Rise Of Artificial Intelligence Research Articles

Le futur du métier de traducteur face à la révolution numérique : préoccupations et perspectives pour les bilingues

The Future of The Translation Profession in The Age of Digital Revolution: Concerns and perspectives for bilingual professionals The digital revolution has caused a significant shift in labour market policies across all sectors. The rapid advancement and widespread adoption of software and IT tools have led to an increased risk of job displacement for skilled professionals, as these tools enable even non-experts to perform tasks traditionally handled by specialists. This study explores the potential threat to employment faced by professional translators due to these technological advancements. The rise of artificial intelligence and machine translation (MT) has encouraged many bilingual individuals to enter the translation field, a trend that has raised concerns among linguistic researchers. These technological developments have also cast doubt on the value of language experts. This research addresses key questions: To what extent is the future of professional translators at risk? Do professional translators face significant competition in the Information Age? To answer these questions, this study employed both quantitative and qualitative research methods, including field data collection from bilinguals and professional translators. Questionnaires were administered first to bilinguals to gauge their motivations and then to professional translators to understand their concerns and perspectives. The data analysis provided clear insights that supported the study's hypotheses. The research suggests that professional translators may face substantial unemployment risks in the future. This is largely attributed to the increasing involvement of bilinguals in the field, driven by the proliferation of machine translation tools. The study highlights the relevance of connectivism and job prospection theories in understanding the challenges faced by professional translators and in addressing the rise of bilinguals in the workforce.

The impact of artificial intelligence represented by ChatGPT on UI design

The rise of Artificial Intelligence (AI), notably represented by ChatGPT, has greatly influenced User Interface (UI) design. ChatGPT, an advanced language model, aids designers by suggesting ideas, generating interface images, providing feedback, and simulating user interactions. Its integration into UI design processes has improved efficiency, allowing designers to explore more design options quickly. Additionally, ChatGPT facilitates user research by simulating conversations with potential users, gathering valuable feedback on user experience. However, there are also a lot of challenges, even risks, of using artificial intelligence to help designers, including maintaining design quality, protecting creativity, and ensuring designs remain user-centered. Moreover, a lot of people worry that with the rapid development of ChatGPT, many UI designers will be out of work in the future. This article discusses the influence of artificial intelligence represented by ChatGPT on UI design, including the advantages and challenges of AI in UI design. There is no doubt that as AI technology evolves, its role in UI design will expand rapidly, reshaping the digital experience in the future. But the question of whether AI will cause a lot of designers to lose jobs needs time to give us an answer.

Detaching the Actor’s Face: Control and Usage of the Face as an Image-object

This article explores the extent to which the actor’s face has impacted society to the point that it is no longer a personal possession and that its usage and context are difficult to control. I bring together perspectives from visual studies and celebrity studies to address the issues of face borrowing and identification. I examine the engagement of the general public with the actor’s face in an attempt to create the basis for a critical analysis. I propose viewing the actor’s face as an image-object, detached from the physical body and able to travel without the actual presence of the actor. Three defining stages are identified in social behaviour that are a direct result of capturing the face in a photograph: detaching, owning and wearing the face. The need for intimacy and identification that governs the relationship between audiences and celebrities has an effect on the handling of images of actors. Furthermore, this article investigates the concurrence of technological advancements that allow individuals to use actors’ faces for shaping their own identities and highlights the danger that actors are facing as the rise of artificial intelligence can render them as replicable digital assets.

FairMOE: counterfactually-fair mixture of experts with levels of interpretability

With the rise of artificial intelligence in our everyday lives, the need for human interpretation of machine learning models’ predictions emerges as a critical issue. Generally, interpretability is viewed as a binary notion with a performance trade-off. Either a model is fully-interpretable but lacks the ability to capture more complex patterns in the data, or it is a black box. In this paper, we argue that this view is severely limiting and that instead interpretability should be viewed as a continuous domain-informed concept. We leverage the well-known Mixture of Experts architecture with user-defined limits on non-interpretability. We extend this idea with a counterfactual fairness module to ensure the selection of consistently fair experts: FairMOE. We perform an extensive experimental evaluation with fairness-related data sets and compare our proposal against state-of-the-art methods. Our results demonstrate that FairMOE is competitive with the leading fairness-aware algorithms in both fairness and predictive measures while providing more consistent performance, competitive scalability, and, most importantly, greater interpretability.

