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The Impact of Artificial Intelligence on the Creativity of Videos
Abstract
This study explored the impact Artificial Intelligence (AI) has on the evaluation of creative elements in artistic videos. The aim was to verify to what extent the use of an AI algorithm (Style Transfer) contributes to changes in the perceived creativity of the videos. Creativity was evaluated in six quantitative items (Likert-type scale) and one qualitative question (qualitative description of the creativity expressed in the video by two words or expressions). Six videos were shown to both control (N = 49) and experimental group (N = 52) aiming at determining possible differences in creativity assessment criteria. Furthermore, both groups contained experts (Experimental, N = 27; Control, N = 25) and non-experts (Experimental, N = 25; Control, N = 24). The first round of videos composed of six videos that were the same for both the experimental and control condition (used to check for bias). No significant differences were found. In a second round, six videos were shown with AI transformation (experimental condition) and without that transformation (control group). Results showed that in two cases the perceived creativity increased in experimental condition, in one case a decrease occurred. In most evaluations no differences were observed. Qualitative evaluations reinforce the absence of a general pattern of improvements in AI transformations. Altogether, the results emphasize the importance of human mediation in the application of AI in creative production: a hybrid approach, or rather, Hybrid Intelligence.
... The academic lens has primarily focused on the optimistic spectrum of AI's intervention in the educational landscape, especially its capability to customize learning, its instrumental role in administrative automation, and the enhancement of pedagogic efficacy [1,[4][5][6][7][8][9][10][11][12][13][14]. Yet, the prevailing discourse has tended to understate, or at times, elide the complexities and potential pitfalls that AI's integration brings to educational settings. ...
... AI is often lauded for its advanced capabilities, substituting for human cognition in activities that entail complex thinking, problemsolving, and analytical reasoning [10]. AI systems strive to simulate nuanced human cognitive processes, encompassing critical thinking, comprehension, abstraction, and adaptive learning [10,13]. In the realm of education, AI's imprint can be found in a variety of technologies such as intelligent tutoring systems, adaptive learning environments, conversational agents, automated evaluative tools, and sophisticated data analytical instruments [3][4][5][6][7][8]. ...
... Regarding current occupation, the majority are students (169, 65.%), followed by faculty members (59, 22.7 %) and administrators (32, 12.3 %). In terms of educational level, most respondents hold Bachelor's degrees (190, 73.1 %), followed by Ph.D. holders (57, 21.9 %) and Master's degree holders (13,5.%). Self-assessed AI expertise is distributed as follows: low (98, 37.7 %), medium (122, 46.9 %), and high (40, 15.4 %). ...
Despite the wave of enthusiasm for the role of Artificial Intelligence (AI) in reshaping education, critical voices urge a more tempered approach. This study investigates the less-discussed 'shadows' of AI implementation in educational settings, focusing on potential negatives that may accompany its integration. Through a multi-phased exploration consisting of content analysis and survey research, the study develops and validates a theoretical model that pinpoints several areas of concern. The initial phase, a systematic literature review, yielded 56 relevant studies from which the model was crafted. The subsequent survey with 260 participants from a Saudi Arabian university aimed to validate the model. Findings confirm concerns about human connection, data privacy and security, algorithmic bias, transparency, critical thinking, access equity, ethical issues, teacher development, reliability, and the consequences of AI-generated content. They also highlight correlations between various AI-associated concerns, suggesting intertwined consequences rather than isolated issues. For instance, enhancements in AI transparency could simultaneously support teacher professional development and foster better student outcomes. Furthermore, the study acknowledges the transformative potential of AI but cautions against its unexamined adoption in education. It advocates for comprehensive strategies to maintain human connections, ensure data privacy and security, mitigate biases, enhance system transparency, foster creativity, reduce access disparities, emphasize ethics, prepare teachers, ensure system reliability, and regulate AI-generated content. Such strategies underscore the need for holistic policymaking to leverage AI's benefits while safeguarding against its disadvantages.
... Can creativity be quantified, or is it inherently subjective? As generative AI systems become more sophisticated, they challenge traditional definitions of creativity, prompting a reevaluation of what it means to be creative in a world where machines can generate art, music, and literature (Rebelo et al., 2022). This exploration is essential not only for artists and creators but also for educators and policymakers who must navigate the implications of AI in creative fields (Solanke, 2024). ...
