Vavilov and Generative AI

In today's dynamic and competitive oil and gas industry, the integration of Artificial Intelligence (AI) has emerged as a game-changer, offering unparalleled opportunities for optimization, cost reduction, and operational excellence. The main objective of autonomous operations is to minimize manual interactions and maximize self-directed plant operations. ADNOC Onshore has implemented generative AI agents in daily maintenance and production operations to boost workforce productivity in the journey of achieving autonomous operations. This paper explains the use cases, challenges, AI architecture & data security in deployment. Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. GPT-4 Turbo is a large multimodal model (accepting text or image inputs and generating text) that can solve difficult problems with greater accuracy and advanced reasoning capabilities. The scope includes empowering reliability, maintenance, and operations professionals to draw insights from equipment manuals, asset operating manuals and operating procedures, maintenance records, and safety & integrity manuals. This in-house solution with support across structured and unstructured data, an LLM-agnostic architecture, deterministic responses with source references, and granular access controls. The solution has been integrated ERP SAP system and sensor time series PI system, data historians for integrated context. A unique automated contextualization engine has been used based on oil and gas specific vocabulary to bring context to their operations. A conversational interactive agent has been built for user interactions. The maintenance and operations engineer can receive suggestions on the proper steps to identify the root cause based on OEM product manuals, previous events, and current performance. This Generative AI solution accelerates time to insight for operators by equipping teams to streamline maintenance operations and Investigate maintenance records with generative AI to troubleshoot operations challenges more efficiently. The internal study showed that operational productivity has increased by 20% after this solution's implementation. For the model to understand industrial environments, it would require retraining the model on industrial data. Using existing models on uncontextualized, unstructured industrial data significantly increases the risk of incorrect and untrustworthy answers – referred to as AI hallucinations. Another significant challenge lies in the dependence on the quality and quantity of available data for training. AI models require extensive and representative datasets to produce accurate and reliable predictions. Large language models are a type of artificial intelligence (AI) model designed to understand and generate human language. These models are built upon deep learning architectures, particularly transformer architectures. Generative AI can play a significant role in oil and gas asset operations towards the goal of achieving autonomous operations.

Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".

With the rapid advancement of digital transformation and artificial intelligence technologies, generative artificial intelligence (Generative AI) has shown broad application prospects in the domain of corporate management. Based on the Technology–Organization–Environment (TOE) theoretical framework and value-chain reengineering theory, this paper constructs an optimized model for empowering corporate financial management with Generative AI. First, it analyzes the core capabilities of generative models in data synthesis, text comprehension, and decision support, and explores innovative pathways for multi-scenario automatic budget preparation, dynamic financial forecasting, and automated compliance audit report generation within the contexts of corporate budgeting, predictive analysis, and risk control. Second, it proposes implementation strategies—such as organizational restructuring, enhancement of data-governance systems, and establishment of continuous iteration mechanisms—and, drawing on representative enterprise case studies, demonstrates how Generative AI improves forecast accuracy, optimizes risk alerts, and enhances audit efficiency. Finally, it addresses challenges related to model bias, data-privacy protection, computational resource investment, and algorithmic transparency, offering technical improvements and governance measures to guide enterprises in deploying Generative AI applications under compliance and ethical constraints. The study shows that Generative AI not only elevates the intelligence level of financial management but also drives enterprise value creation and sustained innovation through dynamic decision support.

The upcoming of the large language models and generative artificial intelligence had Completely change the way in which we generate and understand language, and also start the beginning of a new phase in AI-driven applications. This review paper over see the advancements and changes that have occurred over time, providing a thorough assessment of generative artificial intelligence and large language models, while we also look upon their impactful potential across different areas. The first section of the research focuses on the changes of extensive language models and generative AI, and we will try to focus upon developments in models like GPT-4 and others. These models have shown their ability number of times from applications in various sectors, from automated content generation to acurate conversational agents. They are characterized by their capability to produce text that is both coherent and contextually appropriate. However, despite their accuracy, strengths, generative artificial intelligence and large language models face critical ethical, technological, and societal issues. Some main stream concern arises from the biases present in the training data, which can cause and lead to social inequalities.Here we looks into the causes of these biases and their implications, stressing the need for comprehensive frameworks to identify and mitigate them. Keywords: backpropagation, bert, diffusion models, explainable ai (xai), generative ai, image synthesis, long short-term memory (lstm), natural language processing (nlp), neural network, recurrent neural network (rnn), small language model (sml), and transformer model.

