Bracing for Artificial General Intelligence

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

The present study investigates the potential impact of artificial intelligence (AI) on the future trajectory of human civilization. It focuses on topics such as super-exponential growth, the potential emergence of a galactic civilization, and the associated "doom" hazards. A significant advancement in machine intelligence with human-like consciousness, strong artificial intelligence (AI), also known as artificial general intelligence (AGI), creates new opportunities and capacities. There's growing anxiety about the risk that weak AI will eventually become strong AI. Every year, new transformer models that are more like human interactions are being created, and we have already witnessed some indications of AGI. It is anticipated that AI will reach a "singularity" and advance on its own without assistance from humans. This thesis explores the theoretical and practical foundations, model building blocks, development processes, challenges, and ethical issues surrounding the creation of Consciousness AI (AGI). This paper examines the meaning of the term "technological singularity," the various types of singularities that have no point of return idea, the philosophical risks associated with the development of AI, and the implications of AI singularity for monetary theory and the new economic order. As a new perspective on the deployment of ethical AI in the face of tremendous technological advancements, the study not only contributes to the theoretical discourse but also explores the possible practical implications of AI on our shared future. Several obstacles to AI advancement are covered in the paper, along with prospective directions for future research.

Background: Trastuzumab deruxtecan has substantially changed the treatment of HER2-low breast cancer, emphasizing the need for accurate differentiation between HER2 immunohistochemistry (IHC) scores of 0 and 1+. However, the current accuracy of HER2 IHC 0 and 1+ scoring in real-world is inadequate. Artificial intelligence (AI) has emerged as a potential solution to improve interpretation accuracy. We developed an AI algorithm based on whole slide images (WSI) to quantitatively assess HER2 expression and its role in interpreting HER2 IHC scores of 0 and 1+. Methods: Our three-phase AI analysis framework involved segmenting tumor areas (excluding ductal carcinoma in situ), detecting and classifying tumor cells based on membrane staining patterns, and grading HER2 IHC scores according to the 2018 ASCO/CAP HER2 guideline. The AI tool was trained using 6012 patches annotated by experienced pathologists. Performance evaluation was conducted using a test dataset comprising 265 slides. A total of 141 HER2 IHC slides (27 IHC 0 and 114 IHC 1+) from patients diagnosed with invasive breast cancer at Fudan University Shanghai Cancer Center in 2021 were included. Two pretrained expert pathologists independently rescored the HER2 slides, followed by analysis using the AI tool. All inconsistent cases were also reviewed by a third senior pathologist. Interobserver agreement between the pathologists and concordance between the pathologists and the AI interpretation results were assessed. We also explored potential ranges where HER2 interpretation by pathologists exhibited inconsistency or inaccuracy. Results: The HER2 AI algorithm showed high performance in interpreting all levels of HER2 expression, with an overall kappa value of 0.85. For HER2 IHC 0 and 1+ cases, the AI model demonstrated high sensitivities (0.839 and 0.914) and specificities (0.983 and 0.890) (Table 1). After the rescoring process, 51 cases were reclassified as IHC 0 and 90 cases as IHC 1+. The overall agreement rate between the historical and re-scoring results was 73.05% (103/141). The agreement rate between the AI and re-scoring results was 91.49% (129/141) (Table 2), indicating comparable interpretation capabilities between the AI model and well-trained pathologists. The interobserver agreement between the two pathologists was 83.69% (118/141), with agreement rates of 88.23% (45/51) for IHC 0 and 81.11% (73/90) for IHC 1+. Analyzing the inconsistent cases (n=12) between IHC 0 and IHC 1+, we examined the percentage of weak and incomplete expression provided by the AI. In 9 out of 12 cases, the AI provided the same results as the third senior pathologist, suggesting that AI could assist in interpretation within this range. We identified a range of 2.83% to 24.98% as a "grey-zone," where even well-trained pathologists provided inconsistent results. Conclusion: Our study demonstrates the excellent performance of an AI-based tool for scoring HER2 IHC 0 and 1+. The AI model exhibits comparable capabilities to well-trained pathologists in interpreting HER2 expression. Additionally, we identified a "grey-zone" among pathologists, highlighting the limitations of manual subjective interpretation. AI assistance within this zone may help mitigate subjective variations. Table 1. Correlation between pathologist read and AI read of HER2 immunohistochemistry. Table 2. Correlation between pathologist rescoring and AI read of HER2 immunohistochemistry. Citation Format: Ming Li, Hong Lv, Yizhi Zhao, Chenglu Zhu, Hansheng Li, Mingzhen Lin, Wen-Tao Yang. The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-26-09.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