... Additionally, transformer models, such as OpenAI's GPT series, have revolutionized text generation by leveraging large-scale datasets to produce coherent and contextually relevant narratives (Tlili et al., 2023) The implications of generative AI extend beyond mere content creation; they also encompass the potential for enhancing human creativity. By providing tools that assist in brainstorming, prototyping, and refining ideas, generative AI can serve as a collaborative partner in the creative process (Rebelo et al., 2022). This partnership allows creators to explore new avenues of expression and innovation, ultimately leading to a richer and more diverse creative landscape (Almheiri et al., 2025). ...
This essay explores the transformative impact of generative AI models on human creativity. As artificial intelligence continues to advance, creative fields such as art, music, and writing are being significantly influenced by AI-driven tools that generate original content. The essay examines how generative models, such as GPT and DALL·E, challenge traditional boundaries of human creativity by producing works that mimic or even surpass human-created pieces in some aspects. It discusses the implications of AI’s role in the creative process, including ethical concerns, the potential for collaboration between humans and machines, and the evolving definition of creativity itself. By analyzing current trends and innovations, this paper provides insights into the future of creativity in a world where artificial intelligence is becoming an integral part of the creative landscape.
... while AI has made significant strides, there remains a strong preference for human designers in the realm of innovation. This preference is rooted in the traditional view that associates creativity with human uniqueness and the intrinsic capacity to innovate (Eshraghian, 2020;Rebelo et al., 2022;. Moreover, human response to AI design mostly depends on the product type, consumers believe that AI design may experience more success designing utilitarian products (Longoni & Cian, 2022). ...
... All participants were asked to evaluate the given design on the same set of dependent variables. The dependent variables were chosen from the study of Hong and Curran (2019) and Rebelo et al. (2022), which consist of criteria related to originality of the composition, level of innovation, composition, outstanding artistic style, aesthetic quality, value of the concept or idea, and expressiveness. Participants were shown a style of design with three representative creative designs a time and then asked to evaluate that design according to seven criteria for creativity (Cronbach's α = 0.82), each measured on a 5-point Likert scale (1=Lowest, 5=Highest). ...
... The application of artificial intelligence is increasingly playing a crucial role in creative production [163]. Video style transfer, as a creative and artistic technology, will continue to attract the attention of researchers and application developers. ...
Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such as the balance between style and content, the model generalization ability, and diversity. This article first introduces the origin and development process of style transfer and provides a brief overview of existing methods. Next, this article explores research work related to style transfer, introduces some metrics used to evaluate the effect of style transfer, and summarizes datasets. Subsequently, this article focuses on the application of the currently popular deep learning technology for style transfer and also mentions the application of style transfer in video. Finally, the article discusses possible future directions for this field.
... Искусственный интеллект и креативность приводить к снижению креативности и уникальности итогового творческого результата. В экспериментальном исследовании применения ИИ в процессе разработки творческих видеороликов было обнаружено, что на данном этапе развития технологий ИИ его использование не гарантирует высокого качества результата (Rebelo et al., 2022). Автор приходит к выводу, что наиболее эффективным способом будет гибридный формат его применения, в рамках которого человек использует ИИ в качестве одного из инструментов творческой деятельности, но критически подходит к результатам работы генеративной модели и привносит в итоговый продукт свои собственные творческие идеи. ...
The article focuses on ways to use artificial intelligence in assessment and enhancement of creativity. This topic seems very important in the context of the intensive development of computer technologies providing people with the vast range of opportunities to improve their professional skills and intensify their personal development. Some particular ways of the use of artificial intelligence are analyzed. Artificial intelligence can operate independently and generate its own creative ideas. At the same time, it can interact with humans within the creative process or serve as a “creative assistance” of humans. The results of the empirical studies in this area showed that the efficacy of the artificial intelligence in the course of assessment and enhancement of human creativity is determined to considerable extent by a task given, a particular area which artificial intelligence operates in, and the specific forms of its interactions with humans. In some areas (e.g., generation of alternative uses), artificial intelligence can outperform humans, whereas in other tasks (e.g., creative writing) humans perform better that artificial intelligence. Some practical recommendations on how to optimize the use of artificial intelligence in assessment and enhancement of creativity, were proposed. Results of the study can be used in the development of creativity assessment methods as well as for the improvement of interaction between people and artificial intelligence.
... They seem to believe that although AI may tend to produce stylistically and content-wise uniform outputs; it also can introduce new creative elements and not become a bottleneck for innovation. This trust in AI reflects a viewpoint: AI should be seen as an assistant in the creative process, handling technical and repetitive tasks, while human creators still hold the core of innovation and artistic expression [76,77]. ...