P-166 An artificial intelligence-powered tool to score fresh donor oocytes and predict blastulation: interim analysis of a prospective investigation conducted at 3 laboratories

Abstract Study question Is the Magenta-score associated with fresh donor oocytes’ blastulation competence, and can it be used to predict blastocyst rates per cohort of inseminated oocytes (BRpC)? Summary answer Developmentally-competent fresh donor oocytes showed significantly higher Magenta-score, although the number of blastocysts obtained was within the predicted range in only 63% of the cycles. What is known already Currently, many couples undergo donor oocyte treatments. However, despite donors being theoretically fertile, not all oocytes are developmentally competent, and many do not reach the blastocyst stage. Even if subjective and ineffective to predict oocyte competence, visual oocyte morphological assessment is still routinely used to evaluate oocytes. Lately, the rise of artificial intelligence (AI) resulted in promising tools to assess oocytes more objectively in a standardized method, possibly providing novel insights into the prediction of their competence. In this context, Magenta-score (Future Fertility), an AI-based image analysis tool, can potentially prove useful to optimize the management of oocyte donation treatments. Study design, size, duration A Blinded cohort study (June-2023/December-2023) was conducted to assess the primary outcome: association between fresh donor-oocytes’ Magenta-Score and their subsequent blastocyst development (assumed to be 50%). A sample size of 779 achieves 80%-power to detect the difference between the null-hypothesis point-biserial-correlation of 0 and the alternative hypothesis of 0.1 using a two-sided test with 5%-significance. Secondary outcomes were differences (i) predicted - true BRpCs, and (ii) predicted - true numbers of blastocysts obtained. Participants/materials, setting, methods Interim-analysis of 514 fresh-oocytes obtained from 63 donors at 3 centers and allocated (7.7±3, range:3-18) to 67 recipients. Pictures of denuded mature oocytes were acquired before ICSI. Magenta-scores were generated blindly (providing only donor age and total number of mature oocytes in cohort to the software). The software also estimated BRpCs and range of blastocysts obtainable from each cohort. Donors’ characteristics, ovarian stimulation/treatment cycle parameters, and sperm analyses were tested as confounders. Main results and the role of chance The only visual oocyte anomaly associated with lower Magenta-score was irregular shape (N = 484,5.4±2.2 vs N = 30,3.3±2.1, Mann-Whitney-U<0.01). Oocytes that developed into blastocysts (N = 194) had significantly higher Magenta-scores versus oocytes that did not reach this milestone (N = 320) (5.8±2.1 vs 4.9±2.3, Mann-Whitney-U<0.01; Odds-Ratio adjusted for sperm motility and incubator [standard/time-lapse]=1.26, 95%CI:1.13-1.34, p < 0.01; power=99.6%). No association was noted with day of blastulation or morphological quality. Data were concordant across centers. The average predicted-BRpC was 39.4±11.4% versus a true-BRpC of 40.6±22.6% (Person’s correlation: -0.24; mean difference: -1.1±27.6%, 95%CI from -7.8% to + 5.6%, p = 0.74). The average predicted number of blastocysts per oocyte cohort was 3.3±1.6 versus a true average of 2.9±1.7 (Person’s correlation: 0.25; mean difference: +0.4±2.0, 95%CI from -0.04 to + 0.9, p = 0.07). In 21% (N = 14) and 31% (N = 21) of cycles, the tool predicted an equal or lower number of blastocysts, respectively. In 5 cycles, no blastocyst was obtained, but the tool did not predict this unexpected outcome. When testing the predicted range of blastocysts obtained, the true number was within the range in 63% (N = 42) of cases and higher than the maximum predicted number in 13% (N = 9) of cases. No confounder was identified on the BRpC in this population of fresh donor cycles. Limitations, reasons for caution Of note, this is an interim analysis of an ongoing study. Not all mature oocytes recovered from a donor were utilized, thus not all oocytes in a cohort were imaged and included in analysis. Other putative confounders on BRpC should be considered in future studies with a larger sample size. Wider implications of the findings Effective management of an oocyte donation program is critical to comply with expected high success rates while minimizing the number of surplus blastocysts produced. The integration of genomic data, recipient couples’ characteristics, and the benefits of objective standardized AI-powered oocyte scoring is the most promising workflow to optimize this task. Trial registration number Not applicable