Background: Generative Artificial Intelligence (AI), particularly large language models like ChatGPT, has rapidly entered medical education. In pharmacology instruction within MBBS programs, these AI tools offer novel educational benefitsfrom ondemand tutoring to personalized learningbut also pose risks due to "hallucinations, " i. e., AI -generated misinformation. Objective: This review explores how generative AI is being integrated into pharmacology education, the educational advantages it provides, the nature and causes of AI hallucinations, and the potential risks of relying on inaccurate AI -generated content in medical learning. Mitigation strategies, best practices, case examples, ethical considerations, and future directions are discussed to inform balanced and safe use. Methods: A literature review was conducted focusing on recent (2020-2025) peer -reviewed sources and authoritative reports on generative AI in medical and pharmacology education. Results: Generative AI can enhance pharmacology education through adaptive learning, rapid information access, and interactive simulations, which students have found useful for studying and clinical preparation. However, AI hallucinationsplausible -sounding yet incorrect answersare common, with studies reporting a substantial fraction of AI -generated medical responses containing errors. These inaccuracies can mislead learners and threaten patient safety if not recognized. Strategies such as careful validation of AI -provided information, AI literacy training, and technical approaches (e. g. retrieval -augmented generation) are identified to mitigate risks. Conclusions: Generative AI holds significant promise in medical pharmacology education by augmenting learning and engagement. Yet, educators and students must remain vigilant about AI hallucinations. A balanced approachleveraging AI's benefits while implementing safeguards and ethical guidelinesis essential to ensure that AI becomes a reliable adjunct in pharmacology education rather than a source of misinformation. Ongoing research, policy development, and training are needed to fully realize generative AI's potential in medical education while minimizing its risks.

This paper explains the ethical implications of artificial intelligence (AI) development towards achieving the level of artificial general intelligence (AGI) and analyzes the need for its social control. With the acceleration and intensification of AI growth and development, especially with the ongoing AI race, the transition from narrow AI to AGI becomes certain. The achievement of generative AI, which climaxes with chatbots (such as ChatGPT and others), transforms AI into a machine capable of creation. Although this AI application still appears relatively limited by algorithms, its learning ability is remarkable, and continuous advancements and the launch of increasingly sophisticated versions bring it ever closer to the AGI model. Each day brings us closer to that moment, which will signify AI’s transition from narrow AI to AGI. Unlike narrow AI, AGI deeply delves into the realm of ethics, and interpersonal and social relationships. Regulating AI-related policy and legally controlling AI represents one of the most serious and complex issues. In recent years, the community, led by corporate executives developing AI, prominent experts, researchers, scientists, writers, and other stakeholders, has made significant steps towards raising public awareness of the risks posed by advanced AI and making decisions, initiatives, and measures for monitoring, analyzing, and socially controlling the use of AI.

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., geothermal energy, oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the upstream geoenergy industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream geoenergy industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.

In today’s evolving world of work, Artificial Intelligence (AI) has emerged not merely as a technological tool but as a catalyst for rethinking how jobs are structured and performed (Bankins et al., 2024; Parker & Grote, 2022). Across diverse job families and organizational contexts, tools like generative AI (GAI) go beyond mere instruments for data analysis or structuring and can adopt increasingly far-reaching roles through rapid progress in generating new graphic or textual content or algorithmic decision making (Ferràs- Hernández, 2018; Walsh et al. 2019). Central to this symposium is the recognition that this changing nature of work due to digitalization, propelled by AI and related tools, calls for a fresh look at established theories of job design and strategies for human resource management. Traditionally, research has illuminated how fostering characteristics like autonomy, skill and task variety, or adequate demands affect employee motivation, satisfaction, and well-being and thereby further organizational outcomes (Oldham & Fried, 2016; Parker et al., 2017). Yet the introduction of AI – particularly generative AI – adds complexity: Who performs which tasks? How are decisions made, and by whom? Which skills remain uniquely human, and which may be augmented or even entirely adopted by AI? Such questions demand a more nuanced and strategic approach to designing work in the future that balances efficiency and innovation with psychological and ethical considerations. The four presentations in this symposium aim to contribute to theory building on how AI affects work in terms of work design, the interplay of employees and AI use, and work outcomes. In particular, they highlight potential risks or unique challenges when working with AI (e.g., in terms of less enriched work, lowered autonomy levels, threats to job identity or risks due to disclosure of use) and path the way for interventions to achieve benefits of such technologies at work. In detail, the empirical studies in this symposium illuminate these issues at three interconnected levels: 1. Task-Level Insights: Two contributions focus on the more granular task characteristics (e.g., autonomy, skill variety) in modern, AI-enhanced work contexts. They demonstrate how AI use influences the allocation of specific tasks when designing work roles and how the perception of task characteristics changes when AI is used in a decision-making task. 2. Job- and Identity-Level: One paper zooms out from individual tasks to jobs as a whole and explores how AI-driven change might disrupt employees’ job identity. This lens demonstrates how task changes cascade into bigger questions of role definition and identity. 3. Organizational Context: Finally, one presentation adopts the broadest perspective and investigates managerial and organizational factors that might shape employees’ willingness to disclose and ethically integrate AI tools. Taken together, these presentations point toward a future in which AI’s transformative potential hinges on thoughtful coordination across tasks, roles, and organizational systems. By combining established frameworks with new insights drawn from (quantitative and qualitative) surveys and experiments conducted in Germany, Austria, the United States, the United Kingdom, and Australia, these contributions illustrate how AI can enrich, rather than diminish, work and thereby strengthen human-centered organizations. By focusing on variables at different levels, the studies path the way for applications in practice, emphasizing the importance of individual approaches (e.g., training for job design, supporting self-efficacy), or on the organizational level (e.g., aligning HR practices, develop clear and supportive organizational policies and guidelines for the use of AI at work). Investigating the Influence of Work Design Perspective and AI Integration on Enriched Job Design Author: Tanja Bipp; Heidelberg University Author: Thea Ebert; Aalen University Collaborative Intelligence: Integrating Generative AI in Knowledge Work and its Impact on Job Design Author: Sabina Hodzic; University of Graz Author: Simon Grob; University of Graz Author: Bettina Kubicek; University of Graz Job Identity as Starting Point for SHRM in AI-Driven Organizational Change Author: Sophie Berretta; Ruhr University Bochum Author: Annette Kluge; Ruhr University Bochum Silent Adoption: Understanding the Drivers of Non-Disclosure of Generative AI Tools by Employees Author: Fangfang Zhang; Curtin University - Perth