In recent years, the rapid advancement of artificial intelligence (AI) in the video sector has captured widespread attention. Despite the proliferation of AI video generation tools targeted at general consumers, there remains a gap in addressing the specialized requirements and advanced functionalities sought by industry professionals. This study aims to identify and overcome the barriers hindering the adoption of AI video tools in the Chinese video industry, thereby facilitating their seamless integration. The research unfolded in two phases employing a comprehensive methodology. Initially, we delineated the industry’s video usage standards, drawing upon both established norms and insights gleaned from seasoned industry experts through focus group interviews. These insights informed the formulation of semi-structured interview questions. Subsequently, in-depth semi-structured interviews were conducted with ten Chinese industry experts, leading to the identification of eight primary adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global perspectives and localization. In the second phase, a detailed questionnaire survey involving 401 Chinese industry practitioners validated these factors. A data analysis underscored the significant impact of these eight factors on industry adoption, particularly emphasizing technological maturity. Furthermore, exhaustive examinations and discussions were undertaken for each identified barrier. The findings of this study theoretically bridge the gaps in understanding the impediments to the application of AI video generation tools in the video industry. They offer valuable insights into the current application landscape and furnish pertinent suggestions for advancing technology promotion and development in the future. Ultimately, this research aspires to augment the integration and utilization of AI technology within the Chinese video production industry, thereby propelling its progress and development forward.
... The meaning of 'rules' is further clarified by Wiggins (2006) which is reasoning to an inference within the search. Conceptual spaces are explained as the gather of creation of new ideas (Rebelo et al., 2022). Based on the definition of E-creativity, it can be identified that E-creativity is a special form of creativity. ...
Marketing and advertising are two of the many industries that are significantly impacted by rapid advancements in artificial intelligence (ai). With the launch of sora, ai has changed the game of video captioning and generating text-based videos. sora is an advanced generative artificial intelligence (gai) that can create smooth, high-quality and creative videos from text descriptions, making it one of the most cutting-edge gai technologies available today. this study aims to comprehensively analyze the impact of sora ai on jobs in the marketing and advertising sectors. this review thoroughly examines the current trends, challenges and opportunities to shed light on the revolutionary impact of sora ai on job roles, skill requirements and the marketing and advertising landscape. sora ai leverages machine-learning algorithms to analyze vast amounts of consumer data, predict trends and optimize advertising strategies in real time. the findings of the study suggest that sora ai can replace humans in specific jobs, but it also opens new job opportunities. While some routine jobs may be automated, new opportunities are emerging in ai development, ethical ai use and human-ai collaboration. it increases efficiency across jobs, highlighting the need to acquire new skills and earn money, recently emerging in the age of ai in sora.
Marketing and advertising are two of the many industries that are significantly impacted by rapid advancements in artificial intelligence (AI). With the launch of Sora, AI has changed the game of video captioning and generating text-based videos. Sora is an advanced Generative Artificial Intelligence (GAI) that can create smooth, high-quality and creative videos from text descriptions, making it one of the most cutting-edge GAI technologies available today. This study aims to comprehensively analyze the impact of Sora AI on jobs in the marketing and advertising sectors. This review thoroughly examines the current trends, challenges and opportunities to shed light on the revolutionary impact of Sora AI on job roles, skill requirements and the marketing and advertising landscape. Sora AI leverages machine-learning algorithms to analyze vast amounts of consumer data, predict trends and optimize advertising strategies in real time. The findings of the study suggest that Sora AI can replace humans in specific jobs, but it also opens new job opportunities. While some routine jobs may be automated, new opportunities are emerging in AI development, ethical AI use and human-AI collaboration. It increases efficiency across jobs, highlighting the need to acquire new skills and earn money, recently emerging in the age of AI in Sora.
The AI-generated inspirational images provided a new pathway for design learners to obtain inspiration, but previous research had overlooked the impact of the emotional valence and arousal of AI-generated images on design creativity. This study investigated the effects of emotional valence and arousal of AI-generated images on design creativity through two experiments. Experiment 1 examined the impact of emotional valence (positive, neutral, negative) of AI-generated images on design creativity, while Experiment 2 further analyzed the impact of arousal levels (high, low) of AI-generated images and design problem types (conceptual design problems, knowledge design problems) on design creativity under positive emotional conditions. The results showed that AI-generated images with positive emotional valence facilitated divergent thinking and enhanced the flexibility and originality of creative ideas. In knowledge design problems, AI-generated images with positive emotional valence and high arousal levels enhanced the originality of creative ideas. However, in conceptual design problems, the arousal levels of AI-generated images with positive emotional valence had no significant impact on design creativity. These findings provided insights into how design learners could utilize AI assistance in design.