Towards Education 4.0: The role of Large Language Models as virtual tutors in chemical engineering

Recent years have seen the rise of Artificial Intelligence (AI) powered generative chatbots, such as OpenAI’s ChatGPT or Microsoft’s Copilot. These tools have simultaneously positively surprised and taken aback people worldwide, raising the question of whether they can or should be used in education, as well as how to properly guide students and teachers on using them safely and ethically. To this end, this work provides (i) a brief overview of the current applications of AI in Higher Education (HE), (ii) a discussion of the ethical and societal concerns associated with the usage of AI models, (iii) the initial steps of the implementation of a chatbot used at the Technical University of Denmark (DTU) able to perform audits for Good Manufacturing Practice (GMP), and (iv) an investigation of the need and opportunities of AI in chemical engineering education. The latter is achieved through quantitative and qualitative analyses of the responses given by both Master’s students and academia/industry practitioners on the introduction and use of AI in education. This paves the way for discussing current perceptions, expectations and concerns of AI models, as well as their limitations and the opportunities they could provide.

Evaluating the AI dialogue System's intercultural, humorous, and empathetic dimensions in English language learning: A case study

The rise of artificial intelligence (AI) technologies has been notable in supporting English as an Additional Language (EAL) learners, particularly in offering interactive assessments. AI-driven dialogue systems that produce culturally relevant humor and empathy, bridging the native language (L1) and the target language (L2), have been employed to engage EAL learners more effectively and boost their learning results. However, there's a noticeable gap in research concerning the fusion of humor, empathy, and intercultural dimensions in language learning. Intercultural dimensions encompass acquiring crucial knowledge, communication skills, and the values essential for meaningful interactions across cultures. This case study examines the effectiveness of an AI dialogue system, termed the Multidimensional Approach Culture, Humor, and Empathy Bot (MACHE-Bot), in elevating the English language learning experience and its potential to enhance EAL proficiency. Using convenience sampling, 37 individuals were selected from an English language learning group on Facebook. Following a two-week pilot study, semi-structured interviews were conducted. The results revealed that MACHE-Bot adeptly initiated and reciprocated with humor, empathy, and cultural nuances in a manner well-received by participants. Additionally, the study identified five pivotal factors in EAL learning with MACHE-Bot: (1) intercultural dimensions, which include culturally humorous interaction and culturally empathetic support; (2) user trust comprising both affective and cognitive elements; (3) anthropomorphism; (4) reciprocal self-disclosure; and (5) higher engagement and motivation. These five factors collectively contribute to creating an engaging, relatable, and supportive learning environment, facilitating a more natural learning process, and fostering a deeper learner-bot connection, thereby potentially enhancing motivation and investment in the learning process.

How Live Marketing Affects Green Purchase in the Age of Artificial Intelligence?

ABSTRACT With the rise of artificial intelligence and the public’s attention to environmental issues, live online promotion of green products has become a new marketing method. It has become a key issue for enterprises to choose the appropriate information framework to motivate consumers’ green purchasing behavior through different forms of live broadcasting. Combined with laboratory experiments, this study analyses the interaction between the type of live streaming and message framing on green purchase intention based on explanation level theory. Three experiments results show that using loss-framing message real anchors are more likely to promote consumers’ green purchase intentions, and AI anchors are more suitable for using gain frames. Besides, consumer innovativeness moderates the influence of message framing and live streaming form on green purchase intention, and green perceived value plays a mediating role in this process. Our findings can not only guide the formulation of enterprise marketing strategy, but also provide countermeasures and suggestions for the future development of live marketing.

