General Intelligence Research Articles

The Future of Material Scientists in an Age of Artificial Intelligence.

Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI-models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI-approaches. To structure the discussion, a conceptual framework of an AI-ladder is introduced. This AI-ladder ranges from basic data-fitting techniques to more advanced functionalities such as semi-autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping-stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.

Development And Challenges of Generative Artificial Intelligence in Education and Art

Thanks to the rapid development of generative deep learning models, Artificial Intelligence Generated Content (AIGC) has attracted more and more research attention in recent years, which aims to learn models from massive data to generate relevant content based on input conditions. Different from traditional single-modal generation tasks that focus on content generation for a particular modality, such as image generation, text generation, or semantic generation, AIGC trains a single model that can simultaneously understand language, images, videos, audio, and more. AIGC marks the transition from traditional decision-based artificial intelligence to generative artificial intelligence, which has been widely applied in various fields. Focusing on the key technologies and representative applications of AIGC, this paper identifies several key technical challenges and controversies in the field. These include defects in cross-modal and multimodal generation, issues related to model stability and data consistency, privacy concerns, and questions about whether advanced generative models like ChatGPT can be considered general artificial intelligence (AGI). While this dissertation provides valuable insights into the revolution and challenge of generative AI in art and education, it acknowledges the sensitivity of generated content and the ethical dilemmas it may pose, and ownership rights for AI-generated works and the need for new intellectual property norms are subjects of ongoing discussion. To address the current technical bottlenecks in cross-modal and multimodal generation, future research aims to quantitatively analyze and compare existing models, proposing practical optimization strategies. With the rapid advancement of generative AI, we anticipate a transition from user-generated content (UGC) to artificial intelligence-generated content (AIGC) and, ultimately, a new era of human-computer co-creation with strong interactive potential in the near future.

Meta-analysis of the relationship between the number and location of perivascular spaces in the brain and cognitive function.

Cerebral perivascular spaces are part of the cerebral microvascular structure and play a role in lymphatic drainage and the removal of waste products from the brain. Relationships of the number and location of such spaces with cognition are unclear. To meta-analyze available data on potential associations of severity and location of perivascular spaces with cognitive performance. We searched PubMed, EMBASE, Web of Science and the Cochrane Central Registry of Controlled Trials for relevant studies published between January 2000 and July 2023. Performance on different cognitive domains was compared to the severity of perivascular spaces in different brain regions using comprehensive meta-analysis. When studies report unadjusted and adjusted means, we use adjusted means for meta-analysis.The study protocol is registered in the PROSPERO database (CRD42023443460). We meta-analyzed data from 26 cross-sectional studies and two longitudinal studies involving 7908 participants. In most studies perivascular spaces was using a visual rating scale. A higher number of basal ganglia perivascular spaces was linked to lower general intelligence and attention. Moreover, increased centrum semiovale perivascular spaces were associated with worse general intelligence, executive function, language, and memory. Conversely, higher hippocampus perivascular spaces were associated with enhanced memory and executive function. Subgroup analyses revealed variations in associations among different disease conditions. A higher quantity of perivascular spaces in the brain is correlated with impaired cognitive function. The location of these perivascular spaces and the underlying disease conditions may influence the specific cognitive domains that are affected. The study protocol has been registered in the PROSPERO database (CRD42023443460).

Editorial: Evolution of large language models and their role in shaping general artificial intelligence

Editorial: Evolution of large language models and their role in shaping general artificial intelligence

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

Development and validation of a prehospital termination of resuscitation (TOR) rule for out - of hospital cardiac arrest (OHCA) cases using general purpose artificial intelligence (AI)