The rapid popularity of ChatGPT has brought generative AI into broad focus. The content generation model represented by AI-generated content (AIGC) has reshaped the advertising industry. This study explores the mechanisms by which the characteristics of AI-generated advertisements affect consumers’ willingness to accept these advertisements from the perspectives of perceived eeriness and perceived intelligence. It found that the verisimilitude and imagination of AI-generated advertisements negatively affect the degree of perceived eeriness by consumers, while synthesis positively affects it. Conversely, verisimilitude, vitality, and imagination positively affect the perceived intelligence, while synthesis negatively affects it. Meanwhile, consumers’ perceived eeriness negatively affects their acceptance of AI-generated advertisements, while perceived intelligence positively affects their willingness to accept AI-generated advertisements. This study helps explain consumers’ attitudes toward AI-generated advertisements and offers strategies for brands and advertisers for how to use AI technology more scientifically to optimize advertisements. Advertisers should cautiously assess the possible impact of AI-generated advertisements according to their characteristics, allowing generative AI to play a more valuable role in advertising.
Changing the style of an image/video while preserving its content is a crucial criterion to access a new neural style transfer algorithm. However, it is very challenging to transfer a new map art style to a certain video in which “content” comprises a map background and animation objects. In this paper, we present a novel comprehensive system that solves the problems in transferring map art style in such video. Our system takes as input an arbitrary video, a map image, and an off-the-shelf map art image. It then generates an artistic video without damaging the functionality of the map and the consistency in details. To solve this challenge, we propose a novel network, Map Art Video Network (MAViNet), the tailored objective functions, and a rich training set with rich animation contents and different map structures. We have evaluated our method on various challenging cases and many comparisons with those of the related works. Our method substantially outperforms state-of-the-art methods in terms of visual quality and meets the mentioned criteria in this research domain.
Keywords: artificial intelligence, deep learning, computational creativity. Abstract: While the potential of deep learning(DL) for automating simple tasks is already well explored, recent research has started investigating the use of deep learning for creative design, both for complete artifact creation and supporting humans in the creation process. In this paper, we use insights from computational creativity to conceptualize and assess current applications of generative deep learning in creative domains identified in a literature review. We highlight parallels between current systems and different models of human creativity as well as their shortcomings. While deep learning yields results of high value, such as high-quality images, their novelty is typically limited due to multiple reasons such a being tied to a conceptual space defined by training data. Current DL methods also do not allow for changes in the internal problem representation, and they lack the capability to identify connections across highly different domains, both of which are seen as major drivers of human creativity.
The thinking power of Homo sapiens made human beings the lord of all creation. The ability to reason is also the premise of human existence. We, however, now know that this is not confined only to human but to Artificial Intelligence. Over the history of humankind, human beings have attempted to create an immortal being that could surpass their abilities and complements their inferiorities. We are making something immortal and transcendent, which are different properties from our own. Artificial Intelligence may be able to evolve on its own like humans have been doing. As a kind of numerical being, humans are able to be omnipresent with the technology provided. This new kind of existence makes us think about and see things differently. Humans are attempting to create ‘beings’ that can generate art, take care of weak human beings, talk and discuss human issues, and even fall in love with humans. As our minds can run beyond the boundaries created by our body limitations, we would like to infuse our creativity into AI that might evolve from its original state. Similar to what Prometheus did, humans are attempting to share their legacy with another existence. Recently a research team from Rutgers University in New Jersey proposed a system named CAN: Creative Adversarial Networks for generating art with creative characteristics. The team demonstrated a realization of this system based on a novel, creative adversarial network. Their proposed system possesses the ability to produce novel artworks which make people believe human artists produced them. The data the team proposes proves that AI now attempts to do something considered as a creative activity. With this research, the definition of art should be reconsidered. Since the Fountain(1917) by Duchamp, open concepts toward artworks have been embraced by many artists and their colleagues. However, it is time to contemplate the new phase. When we regard something as artwork, should it be created, selected, and combined by human beings? Is it possible that the thing that is accepted as artwork by people can be art? This paper seeks to propose several opinions regarding these questions. Article received: June 23, 2019; Article accepted: July 6, 2019; Published online: October 15, 2019; Review articleHow to cite this article: Park, Yeonsook. "Can Artworks by Artificial Intelligence be Artworks?" AM Journal of Art and Media Studies 20 (2019): 113-121. doi: 10.25038/am.v0i20.332
This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within a review of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, and how these fears connect to the idea of an audio uncanny valley. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonization made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and broader assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) The current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”.
