely near humans. Most soft robots are powered by pressurized air and are controlled by electronic circuits. But this approach requires complex components like circuit boards, valves, and pumps -- often outside the robot's body. These components, which constitute the robot's brains and nervous system, are typically bulky and expensive. By contrast, the UC San Diego robot is controlled by a light-weight, low-cost system of pneumatic circuits, made up of tubes and soft valves, onboard the robot itself. The robot can walk on command or in response to signals it senses from the environment. "With our approach, you could make a very complex robotic brain," said Tolley, the study's senior author. "Our focus here was to make the simplest air-powered nervous system needed to control walking." The robot's computational power roughly mimics mammalian reflexes that are driven by a neural response from the spine rather than the brain. The team was inspired by neural circuits found in animals, called central pattern generators, made of very simple elements that can generate rhythmic patterns to control motions like walking and running. To mimic the generator's functions, engineers built a system of valves that act as oscillators, controlling the order in which pressurized air enters air-powered muscles in the robot's four limbs. Researchers built an innovative component that coordinates the robot's gait by delaying the injection of air into the robot's legs. The robot's gait was inspired by side neck turtles. The robot is also equipped with simple mechanical sensors -- little soft bubbles filled with fluid placed at the end of booms protruding from the robot's body. When the bubbles are depressed, the fluid flips a valve in the robot that causes it to reverse direction. The Science Robotics paper builds on previous work by other research groups that developed oscillators and sensors based on pneumatic valves and add the components necessary to achieve high-level functions like walking.

New machine learning theory raises questions about nature of science

2021-01-31

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars. The algorithm, devised by a scientist at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop predictions. "Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations," said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. "What I'm doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law." Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a "serving algorithm," then made accurate predictions of the orbits of other planets in the solar system without using Newton's laws of motion and gravitation. "Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data," Qin said. "There is no law of physics in the middle." The program does not happen upon accurate predictions by accident. "Hong taught the program the underlying principle used by nature to determine the dynamics of any physical system," said Joshua Burby, a physicist at the DOE's Los Alamos National Laboratory who earned his Ph.D. at Princeton under Qin's mentorship. "The payoff is that the network learns the laws of planetary motion after witnessing very few training examples. In other words, his code really 'learns' the laws of physics." Machine learning is what makes computer programs like Google Translate possible. Google Translate sifts through a vast amount of information to determine how frequently one word in one language has been translated into a word in the other language. In this way, the program can make an accurate translation without actually learning either language. The process also appears in philosophical thought experiments like John Searle's Chinese Room. In that scenario, a person who did not know Chinese could nevertheless "translate" a Chinese sentence into English or any other language by using a set of instructions, or rules, that would substitute for understanding. The thought experiment raises questions about what, at root, it means to understand anything at all, and whether understanding implies that something else is happening in the mind besides following rules.

Emerging robotics technology may lead to better buildings in less time

2021-02-22

Emerging robotics technology may soon help construction companies and contractors create buildings in less time at higher quality and at lower costs. Purdue University innovators developed and are testing a novel construction robotic system that uses an innovative mechanical design with advances in computer vision sensing technology to work in a construction setting. The technology was developed with support from the National Science Foundation. "Our work helps to address workforce shortages in the construction industry by automating key construction operations," said Jiansong Zhang, an assistant professor of construction management technology in the Purdue Polytechnic Institute. "On a construction site, there are many unknown factors that a construction robot must be able to account for effectively. This requires much more advanced sensing and reasoning technologies than those commonly used in a manufacturing environment."

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

2021-07-16 - 2021-07

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

Newsletter

© 2024 All Rights Reserved by Longdom