Comprehensive Artificial Intelligence Vulnerability Taxonomy

With the rise of artificial intelligence (AI) systems and machine learning (ML), there is a need for a comprehensive vulnerability framework that takes into account the specifics of AI systems. A review of the currently available frameworks shows that even though there have been some efforts to create AI specific frameworks, the end results have been flawed. Previous work analysed for this paper include AVID, Mitre ATLAS, Google Secure AI Framework, Attacking Artificial Intelligence, OWASP AI security and privacy guide, and ENISA Multilayer framework for good cybersecurity practices in AI. While only AVID is intended to be an AI/ML focused vulnerability framework, it has some weaknesses that are discussed further in the paper. Of the other works especially the ENISA framework has a valuable way of determining AI domains that can be affected by vulnerabilities. In our taxonomy proposal the first part of the evaluation process is determining the location in the AI system lifecycle that the vulnerability affects. The second part is determining which attributes of technical AI trustworthiness are compromised by the vulnerability. The third part is determining the possible impact of the vulnerability being exploited on a seven-step scale from the AI system functioning correctly, to it performing unintended, attacker directed actions outside the bounds it is supposed to function in. We also evaluate two known AI vulnerabilities based on our taxonomy proposal to showcase the benefits in comparison to existing frameworks.

Utilizing Artificial Intelligence (AI) in Customer’s Purchase Intentions on Online Food Delivery Service

Generally, food delivery services like GrabFood act as couriers, transporting consumer needs from restaurants or stores directly to their doorsteps. The rise of Artificial Intelligence (AI) has further revolutionized this convenience, allowing people to order meals and other goods from the comfort of their homes. This research investigates how AI is utilized to influence customer purchase intentions on GrabFood. The study examines the impact of six independent variables: instant food delivery, estimated delivery time, customized food recommendations, interactivity, cashless payment methods, and consumer behavior. These variables are analyzed in relation to the dependent variable – the customer's intention to use GrabFood. To gather data, an online survey was conducted with 100 respondents. The collected data was then verified using SPSS software. The findings revealed that delivery speed is a key driver, with both instant delivery and estimated delivery time showing a significant positive correlation (β = 0.457) with purchase intention. However, other features like personalized recommendations (β = 0.174), cashless payment methods (β = 0.119), and user interaction (β = -0.188) did not significantly impact user decisions. These findings require further exploration to understand user preferences for these features

Evolutionary regulatory dynamics in a pluralist and polarized journalism landscape: a case study of the normative framework in Spanish media

Within the European Union, the pluralist polarized journalistic model suggests the presence of journalistic cultures rooted in the connections between political parties and media organizations. In this classical framework, the state exerts significant intervention to influence a media system characterized by lower levels of professionalization. In this regard, Spain serves as a well-examined example of a pluralist polarized Western democracy. Our study entails a systematic review based on two distinct dimensions. Firstly, we scrutinized all legal documents pertaining to media regulation in Spain published between the Spanish transition and the present 1977–2024. From this perspective, we propose a chronological evolution to categorize this extensive collection of norms. Secondly, we complement our primary source assessment with an examination of secondary sources to validate the proposed evolution. Our findings indicate that the Spanish media regulation is evolving due to two pivotal factors: the influence of the European Union and the preservation of the narrative established during the transition to democracy. While contemporary communication grapples with issues such as the rise of artificial intelligence, journalistic instability, algorithmic communication, and fragmented user consumption, these areas are only addressed peripherally within the Spanish media normative context.

Framing Assessment Questions in the Age of Artificial Intelligence: Evidence from ChatGPT 3.5

With the rise of artificial intelligence (AI), higher education faces a significant challenge in learning assessment. The emergence of tools like ChatGPT raises concerns regarding the potential for cheating and the reliability of assessment outcomes. This paper aims to address these concerns by proposing a methodology for framing questions that effectively measures learning outcomes while reducing the risk of AI-enabled cheating. To achieve this objective, we employ a methodological approach that involves getting responses from ChatGPT 3.5 to various question prompts across different domains. These responses are then evaluated by faculty members specializing in management education. Through this process, we aim to identify question-framing strategies that effectively assess learning outcomes while minimizing susceptibility to AI Cheating. Our analysis reveals several key findings. Certain question Types (Decision Making, Recent Events, and Experiential Learning) demonstrate greater resilience against AI-generated responses, indicating their potential effectiveness in assessing student learning. This study offers original insights into the challenges and opportunities associated with learning assessment in the context of AI integration. The paper tries to provide valuable guidance for Policymakers, educators & students seeking to enhance the integrity and reliability of their assessment practices. Doi: 10.28991/ESJ-2024-08-03-09 Full Text: PDF