The aim of this article was to review the definitions of creativity on which many previous studies have been based. Prior literature has merged creativity and creation into its understanding of the construct. By describing creativity with reference to its end result, that is, a creative outcome, theorists have not been able to pay attention to the dynamic process of creativity that may or may not lead to creation. This article reviews critical literature in creativity and departs from accepted definitions of creativity. Considering the various elements that are involved in creative acts, a dynamic definition is proposed; it emphasizes providing insights into the creative act itself, a factor that has been neglected in earlier definitions of creativity.
The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at:
https://osf.io/f8tu4/.
Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improve- ments in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises that underlie these advancements is that machines will 1 day be capable of performing complex tasks or may even supersede humans in performing these tasks. This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017). While previous research has proved that AI performs well in some clearly defined tasks such as playing chess, playing Go or identifying objects on images, it is doubted that the development of an artificial general intelligence (AGI) which is able to solve multiple tasks at the same time can be achieved in the near future (e.g., Russell and Norvig 2016). Moreover, the use of AI to solve complex business problems in organizational con- texts occurs scarcely, and applications for AI that solve complex problems remain mainly in laboratory settings instead of being implemented in practice.
Since the road to AGI is still a long one, we argue that the most likely paradigm for the division of labor between humans and machines in the next decades is Hybrid Intelligence. This concept aims at using the complementary strengths of human intelligence and AI, so that they can perform better than each of the two could separately (e.g., Kamar2016).
The question of whether machines can make art provokes very different answers from pioneers in the field. Harold Cohen refuses to ascribe creativity to his art-making robot AARON, while Leonel Moura argues that since his “Artbots” generate pictures from emergent properties that could not have been predicted by their creator, “they have at least some degree of creativity.” Although the question of whether machines can be artists seems to fall squarely on our definition of the latter, a solution to this philosophical impasse may ironically lie in redirecting the question away from the artist and toward the viewer.
Our essay discusses an AI process developed for making art (AICAN), and the issues AI creativity raises for understanding art and artists in the 21st century. Backed by our training in computer science (Elgammal) and art history (Mazzone), we argue for the consideration of AICAN’s works as art, relate AICAN works to the contemporary art context, and urge a reconsideration of how we might define human and machine creativity. Our work in developing AI processes for art making, style analysis, and detecting large-scale style patterns in art history has led us to carefully consider the history and dynamics of human art-making and to examine how those patterns can be modeled and taught to the machine. We advocate for a connection between machine creativity and art broadly defined as parallel to but not in conflict with human artists and their emotional and social intentions of art making. Rather, we urge a partnership between human and machine creativity when called for, seeing in this collaboration a means to maximize both partners’ creative strengths.
This article reviews the development of the author’s computational art practice, where the computer is used both as a device that provides the medium for generation of art (‘computer as art’) as well as acting actively as an assistant in the process of creating art (‘computer as artist’s assistant’), helping explore the space of possibilities afforded by generative systems. Drawing analogies with Kasparov’s Advanced Chess and the deliberate development of unstable aircraft using fly-by-wire technology, the article argues for a collaborative relationship with the computer that can free the artist to more fearlessly engage with the challenges of working with emergent systems that exhibit complex unpredictable behavior. The article also describes ‘Species Explorer’, the system the author has created in response to these challenges to assist exploration of the possibilities afforded by parametrically driven generative systems. This system provides a framework to allow the user to use a number of different techniques to explore new parameter combinations, including genetic algorithms, and machine learning methods. As the system learns the artist’s preferences the relationship with the computer can be considered to change from one of assistance to collaboration.
Throughout history innovation has been conceived, defined, interpreted and understood in different ways but what is it? This study looks at innovation starting with a brief history of innovation. It then looks at a sample of the multiple definitions that there are of innovation throughout the literature and from these develops a composite definition. From this composite definition key components such as the creative process and academic models of creativity are looked at. The research then looks at the applicability of innovation through highlighting two studies carried out in England of innovation being applied within a social housing organisation. Through the application by a two dimensional typology of social innovation they had identified innovation being applied to new services and improvements to existing services. The research concludes that innovation can be identified with the creation of a new product or service or an improvement of an existing product or service.