The upcoming of the large language models and generative artificial intelligence had Completely change the way in which we generate and understand language, and also start the beginning of a new phase in AI-driven applications. This review paper over see the advancements and changes that have occurred over time, providing a thorough assessment of generative artificial intelligence and large language models, while we also look upon their impactful potential across different areas. The first section of the research focuses on the changes of extensive language models and generative AI, and we will try to focus upon developments in models like GPT-4 and others. These models have shown their ability number of times from applications in various sectors, from automated content generation to acurate conversational agents. They are characterized by their capability to produce text that is both coherent and contextually appropriate. However, despite their accuracy, strengths, generative artificial intelligence and large language models face critical ethical, technological, and societal issues. Some main stream concern arises from the biases present in the training data, which can cause and lead to social inequalities.Here we looks into the causes of these biases and their implications, stressing the need for comprehensive frameworks to identify and mitigate them. Keywords: backpropagation, bert, diffusion models, explainable ai (xai), generative ai, image synthesis, long short-term memory (lstm), natural language processing (nlp), neural network, recurrent neural network (rnn), small language model (sml), and transformer model.

Generative artificial intelligence (GAI) created a whirlwind in late 2022 and emerged as a transformative technology with the potential to revolutionize various industries, including design. Its feasibility and applicability have been extensively explored and studied by scholars. Previous research has investigated the potential of AI in different domains, such as aiding data collection and analysis or serving as a source of creative inspiration. Many design practitioners have also begun utilizing GAI as a design tool, stimulating creativity, integrating data more rapidly, and facilitating iterative design processes. However, excessive reliance on GAI in design may lead to losing the uniqueness emphasized in the field and raise concerns regarding ethical implications, biased information, user acceptance, and the preservation of human-centered approaches. Therefore, this study employs the double-diamond design process model as a framework to examine the impact of GAI on the design process. The double diamond model comprises four distinct stages: discover, define, develop, and deliver, highlighting the crucial interplay between divergence, convergence, and iteration. This research focuses on GAI applications' integration, timing, and challenges within these stages.A qualitative approach is adopted in this study to comprehensively explore the potential functionalities and limitations of generative AI at each stage of the design process. Firstly, we conducted an extensive literature review of recent advancements and technological innovations in generative artificial intelligence. Subsequently, we participated in lectures and workshops and invited experts from various domains to gather insights into the functionality and impact of generative AI. Later, we interviewed design professionals experienced in utilizing generative AI in their workflow. Data triangulation and complementary methods like focus groups are employed for data analysis to ensure robustness and reliability in the findings related to the functionality of generative AI.The findings of this study demonstrate that generative AI holds significant potential for optimizing the design process. In the discovery stage, generative AI can assist designers in generating diverse ideas and concepts. During the definition stage, generative AI aids in data analysis and user research, providing valuable insights to designers, such as engaging in ideation by addressing "How might we" questions, thereby enhancing decision-making quality. In the development stage, generative AI enables designers to rapidly explore and refine design solutions. Lastly, in the delivery stage, generative AI can help generate design concept documentation and produce rendered design visuals, enhancing the efficiency of iterative processes.This study contributes to a better understanding of how generative artificial intelligence can reshape the existing framework of the design process and provides practical insights and recommendations for contemporary and future designers.

Generative AI (GenAI) use is increasing across society in many different industries. Despite widespread adoption in workplaces, there is little consensus on the scope of its benefits and challenges at the level of most industries. Universities are being called upon to equip graduates with important knowledge and skills using GenAI for their professional contexts. Higher education, however, faces issues in effectively and sustainability embedding a use of GenAI in the student experience, which requires adjustments to learning and teaching activities, assessment, and learning outcomes and in access to useful GenAI platforms relevant to the various disciplines. As pedagogical models, ethical debates, and technologies continue to develop in this space, university teachers’ experiences of teaching with GenAI have yet to be explored in detail. Adopting a phenomenographic perspective, this study examines university teachers’ conceptions, perceptions, and approaches to using GenAI in teaching. Leveraging semi-structured interviews with 30 teaching academics, variations of teaching using GenAI were identified. Quantitative analysis was also conducted to capture the associations between these variations. By exploring the qualitative differences between these variations, a nuanced and important contribution to the GenAI discussion from the understanding of university teachers is uncovered. The results show that some ways of understanding and teaching with GenAI are more likely to help students develop effective knowledge and skills for the workplace than others. The findings also offer education leaders evidence to help design effective support for teachers using GenAI to innovate in the student experience. Through investigating the university teacher experience of GenAI, this research adds to the growing debate on the GenAI enabled benefits and challenges that are set to shape the higher education sector.

