AI Production Research Articles

Making green growth a reality: Reconciling sobriety with stakeholders' satisfaction.

The notion of sobriety is considered a key variable in various energy transition scenarios. Often associated with a form of punitive ecology, it is, nevertheless, possible to make it a component that supports green growth, by linking it to the concept of "satisfaction". In this work, we have invented a way to achieve both "digital", "economic", and "ecological" sobriety, while ensuring the satisfaction of the end user. Directly correlated to the production of goods or services, the satisfaction function is built on the well-documented marginal utility function, which measures the need (or not) to consume further resources to satisfy the economic agents. Hence, it is justified and exists because it stands for the expectations of end users and makes sure the latter is met. This product itself is a function of the allocation of a set of resources, mapped using activity-based costing tools (ABC method). In this work, we focus on an AI proof-of-concept and demonstrate that it is possible to reach numerical sobriety by controlling the size of the training dataset while ensuring roughly the same model performance. In general, we show that it is possible to preserve the efficiency of AI processes while significantly minimizing the need for resources. In this sense, after establishing an analytical model, we suggest reducing the amount of data required to train the machine learning (ML) models, while guaranteeing zero change in terms of performance (say their accuracy). We show that it affects the energy consumed, and, thereby, the associated cost (i.e., economic and ecological) and the associated CO2eq emission. We thus confirm the existence of a "triangle of sobriety". It is defined as a virtual circle governed by a digital-economic-ecological sovereignty. We also propose that if AI production processes have a potential for sobriety, all identical activities have the same characteristics, thus opening the path to green growth.

Research and Development of Machine Vision Algorithm Performance Evaluation System in Complex Scenes

In recent years, deep learning technology has gained widespread attention and has been successfully applied in various applications, including machine vision for automatic driving. However, one of the major challenges in this field is the ability of AI models to perform well in complex scenes with varying weather conditions and lighting conditions. The proposed scheme uses VR technology to simulate different weather conditions and lighting conditions and to evaluate the performance of AI products in such complex scenes. To further enhance the generalization capabilities of AI models, the Software Development Kit has been updated with complex scene data with configurable parameters, and training data with typical characteristics according to machine vision tasks. This approach has the potential to significantly improve the accuracy and reliability of machine vision systems for automatic driving, ultimately enhancing their safety and effectiveness.

Theory languages in designing artificial intelligence

The foundations of AI design discourse are worth analyzing. Here, attention is paid to the nature of theory languages used in designing new AI technologies because the limits of these languages can clarify some fundamental questions in the development of AI. We discuss three types of theory language used in designing AI products: formal, computational, and natural. Formal languages, such as mathematics, logic, and programming languages, have fixed meanings and no actual-world semantics. They are context- and practically content-free. Computational languages use terms referring to the actual world, i.e., to entities, events, and thoughts. Thus, computational languages have actual-world references and semantics. They are thus no longer context- or content-free. However, computational languages always have fixed meanings and, for this reason, limited domains of reference. Finally, unlike formal and computational languages, natural languages are creative, dynamic, and productive. Consequently, they can refer to an unlimited number of objects and their attributes in an unlimited number of domains. The differences between the three theory languages enable us to reflect on the traditional problems of strong and weak AI.

ChatGPT, a Product of AI, and its Influences in the Business World

Specifically the study analyzes how ChatGPT AI is transforming business operations and enhancing customer experience in customer service, e-commerce, healthcare, finance, marketing, and developer business sectors. Using a comprehensive literature review approach.The study explores the implications of ChatGPT AI for businesses, including its potential to improve efficiency, reduce costs, and enhance competitiveness. Additionally, ethical and legal considerations surrounding the use of ChatGPT AI in business operations are evaluated, including issues related to data privacy, bias, and transparency.The study provides insights into future directions for the application of ChatGPT AI in business and the potential impact of this technology on various business sectors. The objective is to provide a comprehensive review of the influence of ChatGPT AI on businesses, its potential benefits, and challenges for businesses looking to adopt this technology. Overall, this study highlights the importance of considering the benefits and limitations of AI adoption in businesses, as well as the ethical and legal implications of its use.