In this wide-ranging essay, the leader of Google’s Seattle AI group and founder of the Artists and Machine Intelligence program discusses the long-standing and complex relationship between art and technology. The transformation of artistic practice and theory that attended the 19th century photographic revolution is explored as a parallel for the current revolution in machine intelligence, which promises not only to mechanize (or democratize) the means of reproduction, but also of production.
International Journal of Exercise Science 8(3): 297-302, 2015. Likert, Likert-type, and ordinal-scale responses are very popular psychometric item scoring schemes for attempting to quantify people's opinions, interests, or perceived efficacy of an intervention and are used extensively in Physical Education and Exercise Science research. However, these numbered measures are generally considered ordinal and violate some statistical assumptions needed to evaluate them as normally distributed, parametric data. This is an issue because parametric statistics are generally perceived as being more statistically powerful than non-parametric statistics. To avoid possible misinterpretation, care must be taken in analyzing these types of data. The use of visual analog scales may be equally efficacious and provide somewhat better data for analysis with parametric statistics.
Abstract intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the Generic Abstract Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract intelligence theories, thethe compatibilitycompatibility ofof naturalnatural andand machinemachine intelligenceintelligence isis rrevealedevealed in order to investigate into a wide range of paradigms of abstract intelligence such as natural, artificial, machinable intelligence, and their engineering applications.
Likert questionnaires are widely used in survey research, but it is unclear whether the item data should be investigated by means of parametric or nonparametric procedures. This study compared the Type I and II error rates of the t test versus the Mann-Whitney-Wilcoxon (MWW) for five-point Likert items. Fourteen population distributions were defined and pairs of samples were drawn from the populations and submitted to the t test and the t test on ranks, which yields the same results as MWW. The results showed that the two tests had equivalent power for most of the pairs. MWW had a power advantage when one of the samples was drawn from a skewed or peaked distribution. Strong power differences between the t test and MWW occurred when one of the samples was drawn from a multimodal distribution. Notably, the Type I error rate of both methods was never more than 3% above the nominal rate of 5%, even not when sample sizes were highly unequal. In conclusion, for five-point Likert items, the t test and MWW generally have similar power, and researchers do not have to worry about finding a difference whilst there is none in the population. Likert scales are widely used in various domains such as behavioral sciences, healthcare, marketing, and usability research. When responding to a Likert scale, participants specify their level of agreement to statements with typically five or seven ordered response levels. Likert item data have distinct characteristics: discrete instead of continuous values, tied numbers, and restricted range.
Purpose:
The Bonferroni correction adjusts probability (p) values because of the increased risk of a type I error when making multiple statistical tests. The routine use of this test has been criticised as deleterious to sound statistical judgment, testing the wrong hypothesis, and reducing the chance of a type I error but at the expense of a type II error; yet it remains popular in ophthalmic research. The purpose of this article was to survey the use of the Bonferroni correction in research articles published in three optometric journals, viz. Ophthalmic & Physiological Optics, Optometry & Vision Science, and Clinical & Experimental Optometry, and to provide advice to authors contemplating multiple testing.
Recent findings:
Some authors ignored the problem of multiple testing while others used the method uncritically with no rationale or discussion. A variety of methods of correcting p values were employed, the Bonferroni method being the single most popular. Bonferroni was used in a variety of circumstances, most commonly to correct the experiment-wise error rate when using multiple 't' tests or as a post-hoc procedure to correct the family-wise error rate following analysis of variance (anova). Some studies quoted adjusted p values incorrectly or gave an erroneous rationale.
Summary:
Whether or not to use the Bonferroni correction depends on the circumstances of the study. It should not be used routinely and should be considered if: (1) a single test of the 'universal null hypothesis' (Ho ) that all tests are not significant is required, (2) it is imperative to avoid a type I error, and (3) a large number of tests are carried out without preplanned hypotheses.