Purpose This conceptual paper examines what underlies decision-making in generative change processes to explore how generative artificial intelligence (GAI) might shape the future of organisational change. Design/methodology/approach This investigation draws on Snowden and Stanbridge's (2004) social complexity concept, Archer's (1995) morphogenetic/morphostatic explanatory methodology and Mackenzie and Bititci's (2023) social systems-based model for organisational change to explain how structure, culture and agency influence generative change processes in complex adaptive social systems. Findings Both human-based decision-making and machine-based decision-making have roles to play in generative change. This paper proposes that human reflexivity mediates ideas, whereas the material aspects of artificial intelligence (AI) and GAI mediate tasks. The former shapes the change interventions that take place and the latter contributes to their more effective execution. Practical implications In generative change processes, AI and GAI technologies should be focused on tasks that support human-based decision-making. Originality/value This paper explores decision-making in organisational change from a social complexity perspective and identifies the complementary roles of human reflexivity and AI and GAI materiality in delivering emergent outcomes.

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.

Purpose Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era.

Generative artificial intelligence in dentistry: A narrative review of current approaches and future challenges

Fatty liver disease, also known as hepatic steatosis, poses a significant global health concern due to the excessive accumulation of fat within the liver. If left untreated, this condition can give rise to severe complications. Recent advances in artificial intelligence (AI, specifically AI-based ultrasound imaging) offer promising tools for diagnosing this condition. This review endeavors to explore the current state of research concerning AI's role in diagnosing fatty liver disease, with a particular emphasis on imaging methods. To this end, a comprehensive search was conducted across electronic databases, including Google Scholar and Embase, to identify relevant studies published between January 2010 and May 2023, with keywords such as "fatty liver disease" and "artificial intelligence (AI)." The article selection process adhered to the PRISMA framework, ultimately resulting in the inclusion of 13 studies. These studies leveraged AI-assisted ultrasound due to its accessibility and cost-effectiveness, and they hailed from diverse countries, including India, China, Singapore, the United States, Egypt, Iran, Poland, Malaysia, and Korea. These studies employed a variety of AI classifiers, such as support vector machines, convolutional neural networks, multilayer perceptron, fuzzy Sugeno, and probabilistic neural networks, all of which demonstrated a remarkable level of precision. Notably, one study even achieved a diagnostic accuracy rate of 100%, underscoring AI's potential in diagnosing fatty liver disease. Nevertheless, the review acknowledged certain limitations within the included studies, with the majority featuring relatively small sample sizes, often encompassing fewer than 100 patients. Additionally, the variability in AI algorithms and imaging techniques added complexity to the comparative analysis. In conclusion, this review emphasizes the potential of AI in enhancing the diagnosis and management of fatty liver disease through advanced imaging techniques. Future research endeavors should prioritize the execution of large-scale studies that employ standardized AI algorithms and imaging techniques to validate AI's utility in diagnosing this prevalent health condition.

Chapter 7 - Artificial general intelligence