The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types

While artificial intelligence products are widely used in the market, their anthropomorphic appearance design is becoming a frontier issue in product strategy and consumer behavior research. The aim of this study was to investigate the influence of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. It was conducted in China, a new but rapidly-growing country in the field of Internet, AI technology and AI product consumption. This study conducted four situational experiments with a 2 (anthropomorphic design: anthropomorphic vs. non-anthropomorphic) × 2 (product type: hedonic vs. utilitarian) between subjects’ experimental design. Data was collected from 1172 Chinese “Digital Natives” by using a structured questionnaire. The findings revealed that for hedonic AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived entertainment, and intelligence level significantly moderates the mediating effect of perceived entertainment; while for practical AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived usefulness, and intelligence level does not significantly moderate the mediating effect of perceived usefulness. There is no significant moderating effect of intelligence level on perceived usefulness. The study contributes to development and validation of a more comprehensive understanding and theoretical foundation of anthropomorphism, and furthermore explores the impact of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. This study also provides insights for companies to apply anthropomorphic strategies.

Research on The Copyright Law of Artificial Intelligence Generation

With the continuous development and innovation of science and technology in the current era, at this point, artificial intelligence has been able to generate all kinds of "works" like human beings, which has produced many new intellectual property legal issues in the field of artificial intelligence, which also put forward new legal challenges for its protection. What kind of protection should be given to artificial intelligence products, its copyright, the ownership of the rights of products is the issue of this paper. At present, the author believes that two preconditions should be made clear: first, the "works" of artificial intelligence products are regarded as copyright-protected works from its legal qualitative understanding; Second, the copyright of AI products should be owned by AI investors and users. On this basis, relevant legal protection mechanisms should be established to reduce legal disputes caused by artificial intelligence products.

AI ethics as subordinated innovation network

AI ethics is proposed, by the Big Tech companies which lead AI research and development, as the cure for diverse social problems posed by the commercialization of data-intensive technologies. It aims to reconcile capitalist AI production with ethics. However, AI ethics is itself now the subject of wide criticism; most notably, it is accused of being no more than “ethics washing” a cynical means of dissimulation for Big Tech, while it continues its business operations unchanged. This paper aims to critically assess, and go beyond the ethics washing thesis. I argue that AI ethics is indeed ethics washing, but not only that. It has a more significant economic function for Big Tech. To make this argument I draw on the theory of intellectual monopoly capital. I argue that ethics washing is better understood as a subordinated innovation network: a dispersed network of contributors beyond Big Tech’s formal employment whose research is indirectly planned by Big Tech, which also appropriates its results. These results are not intended to render AI more ethical, but rather to advance the business processes of data-intensive capital. Because the parameters of AI ethics are indirectly set in advance by Big tech, the ostensible goal that AI ethics sets for itself—to resolve the contradiction between business and ethics—is in fact insoluble. I demonstrate this via an analysis of the latest trend in AI ethics: the operationalization of ethical principles.

Deficiencies and Upgrades in Artificial Intelligence Languages

In the era of artificial intelligence, people have started to have a lot of exposure to machine output language, such as online translation robot customer service, Siri, Microsoft Ice. However, these AI languages are still in the primary stage. Compared with human languages, AI languages still have many shortcomings, for example, lack of intentionality, lack of metaphorical ability. And people need to make some changes and improvements to make them reach a higher level of intelligence, so that AI languages can be closer to natural languages in various aspects, and AI products can better facilitate human production and life, as well as better serve society. This article is written around AI language, through the existing products of AI language as an example, analyzing AI language from different levels such as linguistics and philosophy, finding out the deficiencies of AI language and the aspects that need to be improved, and finally putting forward constructive opinions on how to make today's AI language reach a higher level of intelligence, that is, paying attention to the importance of metaphor in AI, and people should solve the problem of In order to solve the problem of "intelligence", the problem of "intelligence" needed to be solved first; people should not only deal with the problem of man and the world but also deal with the problem of man, nature and the imaginary world.