States that both the popular creativity tests, such as the Torrance Tests of Creative Thinking, and the subjective assessment techniques used in some previous creativity studies are ill-suited to social psychological studies of creativity. A consensual definition of creativity is presented, and as a refinement of previous subjective methods, a reliable subjective assessment technique based on that definition is described. The results of 8 studies testing the methodology in elementary school and undergraduate populations in both artistic and verbal domains are presented, and the advantages and limitations of this technique are discussed. The present methodology can be useful for the development of a social psychology of creativity because of the nature of the tasks employed and the creativity assessments obtained. Creativity assessment is discussed in terms of the divergent aims and methods of personality psychology and social psychology. (46 ref)
Purpose
– The purpose of this paper is first, to discuss the theoretical assumptions, qualities, problems and myopia of the dominating quantitative and qualitative approaches; second, to describe the methodological lessons that the authors learned while conducting a series of longitudinal studies on the use and usefulness of a specialized balanced scorecard; and third, to encourage researchers to actually use multiple methods and sources of data to address the very many accounting phenomena that are not fully understood.
Design/methodology/approach
– This paper is an opinion piece based on the authors' experience conducting a series of longitudinal mixed method studies.
Findings
– The authors suggest that in many studies, using a mixed method approach provides the best opportunity for addressing research questions.
Originality/value
– This paper provides encouragement to those who may wish to bridge the authors' ideological gaps and to those who are actively trying to do so.
Abstract intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the Generic Abstract Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract intelligence theories, the compatibility of natural and machine intelligence is revealed in order to investigate into a wide range of paradigms of abstract intelligence such as natural, artificial, machinable intelligence, and their engineering applications.
Creativity isn't magical. It's an aspect of normal human intelligence, not a special faculty granted to a tiny elite. There are three forms: combinational, exploratory, and transformational. All three can be modeled by AI - in some cases, with impressive results. AI techniques underlie various types of computer art. Whether computers could "really" be creative isn't a scientific question but a philosophical one, to which there's no clear answer. But we do have the beginnings of a scientific understanding of creativity. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.
We develop a model of scientific creativity and test it in the field of rare diseases. Our model is based on the results of
an in-depth case study of the Rett Syndrome. Archival analysis, bibliometric techniques and expert surveys are combined with
network analysis to identify the most creative scientists. First, we compare alternative measures of generative and combinatorial
creativity. Then, we generalize our results in a stochastic model of socio-semantic network evolution. The model predictions
are tested with an extended set of rare diseases. We find that new scientific collaborations among experts in a field enhance
combinatorial creativity. Instead, high entry rates of novices are negatively related to generative creativity. By expanding
the set of useful concepts, creative scientists gain in centrality. At the same time, by increasing their centrality in the
scientific community, scientists can replicate and generalize their results, thus contributing to a scientific paradigm.
This study examines how people perceive artwork created by artificial intelligence (AI) and how presumed knowledge of an artist's identity (Human vs AI) affects individuals' evaluation of art. Drawing on Schema theory and theory of Computers Are Social Actors (CASA), this study used a survey-experiment that controlled for the identity of the artist (AI vs. Human) and presented participants with two types of artworks (AI-created vs. Human-created). After seeing images of six artworks created by either AI or human artists, participants (n=288) were asked to evaluate the artistic value using a validated scale commonly employed among art professionals. The study found that human-created artworks and AI-created artworks were not judged to be equivalent in their artistic value. Additionally, knowing that a piece of was created by AI did not in general influence participants' evaluation of art pieces' artistic value. However, having a schema that AI cannot make art significantly influenced evaluation. Implications of the findings for application and theory are discussed.
Ideation is a source of innovation and creativity, and is commonly used in early stages of engineering design processes. This paper proposes an integrated approach for enhancing design ideation by applying artificial intelligence and data mining techniques. This approach consists of two models, a semantic ideation network and a visual concepts combination model, which provide inspiration semantically and visually based on computational creativity theory. The semantic ideation network aims to provoke new ideas by mining potential knowledge connections across multiple knowledge domains, and this was achieved by applying “step-forward” and “path-track” algorithms which assist in exploring forward given a concept and in tracking back the paths going from a departure concept through a destination concept. In the visual concepts combination model, a generative adversarial networks model is proposed for generating images which synthesize two distinct concepts. An implementation of these two models was developed and tested in a design case study, which indicated that the proposed approach is able to not only generate a variety of cross-domain concept associations but also advance the ideation process quickly and easily in terms of quantity and novelty.