BackgroundPrehospital identification of futile resuscitation efforts (defined as a predicted probability of survival lower than 1%) for out-of-hospital cardiac arrest (OHCA) may reduce unnecessary transport. Reliable prediction variables for OHCA 'termination of resuscitation' (TOR) rules are needed to guide treatment decisions. The Universal TOR rule uses only three variables (Absence of Prehospital ROSC, Event not witnessed by EMS and no shock delivered on the scene) has been externally validated and is used by many EMS systems. Deep learning, an artificial intelligence (AI) platform is an attractive model to guide the development of TOR rule for OHCA. The purpose of this study was to assess the feasibility of developing an AI-TOR rule for neurologically favorable outcomes using general purpose AI and compare its performance to the Universal TOR rule. Methods: We identified OHCA cases of presumed cardiac etiology who were 18 years of age or older from 2016 to 2019 in the All-Japan Utstein Registry. We divided the dataset into 2 parts, the first half (2016–2017) was used as a training dataset for rule development and second half (2018–2019) for validation. The AI software (Prediction One®) created the model using the training dataset with internal cross-validation. It also evaluated the prediction accuracy and displayed the ranking of influencing variables. We performed validation using the second half cases and calculated the prediction model AUC. The top four of the 11 variables identified in the model were then selected as prognostic factors to be used in an AI-TOR rule, and sensitivity, specificity, positive predictive value, and negative predictive value were calculated from validation cohort. This was then compared to the performance of the Universal TOR rule using same dataset. ResultsThere were 504,561 OHCA cases, 18 years of age or older, 302,799 cases were presumed cardiac origin. Of these, 149,425 cases were used for the training dataset and 153,374 cases for the validation dataset. The model developed by AI using 11 variables had an AUC of 0.969, and its AUC for the validation dataset was 0.965. The top four influencing variables for neurologically favorable outcome were Prehospital ROSC, witnessed by EMS, Age (68 years old and younger) and nonasystole. The AUC calculated using the 4 variables for the AI-TOR rule was 0.953, and its AUC for the validation dataset was 0.952 (95%CI 0.949 –0.954). Of 80,198 patients in the validation cohort that satisfied all four criteria for the AI-TOR rule, 58 (0.07%) had a neurologically favorable one-month survival. The specificity of AI-TOR rule was 0.990, and the PPV was 0.999 for predicting lack of neurologically favorable survival, both the specificity and PPV were higher than that achieved with the universal TOR (0.959, 0.998). ConclusionsThe accuracy of prediction models using AI software to determine outcomes in OHCA was excellent and the AI-TOR rule’s variables from prediction model performed better than the Universal TOR rule. External validation of our findings as well as further research into the utility of using AI platforms for TOR prediction in clinical practice is needed.

Trajectories of AI technologies: Insights for managers

Generative artificial intelligence (GenAI) has long been considered a technology for the future. With the release of the chatbot ChatGPT 4, many now feel the future has arrived. Long in gestation, this new technology promises many benefits to humankind, but worries persist that as AI technology scales and comes to rival or exceed human intelligence, the servant may become the master. Amid such hyperbole, the more nuanced trajectories of this technology have been neglected. In this article, we use the Trajectories of Technology (ToT) framework developed by Berthon and colleagues to explore the disparate paths that AI has taken and will take in the coming years, especially in the form of chatbots. This framework provides managers with a conceptual tool to strategically plan for the enormous promises and perils of AI in general and of chatbots specifically.

Person-Hood Issues Related To Artificial Intelligence

The abstract explores the intricate and ethically challenging subject of personhood issues in the realm of Artificial Intelligence (AI). As AI continues to advance, questions regarding the legal status, rights, and responsibilities of AI systems emerge, sparking a profound debate on whether AI entities should be granted personhood. The discussion begins by framing the current state of AI, acknowledging its increasing sophistication and integration into various aspects of human life. The focus then shifts to the core question: Should AI systems be endowed with legal personhood? This query becomes more pertinent as AI evolves towards achieving artificial general intelligence, comparable to human cognitive abilities. The abstract delves into the philosophical and ethical dimensions surrounding personhood, drawing parallels with historical debates on consciousness and humanity. The concept of personhood for AI is examined through the lens of ownership, accountability, representation, and management. The critical juncture is highlighted, where the evolution of AI, particularly towards artificial general intelligence, prompts a reassessment of the legal and ethical frameworks governing these entities. Notable considerations include the potential implications of personhood for AI, such as issues of ownership and accountability. The abstract raises thought-provoking scenarios, questioning the consequences of AI entities being granted legal status. The potential accumulation of wealth, rights, and influence by autonomous AI entities poses challenges to existing societal structures. Drawing on analogies from corporate law, the abstract explores the possibility of AI entities having a distinct legal status, separate from their human creators. However, it cautions against succumbing to the humanoid hype, emphasizing the need for a nuanced understanding of the implications of granting legal personhood to AI. In conclusion, the abstract underscores the urgency of addressing personhood issues related to AI. It navigates through the complex intersection of technology, ethics, and law, encouraging a thoughtful approach to the evolving nature of AI entities and their potential impact on societal structures. As AI progresses, defining the legal and ethical boundaries of personhood for these entities becomes a pivotal task to ensure responsible and equitable integration into human society.