Despite concerns about students’ use of generative AI (GenAI) in assessment, the technology has become embedded into students’ everyday assessment practices. It is unclear how students are making judgements about their ways of working with GenAI and what impact this has upon their learning. This qualitative multimodal study examines students exercising judgement as they work with GenAI to complete assessment tasks. Twenty-six interviews were conducted with Australian university students, primarily using a scroll-back approach, which revisits traces of students’ historical interactions with GenAI in the interviews. Employing a holistic definition of judgement and a narrative approach to analysis, we interpreted six distinct categories of judgement events. These are: 1) making judgements about knowledge when working with GenAI; 2) learning to judge GenAI through its limitations; 3) relying on GenAI for things they could not otherwise do; 4) adopting ideas with low levels of criticality; 5) misjudging GenAI contributions as their own; and 6) submitting GenAI content in an assignment without judging it. This study suggests GenAI use strongly shapes student learning in complex ways when undertaking assessment tasks, and that making judgements about GenAI entails a student making judgements about their own knowledge, deficits, and quality of contributions.

In dynamic landscape of oil and gas drilling, Generative Artificial Intelligence (Generative AI) emerges as the indispensable ally, leveraging historical drilling data to revolutionize operational efficiency, mitigate risks, and empower informed decision-making. Existing Generative AI methods and tools, such as Large Language Models (LLMs) and agents, require tuning and customization to the oil and gas drilling sector. Applying Generative AI in drilling confronts hurdles such as ensuring data quality and navigating the complexity of operations. A methodology integrating Generative AI into drilling demands is comprehensive and interdisciplinary. Agile strategy revolves around constructing a network of specialized agents of LLMs, meticulously crafted to understand industry-specific terminology and intricate operational relationships rooted in drilling domain expertise. Every agent is linked to manuals, standards, specific operational drilling data source and it has unique instructions optimizing computational efficiency and driving cost savings. Moreover, to ensure cost-effectiveness, LLMs are selectively employed, while repetitive user inquiries are addressed through data retrieval from an aggregated storage. Consistent responses to user queries are provided through text and graphs revealing insights from drilling operations, standards, manuals, practices, and lessons learned. Applied methodology efficiently navigates inside the pre-processed user database relying on custom agents developed. Communication with the user is set in the form of chat framed within a web application, and queries on the database about hundreds of wells are answered in less than a minute. Methodology can analyze data and graphs by comparing Key Performance Indicators (KPIs). A wide range of graph output is represented by bar charts, scatter plots, and maps, including self-explaining charts like Time versus Depth Curve (TVD) with Non-Productive Time (TVD) events marked with details underneath. Understanding the data content, data preparation steps, and user needs is fundamental to a successful methodology application. The proposed Generative AI methodology is not just a tool for data interpretation, but a catalyst for real-time decision-making in complex drilling environments. Its integration into oil and gas drilling operations signifies a pivotal advancement, showcasing its transformative potential in revolutionizing the industry's landscape. This approach leads to notable cost reductions, improved resource utilization, and increased productivity, paving the way for a new era in drilling operations. A method driven by selective, cost-effective, and domain specific LLM agents stands poised to revolutionize drilling operations, seamlessly integrating generative AI to amplify efficiency and propel informed decision-making within the oil and gas drilling sector.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