Human Voice Recognition for Authentication

Abstract: Human voice plays a very important role as a vital biometric parameter for authentication and Identification. Voice recognition is a biometric technology used to identify one particular person’s voice. It provides Enhanced security, convenient authentication and considerable cost saving. It can be performed using the concept of processing using Technique. Voice recognition is a software program or hardware device that can decode the human voice. It can receive and interpret dictation, understand and perform spoken commands, and identify a person based on their unique voiceprint. Voice recognition has become more popular in recent years with the rise of Artificial Intelligence (AI) and intelligent assistants, such as: Amazon’s Alexa, Apple’s Siri, Google Assistant. Voice recognition is used to control a smart home ,Instruct a smart speaker, Command phones and tablets ,Voice recognition is also known as Automatic speech recognition (ASR), Computer speech recognition.

Educating patients on osteoporosis and bone health: Can "ChatGPT" provide high-quality content?

The rise of artificial intelligence (AI) models like ChatGPT offers potential for varied applications, including patient education in healthcare. With gaps in osteoporosis and bone health knowledge and adherence to prevention and treatment, this study aims to evaluate the accuracy of ChatGPT in delivering evidence-based information related to osteoporosis. Twenty of the most common frequently asked questions (FAQs) related to osteoporosis were subcategorized into diagnosis, diagnostic method, risk factors, and treatment and prevention. These FAQs were sourced online and inputted into ChatGPT-3.5. Three orthopedic surgeons and one advanced practice provider who routinely treat patients with fragility fractures independently reviewed the ChatGPT-generated answers, grading them on a scale from 0 (harmful) to 4 (excellent). Mean response accuracy scores were calculated. To compare the variance of the means across the four categories, a one-way analysis of variance (ANOVA) was used. ChatGPT displayed an overall mean accuracy score of 91%. Its responses were graded as "accurate requiring minimal clarification" or "excellent," with a mean response score ranging from 3.25 to 4. No answers were deemed inaccurate or harmful. No significant difference was observed in the means of responses across the defined categories. ChatGPT-3.5 provided high-quality educational content. It showcased a high degree of accuracy in addressing osteoporosis-related questions, aligning closely with expert opinions and current literature, with structured and inclusive answers. However, while AI models can enhance patient information accessibility, they should be used as an adjunct rather than a substitute for human expertise and clinical judgment.

Application of new features based on artificial intelligent robot technology in medium-scale urban design pedigree and intelligent management and control

Since the 21st century, China has been vigorously developing urban construction, and now the rise of artificial intelligence (AI) has brought new opportunities for urban design and management. Meso-scale cities are the most developed cities in China and play an important role in economic and social development. The current robot technology is mainly divided into industrial robots and service robots, which can play an important role in the development of cities. This paper aims to apply AI robotics technology to the analysis and intelligent management of mesoscale urban design genealogy. Firstly, the development pedigree of the mesoscale city was analyzed, from which the characteristics of previous designs could be clearly understood. Then, the urban management and control system is intelligently designed from many aspects, the characteristics of the robot are analyzed, and the application of the intelligent robot in urban design is introduced. After that, four developing meso‑scale cities in a province were selected as the evaluation objects, and an Analytic Hierarchy Process (AHP) was proposed to evaluate the application effect of robotics technology in meso‑scale cities. The results showed that the overall score of the robot in urban traffic design was greater than 70 points, and the overall score of urban architectural design was greater than 65 points, which was in the acceptable range. In the application score of urban environment design, the influence was about 70 to 80 points, and the aesthetics was more than 75 points. The cultural aspect of the design impact score was no more than 75 points, while the cultural support aspect score was around 80 points. After the weight calculation, the final overall score was 489 points, and the comprehensive average score was 36 points. The overall composite score and average score were both good. This showed that the application of robot technology to the pedigree analysis and intelligent management and control of mesoscale urban design could achieve good results.