Prospective research in the field of teaching creative skills to artificial intelligence

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable

NEED/ROLE OF GENERATIVE AI IN PHARMACEUTICAL RESEARCH

Dear Reader, In current times, when countries like Singapore are relying mainly on "Gen AI" (General Artificial Intelligence) for hastening the growth and modernization of the pharma industry, pharma leaders like India need to incorporate the latest trends in digital to Gen AI in the curriculum of the pharmacy degree and post-graduate degrees.

Artificial intelligence is algorithmic mimicry: why artificial “agents” are not (and won’t be) proper agents

What is the prospect of developing artificial general intelligence (AGI)? I investigate this question by systematically comparing living and algorithmic systems, with a special focus on the notion of “agency.” There are three fundamental differences to consider: (1) Living systems are autopoietic, that is, self-manufacturing, and therefore able to set their own intrinsic goals, while algorithms exist in a computational environment with target functions that are both provided by an external agent. (2) Living systems are embodied in the sense that there is no separation between their symbolic and physical aspects, while algorithms run on computational architectures that maximally isolate software from hardware. (3) Living systems experience a large world, in which most problems are ill defined (and not all definable), while algorithms exist in a small world, in which all problems are well defined. These three differences imply that living and algorithmic systems have very different capabilities and limitations. In particular, it is extremely unlikely that true AGI (beyond mere mimicry) can be developed in the current algorithmic framework of AI research. Consequently, discussions about the proper development and deployment of algorithmic tools should be shaped around the dangers and opportunities of current narrow AI, not the extremely unlikely prospect of the emergence of true agency in artificial systems.

Becoming-with-AI: Rethinking Professional Knowledge Through Generative Knowing

ABSTRACT ChatGPT presents a significant disruption in the ways of knowing that highlight the value of control and mastery of knowledge as a quest for certainty. ChatGPT, as a step toward Artificial General Intelligence (AGI) lets us surface Schön’s prescient insight on the crisis of confidence in professionals. Instead of hastily asking questions about how to cope with the advances in artificial intelligence from the gaze of deficit and improvement, however, we resort to the nonrepresentational epistemologies that highlights reflection as seeing as . How might we reconceptualize a professional knowledge model that encourages ethical commitment to enacting practices that deploy relational forces within high-pressure, time-constrained professional environments? We attempt to answer the question by proposing a theory that cultivates conditions for experimenting with experiential encounters with others and creatively activating potentials, which we call generative knowing. Doing so, we suggest how compassionately disruptive condition mobilizes professional learners to engage the machinic creativity that collapses semiotic chains and effectuates diverse imaginations of the future of becoming-with ChatGPT.

Surviving ChatGPT in healthcare.

At the dawn of of Artificial General Intelligence (AGI), the emergence of large language models such as ChatGPT show promise in revolutionizing healthcare by improving patient care, expanding medical access, and optimizing clinical processes. However, their integration into healthcare systems requires careful consideration of potential risks, such as inaccurate medical advice, patient privacy violations, the creation of falsified documents or images, overreliance on AGI in medical education, and the perpetuation of biases. It is crucial to implement proper oversight and regulation to address these risks, ensuring the safe and effective incorporation of AGI technologies into healthcare systems. By acknowledging and mitigating these challenges, AGI can be harnessed to enhance patient care, medical knowledge, and healthcare processes, ultimately benefiting society as a whole.