Persuading Patients Using Rhetoric to Improve Artificial Intelligence Adoption: Experimental Study

Artificial intelligence (AI) can transform health care processes with its increasing ability to translate complex structured and unstructured data into actionable clinical decisions. Although it has been established that AI is much more efficient than a clinician, the adoption rate has been slower in health care. Prior studies have pointed out that the lack of trust in AI, privacy concerns, degrees of customer innovativeness, and perceived novelty value influence AI adoption. With the promotion of AI products to patients, the role of rhetoric in influencing these factors has received scant attention. The main objective of this study was to examine whether communication strategies (ethos, pathos, and logos) are more successful in overcoming factors that hinder AI product adoption among patients. We conducted experiments in which we manipulated the communication strategy (ethos, pathos, and logos) in promotional ads for an AI product. We collected responses from 150 participants using Amazon Mechanical Turk. Participants were randomly exposed to a specific rhetoric-based advertisement during the experiments. Our results indicate that using communication strategies to promote an AI product affects users' trust, customer innovativeness, and perceived novelty value, leading to improved product adoption. Pathos-laden promotions improve AI product adoption by nudging users' trust (n=52; β=.532; P<.001) and perceived novelty value of the product (n=52; β=.517; P=.001). Similarly, ethos-laden promotions improve AI product adoption by nudging customer innovativeness (n=50; β=.465; P<.001). In addition, logos-laden promotions improve AI product adoption by alleviating trust issues (n=48; β=.657; P<.001). Promoting AI products to patients using rhetoric-based advertisements can help overcome factors that hinder AI adoption by assuaging user concerns about using a new AI agent in their care process.

Artificial Intelligent Systems and the Problem of “Natural” Trust

The development of AI technologies has heightened the problem of humanitarian challenges at all levels of social regulations. Ethical issues and, in particular, the problem of trust have become relevant to the field of high technology, given the fact that AI performs increasingly significant managerial functions that previously could only be performed by humans. This issue is directly related to artificial intelligence systems, which have already been embodied in specific extensive projects. In this study, the authors analyze the concept of trust through the prism of technological development. For this purpose, the study presents an overview of historical and contemporary interpretations of the concept of trust and proves that this concept is relevant and necessary to control the risks that arise when integrating AI products into social life. The authors show that a rethinking of the concepts of ethics and morality in the new context is required. This is a necessary requirement for the creation of trusted AI and for the achievement of trust in human interaction with technology products. The authors conclude that it is necessary to build an interdisciplinary dialogue to integrate theory and practice from numerous fields. To do this, it is necessary to create a common knowledge base and a platform for communication between all stakeholders, but it is also important to create favorable conditions for sustainable and constructive interaction. Therefore, trust is a relevant concept that needs to be constructed in a multidimensional frame of reference that targets different stakeholders and also takes into account interaction between human and technology, in other words, at all levels and on all scales.

Developing a Trustworthy AI Rating System and Its Impact on Customer Engagement

Since customers’ trust in products drives their engagement, the question of whether AI products can be trusted is critically important.

How to cite ChatGPT and AI products

In the context of increasing access to tools that use artificial intelligence, the outcome of debates about intellectual property is important because it allows us to set the limits in which we can appropriate or refer to a product we have created with the help of AI applications, constructed as original or derivative work, using in its turn, indiscriminately, a large amount of original works protected by copyright law as a source of inspiration. At this time, texts, images or sounds generated at your request by an artificial intelligence tool cannot be attributed to it. Content generated with artificial intelligence applications does not carry intellectual property restrictions – it can be used freely, for any legal means, including commercial use. The partial or exclusive contribution or intervention of artificial intelligence in the development of a text, image or any other product cannot be detected with certainty. To acknowledge the AI contribution, it is recommended to add a note in a visible place. Artificial intelligence is not ”concerned” about truth or originality, it is concerned with constructing a plausible product, generating the most likely sequel for each sequence of text, image or sound, using as a benchmark a database of similar digital products. Therefore, the result must be checked carefully. In this article: How do we access AI tools? Under what conditions do works made with the contribution of AI benefit from copyright protection? Does content generated with artificial intelligence applications have intellectual property restrictions? How to cite ChatGPT? How to cite Bing Chat? How to reference images generated with AI programs? Can content generated by AI applications be detected?