A Study of “Rise of AI in Digital Marketing”

In recent years, Artificial Intelligence (AI) has completely transformed the world of digital marketing. It's brought about a whole new era of innovation and efficiency. This abstract will dive into the many ways AI has impacted digital marketing strategies, highlighting its game-changing rise and what it means for businesses. AI has given marketers access to an incredible amount of data, allowing them to make smarter decisions using advanced analytics and machine learning algorithms. By digging deep into consumer behavior, preferences, and trends, AI helps create highly targeted and personalized marketing campaigns that really engage customers and drive up conversion rates. AI-powered platforms take care of all the repetitive tasks like ad placement, audience segmentation, and campaign optimization. This automation not only saves time and resources, but it also lets marketers focus on the big picture and drive real results. So, in a nutshell, AI has completely revolutionized the world of digital marketing. It's given marketers a wealth of opportunities to understand their customers better, create amazing content, and streamline their campaigns. But it's not without its challenges. As AI continues to advance, it's important for marketers to stay on top of the game and navigate the ethical considerations that come with it.

New Frontiers in Breast Cancer Imaging: The Rise of AI.

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

FRAUD DETECTION AND PREVENTION IN FINANCIAL INSTITUTIONS

Financial fraud, characterized by deceptive practices aimed at securing financial gains, has emerged as a prevalent threat within companies and organizations. Traditional methods like manual verification and inspection are inaccurate, expensive, and time-consuming in identifying fraudulent activities. With the rise of artificial intelligence, machine learning-based approaches offer a promising solution to detect fraudulent transactions through the analysis of vast financial datasets. This paper presents a systematic literature review (SLR) that systematically surveys and consolidates existing literature on machine learning (ML)-based fraud detection. Employing the Kitchenham approach, which employs defined protocols to extract and synthesize relevant articles, this review reports the findings from various studies gathered through specified search strategies from electronic databases. After applying inclusion/exclusion criteria, 93 articles were selected, synthesized, and analyzed. The review outlines popular ML techniques utilized for fraud detection, prevalent fraud types, and evaluation metrics. The surveyed articles indicate that support vector machine (SVM) and artificial neural network (ANN) are commonly employed ML algorithms for fraud detection, with credit card fraud being the most addressed fraud type using ML techniques. Additionally, the paper discusses key issues, gaps, and limitations in financial fraud detection and suggests potential areas for future research. Furthermore, the review identifies challenges like class imbalance in datasets and the dynamic nature of fraud patterns. While SVM and ANN are popular, exploring ensemble methods, deep learning, and anomaly detection techniques shows promise for improving detection accuracy in suitability. Future research should address these limitations and foster collaboration between academia, industry, and regulatory bodies to advance fraud detection methods and safeguard financial transactions.

Rise of Artificial Intelligence in Business and Industry

The integration of artificial intelligence (AI) into business and industry is catalyzing a paradigm shift in how organizations operate, innovate, and interact with stakeholders. This abstract explores the multifaceted implications of AI across various domains, highlighting its role in automation, predictive analytics, personalized customer experiences, supply chain optimization, enhanced decision-making, natural language processing, product innovation, risk management, fraud detection, healthcare advancements, and workforce augmentation. By leveraging AI technologies, businesses can automate repetitive tasks, anticipate trends, tailor experiences, optimize operations, mitigate risks, and foster innovation. However, the widespread adoption of AI also poses ethical and societal challenges, including concerns about job displacement, data privacy, and algorithmic bias. Therefore, a holistic approach that balances technological advancement with ethical considerations is essential to harness the full potential of AI while ensuring its responsible and equitable deployment in business and industry.

Deep learning approaches to the game of Connect6

Especially with the rise of artificial intelligence, people need more ability to think logically and make judgments than before. Board games can be used as a tool for training logic, which has a positive effect on the sustainable development of society. Connect6 is a very popular board game because it is a fair and highly complex game with simple rules. This research aims to address the technical challenges of building a game-based logical learning system with a deep neural network for Connect6. We used heuristic knowledge to establish the knowledge base. Our novel algorithm, based on residual deep convolutional neural networks (DCNNs), outperforms conventional approaches. We compare neural network architectures, demonstrating the superiority of residual DCNNs.Our AI program, “Kavalan,” achieves remarkable search performance. Furthermore, the introduced approaches lend themselves to application in other sudden-death board games, such as Gomoku, the endgame of Chess and Shogi.