There is nothing like a Delphi Artificial Intelligence Model (DAIM)

Key digested messageThis article explores an innovative ensemble approach integrating multiple AI models to achieve consensus in psychological assessments in the educational psychology domain. Addressing the rapid advancements in Artificial General Intelligence (AGI), it emphasises the importance of principles over specific models in this fast-evolving field. The study compares responses from various AI models using a set of literacy and socioeconomic questions. The findings highlight the diversity of responses from individual models and the holistic ensemble perspective offered by DAIM. The article underscores the potential shift towards Artificial Specific Intelligence (ASI) in psychology, advocating for a focus on multi-model approaches and, potentially, a re-evaluation of humanconsensus in psychological research.

Analisis Pengaruh Variabel Nilai TIU, TWK, Dan TKP Terhadap Kelulusan SKD Pada Tes CPNS Menggunakan Analisa Bivariat Sederhana

This study aims to assess the impact of the General Intelligence Test (TIU), National Insight Test (TWK), and Personal Characteristics Test (TKP) on the success of the Basic Competency Selection (SKD) for Civil Servant Candidate (CPNS). Using data from participants in the Ministry of Law and Human Rights' SKD selection in 2023, we employed univariate analysis, simple bivariate analysis, and binning methods to comprehend variable relationships. Results reveal non-normal distributions for TIU, TWK, and TKP scores, highlighting the intricate nature of distribution in CPNS selection. While a positive correlation exists between variable values and total SKD score, binning analysis emphasizes TKP competitiveness over TIU and TWK scores. These findings offer practical insights for SKD participants to prepare effectively, with a focus on TIU, TWK, and TKP. Additionally, they contribute to transparency and effectiveness in the CPNS selection process. Further studies are recommended to explore additional factors, like age and gender, for the development of more holistic and accurate selection methods. This research supports enhancements in the adaptive and efficient CPNS SKD selection system.

Relationship Between Actual Intelligence & the CAMCOG-R in Cognitively Normal Elders

Abstract: We examined (1) the relationship between actual intelligence and the CAMCOG-R performance in cognitively normal elders and (2) the presumed impact of age and education on the relationship. Our retrospective study of 94 participants assessed in a specialized setting, measuring actual intelligence (general and specific) with the Wechsler Adult Intelligence Scale-III, the Full-Scale Intelligence Quotient (WAIS-III FSIQ), and the specific subtests thereof. The CAMCOG-R correlated largely with FSIQ (.629, p < .001; .708 when controlled for age). The CAMCOG-R’s scores increased across three different IQ levels as IQ increased. FSIQ accounted for 40% of the CAMCOG-R’s variability. Combined, FSIQ and age accounted for 59% of the CAMCOG-R’s variability. FSIQ had a stronger weight than age (β: .644 and −.439, respectively). Regression analyses with FSIQ’s subtests, age and education, found the best-equilibrated model to include Information, age, and Picture Completion. Combined, they accounted for 55% of the CAMCOG-R’s variability. Information received the strongest weight (β: .411). Overall, education was nonsignificant. Thus, a significant relationship existed primarily between general intelligence and the CAMCOG-R and, to a lesser extent, between the above-mentioned subtests of intelligence and the CAMCOG-R. Adding age increased the prediction rate. Actual intelligence and age should be considered when interpreting the CAMCOG-R’s scores. The practical implementation of the intelligence factor is complex.

A research of artificial intelligence game agent application

Currently, large language models are on the rise with breakthrough progress in artificial intelligence. Existing reviews of AI game agents have not covered these latest developments, requiring a combing and analysis of the newest research advancements in game AI agents. This paper summarizes the application scenarios of game AI agents in four aspects: combat AI, Non-Player Character (NPC) interaction, automated testing, and Artificial General Intelligence (AGI) testing. In combat AI, there is a progressive developmental trend, with the introduction of Monte Carlo tree search and reinforcement learning enabling AI game agents to fully surpass humans in traditional board games. In NPC interaction, full AI is unnecessary. Game developers only need to incorporate AI for abilities related to player experience to increase appeal, with controllable generation results. In automated testing, game AI agents lack generalizability for testing so far. In AGI testing, academia has helpfully explored general game AI, but capabilities remain limited to certain games. Introducing large language models to game AI agents shows unprecedented capabilities. Finally, this paper provides an outlook on the hot topics and future directions of this research subject.