The incorporation of generative artificial intelligence in governance, political knowledge, electoral, and democratic processes is essential as the world transitions to a digital paradigm. Numerous nations have adopted Generative AI (GenAI), a disruptive technology that compels electoral bodies to advocate for the integration of such tools into governance, electoral, and democratic processes. Nevertheless, these technologies do not ensure effortless integration or efficient usage owing to intricate socio-cultural and human dynamics. Certain African jurisdictions are ill-prepared for the adoption of these technologies due to factors including underdevelopment, insufficient electrical supply, lack of technology literacy, reluctance to change, and the goals of governing parties. This study examines generative artificial intelligence as a disruptive technology for enhancing governance, political knowledge, electoral processes, and democracy. A mixed-method approach was employed, incorporating surveys and in-person interviews. The analysis of data, debates, and interpretation of findings were grounded in postdigital theory and theme analysis employing an abductive reasoning technique, in alignment with the tenets of critical realism. The study demonstrated that GENAI can influence political knowledge, election processes, and enhance efficiency in government and democracy. Moreover, GENAI, including ChatGPT, can either exacerbate or mitigate societal tendencies that contribute to human division, facilitate the dissemination of misinformation, perpetuate echo chambers, and undermine social and political trust, as well as polarise disparate groups or sets of viewpoints or beliefs. AI offers substantial opportunities but also poses many obstacles, including technical constraints, ethical dilemmas, and social ramifications. The swift progression of AI may disrupt labour markets by automating tasks conventionally executed by people, resulting in job displacement. Implementing AI necessitates significant upskilling and proficiency with digital tools; therefore, governments and organisations must adequately train their personnel to reconcile the disparity between AI's capabilities and users' comprehension. Additionally, there is a requisite for governmental oversight, regulation, and monitoring of AI adoption and utilisation across all facets of its implementation.

Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.

Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship. Additionally, we highlight our most important generative AI ethics and equity considerations for these stakeholders. For clinicians, we recommend approaching generative AI similarly to other important biomedical advancements, critically appraising its evidence and utility and incorporating it thoughtfully into practice. For technologists developing generative AI for healthcare applications, we recommend a major frameshift in thinking away from the expectation that clinicians will “supervise” generative AI. Rather, these organizations and individuals should hold themselves and their technologies to the same set of high standards expected of the clinical workforce and strive to design high-performing, well-studied tools that improve care and foster the therapeutic relationship, not simply those that improve efficiency or market share. We further recommend deep and ongoing partnerships with clinicians and patients as necessary collaborators in this work. And for healthcare organizations, we recommend pursuing a combination of both incremental and transformative change with generative AI, directing resources toward both endeavors, and avoiding the urge to rapidly displace the human clinical workforce with generative AI. We affirm that the practice of medicine remains a fundamentally human endeavor which should be enhanced by technology, not displaced by it.

With cyberattacks increasing in volume and number, organizations are increasingly at risk of adverse financial and reputational impacts. Cyber attackers are quick to implement technologies like Generative Artificial Intelligence (GAI) to enhance attacks, while organizations have yet to fully benefit from GAI to improve cybersecurity defenses. This scoping literature review analyzes current research and identifies gaps in the literature about how Generative Artificial Intelligence (GAI) can be used to enhance cybersecurity resilience. The analysis includes an overview of GAI, ethical considerations and challenges, future directions and research opportunities, and a discussion of how this GAI research can be applied.

In the current spring of Artificial Intelligence, the rapid development of Generative AI (GenAI) has initiated vivid discussions in higher education. Opportunities as well as challenges have been identified and to cope with this new situation there is a need for a large-scale teacher professional development. With basic skills about GenAI teachers could use the new technology as an extension of the existing technology enhanced teaching and learning. The aim of this paper is to present and discuss the project FAITH (Frontline Application of AI and Technology-enhanced Learning for Transforming Higher Education). FAITH is a higher education pedagogical development initiative for institutional development for teachers with good fundamental skills in traditional pedagogy. A project with the overall objective of increasing the staff understanding of AI and to develop new competencies in the field of GenAI and technology enhanced learning. The research question that guided this study was: "What are the perceived opportunities, challenges and expectations of involving GenAI in higher education?" The overall research strategy for the FAITH project is design-based research, which involves iterative and cumulative development processes. In the early iteration that this study was a part of has been carried out inspired by Collective Autoethnography where members of the steering group behind the FAITH project, and members of the project team have constituted the main focus group. Data were collected by structured interviews where two GenAI tools also have been interviewed. Findings show that the expectations are high, but that the FAITH ambition of institutional development is depending on teachers’ motivation for taking an active part in the project. Another challenge could be that many teachers see GenAI as something that threatens the current course design, and that a general ban of GenAI is the appropriate solution. One of, several identified opportunities, is that a general revision of syllabi and assessment in an adaptation for GenAI enhanced learning would improve the current course design.