Usability Analysis of Text Generation by ChatGPT OpenAI Using System Usability Scale Method

The development of artificial intelligence systems has resulted in various AI products including ChatGPT, which is a new product classified as a chatbot. This research aims to ensure that text generation systems such as ChatGPT open AI have the best level of quality and usability and are able to provide a satisfying experience for users. To measure and evaluate the effectiveness, efficiency and user satisfaction of the ChatGPT platform, researchers used the System Usability Scale (SUS) method. This data collection was carried out using an online questionnaire. After the collected data has been tested for validity and reliability, the researchers then analyzed the data results. From the results of the research conducted, the SUS value of the ChatGPT platform is 67.44. This score is included in the marginal high category of class D, with a reasonable or sufficient interpretation. With the results of the analysis per question item, it shows that users tend to agree that the system runs quite effectively, efficiently, well and is easy to understand. Although ChatGPT is able to perform tasks or commands well. However, it should be noted that not all information loaded by ChatGPT is presented in a complete, current and correct manner. This is because the information presented by ChatGPT is only limited to 2021. Because ChatGPT is a new technology and is still under development, further researchers are expected to test other features or ChatGPT to ensure the stability and reliability of the entire ChatGPT system using other research methods.

Investment Value in China’s Artificial Intelligence Industry

At present, various AI products are increasingly connected with people, playing a crucial role in their lives. It can be seen from the report on China's artificial intelligence industry that the market size of China's AI market is growing rapidly with great development potential. Moreover, investing in China’s AI market is also the top choice for numerous investors. Hence, this paper focuses on studying the development trend and potential problems of companies in China's AI market, and eliciting relevant investment suggestions, through analyzing three representative China firms- iFLYTEK, Baidu, and HUNDSUN. To analyze, this research uses the CAPM model and the calculation formula of WACC to calculate the WACC, and evaluate their profitability by calculating their gross profit margin and net income margin respectively. From the results, it can be seen there exists profitability issue of iFLYTEK and Baidu, due to the main factor that they are trying to develop in new fields resulting in high R&amp;D expenses and selling expenses generated without a significant result in their revenue. The profitability of HUNDSUN seems optimistic, yet its R&amp;D expenses and COGS are also increasing significantly in 2021, which are used to maintain the market share of core businesses, develop new businesses and upgrade core technologies. For those companies, they diversify their business structure for further growth opportunities due to their core business markets are close to saturation; however, this will also increase their business risk. Currently, in conclusion, the great development chances of China’s AI market coexist with risks.

From the Classroom to the Newsroom: A Critical Route to Introduce AI in Journalism Education

From a computer vision application to monitor elections transparency in Argentina to automated real-state texts in Norway, and everything in between, Artificial Intelligence-powered tools are changing journalism. Scholars have taken note, and the academic production of AI in journalism has gained considerable ground in the last five years. However, research on how journalism education deals with AI influence in the industry is scarce. Based on a self-training method using available online free courses for journalists and a review of university teaching initiatives, this article proposes key elements to trace teaching trajectories to introduce AI into journalism curriculum. Included are recommendations for drawing a path to teaching journalism students to think critically about AI and, at the same time, to understand the available tools for reporting and investigating in a complex context where journalism lives in a profound state of crisis.