When tomorrow comes: A prospective risk assessment of a future artificial general intelligence-based uncrewed combat aerial vehicle system

There are concerns that Artificial General Intelligence (AGI) could pose an existential threat to humanity; however, as AGI does not yet exist it is difficult to prospectively identify risks and develop requisite controls. We applied the Work Domain Analysis Broken Nodes (WDA-BN) and Event Analysis of Systemic Teamwork-Broken Links (EAST-BL) methods to identify potential risks in a future ‘envisioned world’ AGI-based uncrewed combat aerial vehicle system. The findings suggest five main categories of risk in this context: sub-optimal performance risks, goal alignment risks, super-intelligence risks, over-control risks, and enfeeblement risks. Two of these categories, goal alignment risks and super-intelligence risks, have not previously been encountered or dealt with in conventional safety management systems. Whereas most of the identified sub-optimal performance risks can be managed through existing defence design lifecycle processes, we propose that work is required to develop controls to manage the other risks identified. These include controls on AGI developers, controls within the AGI itself, and broader sociotechnical system controls.

On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper

AbstractThe ability to coactivate (or “superpose”) multiple conceptual representations is a fundamental function that we constantly rely upon; this is crucial in complex cognitive tasks requiring multi-item working memory, such as mental arithmetic, abstract reasoning, and language comprehension. As such, an artificial system aspiring to implement any of these aspects of general intelligence should be able to support this operation. I argue here that standard, feed-forward deep neural networks (DNNs) are unable to implement this function, whereas an alternative, fully brain-constrained class of neural architectures spontaneously exhibits it. On the basis of novel simulations, this proof-of-concept article shows that deep, brain-like networks trained with biologically realistic Hebbian learning mechanisms display the spontaneous emergence of internal circuits (cell assemblies) having features that make them natural candidates for supporting superposition. Building on previous computational modelling results, I also argue that, and offer an explanation as to why, in contrast, modern DNNs trained with gradient descent are generally unable to co-activate their internal representations. While deep brain-constrained neural architectures spontaneously develop the ability to support superposition as a result of (1) neurophysiologically accurate learning and (2) cortically realistic between-area connections, backpropagation-trained DNNs appear to be unsuited to implement this basic cognitive operation, arguably necessary for abstract thinking and general intelligence. The implications of this observation are briefly discussed in the larger context of existing and future artificial intelligence systems and neuro-realistic computational models.

A Cognitive Robotics Implementation of Global Workspace Theory for Episodic Memory Interaction With Consciousness

Artificial general intelligence revived in recent years after people achieved significant advances in machine learning and deep learning. This leads to the thinking of how real intelligence could be created. Consciousness theories believe that general intelligence is essentially conscious, yet no universal definition is agreed upon. In this work, Global Workspace Theory is implemented and integrated with crucial cognitive components. With the focus on episodic memory and inspiration from the nature of episodic memory in psychology and neuroscience, the episodic memory component is implemented within the Global Workspace framework. In our experiment, the robotic agent operates in a real-world interactive context, forming episodic memory and demonstrating static, temporal, and context memory capabilities during interactions. Consciousness in this work engages in all formation, maintenance, and retrieval processes of episodic memory. The novelty and contributions of this work are 1) this work is implementing episodic memory within the consciousness framework, suggesting the sustainable potential of such an integrated approach to cognitive agents with AGI; 2) Regarding the limited examples in consciousness-based cognitive architectures, this work attempts to contribute to the diversity of perspectives and approaches; 3) Extant episodic memory implementations are suffering from various limitations, while this work summarises some key features for modelling episodic memory within a cognitive architecture; 4) Authors discuss the relationship between episodic memory, consciousness, and general intelligence, proposing the compatibility and relationship between machine consciousness and other AGI research. It is believed that a better alignment between them would further boost the fusion of diverse research for achieving desired cognitive machines.