Creativity is crucial for most organizations. However, availability of a multitude of factors, tests, and methods of creativity makes it difficult for companies to select an appropriate test, factor, and method for enhancing the creative outcomes within their organization. In this work, we first synthesize a generic or ‘common’ definition of creativity from the variety of definitions of creativity available, using two methods. Using this definition, we then identify, creativity measures that assess creativity of people, factors affecting creativity, and creativity enhancing methods. This research shows that depending on the focus on the major factors of creativity, derived from the common definition, a company can select a specific set of tests and methods.
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
The last 100 years have witnessed a fervent debate in the USA about quantitative and qualitative research paradigms. Unfortunately, this has led to a great divide between quantitative and qualitative researchers, who often view themselves as in competition with each other. Clearly, this polarization has promoted purists, namely, researchers who restrict themselves exclusively either to quantitative or to qualitative research methods. Mono‐method research is the biggest threat to the advancement of the social sciences. Indeed, as long as we stay polarized in research, how can we expect stakeholders who rely on our research findings to take our work seriously? Thus, the purpose of this paper is to explore how the debate between quantitative and qualitative is divisive and, hence, counterproductive for advancing the social and behavioural science field. This paper advocates that all graduate students learn to utilize and to appreciate both quantitative and qualitative research. In so doing, students will develop into what we term as pragmatic researchers.
The mixed methods paradigm is still in its adolescence, and, thus, is still relatively unknown and confusing to many researchers.
In general, mixed methods research represents research that involves collecting, analyzing, and interpreting quantitative
and qualitative data in a single study or in a series of studies that investigate the same underlying phenomenon. Over the
last several years, a plethora of research designs have been developed. However, the number of designs that currently prevail
leaves the doctoral student, the beginning researcher, and even the experienced researcher who is new to the field of mixed
methods research with the challenge of selecting optimal mixed methods designs. This paper presents a three-dimensional typology
of mixed methods designs that represents an attempt to rise to the challenge of creating an integrated typology of mixed methods
designs. An example for each design is included as well as a notation system that fits our eight-design framework.
Algodão - “Eu não sei quando te perdi”
- Nuno Barbosa
- Barbosa Nuno
Emmy Curl - “Come Closer”
- Nuno Barbosa
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Nuno Barbosa. 2015. Emmy Curl -"Come Closer". Video. (2015). April 10, 2020 from https://vimeo.com/113000956.
Souq - “Desert Snake Catcher”
- Nuno Barbosa
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Júlio Resende - “Let's go to the Moon again”
- Nuno Barbosa
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Qualitative research methods: an introduction for students of psychology and education
- Arthur J Cropley
Arthur J. Cropley. 2016. Qualitative research methods: an introduction for students of psychology and education.
Junip - “Without You”
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Amplifying Creativity. Video
- Nuno Barbosa
Nuno Barbosa. 2013. Amplifying Creativity. Video. (2013). April 10, 2020 from https://vimeo.com/195623736.
MAGNOLIUS - “Selaron”
- Nuno Barbosa
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Emmy Curl - March for Peace
- Nuno Barbosa
- Barbosa Nuno
Nuno Barbosa. 2014. Emmy Curl -March for Peace. Video. (2014). April 10, 2020 from https://vimeo.com/213043420.
Júlio Resende - “Fado Cyborg”
- Nuno Barbosa
- Barbosa Nuno
Nuno Barbosa. 2018. Júlio Resende -"Fado Cyborg". Video. (2018). April 10, 2020 from https://vimeo.com/281950030.
Driving the creative machine
- Harold Cohen
- Cohen Harold
Harold Cohen. Driving the creative machine, September 2010. Lecture. http://www.aaronshome.com/aaron/
publications/orcastalk2s.pdf.
Artificial intelligence: Definition types examples technologies
- Chethan Kumar
Chethan Kumar. 2018. Artificial intelligence: Definition, types, examples, technologies, Aug 2018. https://medium.
com/@chethankumargn/artificial-intelligence-definition-types-examples-technologies-962ea75c7b9b.
Real-Time Style Transfer
- Cheng-Bin Jin
Cheng-Bin Jin. 2018. Real-Time Style Transfer. https://github.com/ChengBinJin/Real-time-style-transfer/.
Desert Snake Catcher
- Nuno Barbosa
Nuno Barbosa. 2013. Souq -"Desert Snake Catcher". Video. (2013). April 10, 2020 from https://vimeo.com/63415960.
Eu não sei quando te perdi
- Nuno Barbosa
Nuno Barbosa. 2013. Algodão -"Eu não sei quando te perdi". Video. (2013). April 10, 2020 from https://vimeo.com/
53332710.
- Nuno Barbosa
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