AI ageism: a critical roadmap for studying age discrimination and exclusion in digitalized societies

In the last few years, we have witnessed a surge in scholarly interest and scientific evidence of how algorithms can produce discriminatory outcomes, especially with regard to gender and race. However, the analysis of fairness and bias in AI, important for the debate of AI for social good, has paid insufficient attention to the category of age and older people. Ageing populations have been largely neglected during the turn to digitality and AI. In this article, the concept of AI ageism is presented to make a theoretical contribution to how the understanding of inclusion and exclusion within the field of AI can be expanded to include the category of age. AI ageism can be defined as practices and ideologies operating within the field of AI, which exclude, discriminate, or neglect the interests, experiences, and needs of older population and can be manifested in five interconnected forms: (1) age biases in algorithms and datasets (technical level), (2) age stereotypes, prejudices and ideologies of actors in AI (individual level), (3) invisibility of old age in discourses on AI (discourse level), (4) discriminatory effects of use of AI technology on different age groups (group level), (5) exclusion as users of AI technology, services and products (user level). Additionally, the paper provides empirical illustrations of the way ageism operates in these five forms.

The AI field needs translational Ethical AI research

AbstractCalls for Ethical AI have become urgent and pervasive, especially as ethical issues surrounding AI products at tech companies are increasingly scrutinized by the public. Yet even after a first wave of responses to these calls coalesced around Ethical AI principles to guide decision‐making and a second wave generated technical tools to mitigate specific ethical issues, multiple lines of evidence indicate that these Ethical AI principles and technical tools have only a limited impact on the daily practices of AI users and producers. In other words, there is a big gap between what we publish in academic papers and what AI creators need to generate AI products that reflect society's values. Ethical AI is by no means the only field to have this problem. However, when medical and ecology fields documented similar gaps between their fields’ scientific discoveries and the practices and products that people actually use, they invested tremendous resources into subfields that developed evidence about how to translate what was done in the lab to adopted solutions. I argue in this commentary that it is our research community's moral duty to invest in our own subfield of “Translational Ethical AI” that will determine how best to ensure AI practitioners can implement the Ethical AI technical tools we publish in academic venues in production settings. Further, I offer concrete steps for doing that, drawing on insights gleaned from other translational fields. Closing the “Ethical AI Publication‐to‐Practice gap” will be a considerable transdisciplinary challenge, but one of the AI research community has the unique expertise, political leverage, and moral responsibility to tackle.

Motivation, Definition, Application and the Future of Edge Artificial Intelligence

The term " Edge Artificial Intelligence (Edge AI)" refers to the part of a network where data is analysed and aggregated. Dispersed networks, such as those found in the Internet of Things (IoT), have enormous ramifications when it comes to "Edge AI," or "intelligence at the edge". Smartphone applications like real-time traffic data and facial recognition data, including semi-autonomous smart devices and automobiles are integrated in this class. Edge AI products include wearable health monitors, security cameras, drones, robots, smart speakers and video games. Edge AI was established due to the marriage of Artificial Intelligence with cutting Edge Computing (EC) systems. Edge Intelligence (EI) is a terminology utilized to define the model learning or the inference processes, which happen at the system edge by employing available computational resources and data from the edge nodes to the end devices under cloud computing paradigm. This paper provides a light on "Edge AI" and the elements that contribute to it. In this paper, Edge AI's motivation, definition, applications, and long-term prospects are examined.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

A Caution against the Artificialistic Fallacy

The casual justification of the influence of a technology, particularly artificial intelligence, by appeal to the existence of the technology constitutes an artificialistic fallacy, analogous to the naturalistic fallacy that is well-known in philosophy. Similar to an invocation of nature to provide moral warrant (the naturalistic fallacy), modern tech evangelists invoke the burgeoning of hardware and software products in order to promote that burgeoning (the artificialistic fallacy). This fallacy is often tacit or committed by omission. Emerging ethical initiatives emphasize the refinement, explanation, and oversight of AI products rather than their fundamental ethical effect, making the fallacy recursive.