General Intelligence Research Articles

Association of blood lead level with cognitive performance and general intelligence of urban school children in ten cities of India

BackgroundLead is toxic to children and effects the cognitive development. The primary objective was to assess the association of blood lead level (BLL) with cognitive performance and general intelligence, of urban school going children aged 6–16 years from ten cities of India. Secondary objective was to assess the association of anaemia with cognitive performance and general intelligence. MethodIn this cross-sectional multicentric study, general intelligence was assessed by colored progressive matrices (CPM)/standard progressive matrices (SPM) test, visual-spatial ability through coding, attention and concentration ability through digit span and working memory through arithmetic tests. Blood samples of participants were collected to assess lead levels. Socioeconomic status and anthropometric measures were also collected. ResultsFrom April 2019–February 2020, 2247 participants with equal gender distribution were enrolled from 60 schools. The median (IQR) BLL was 8.8 (4.8, 16.4) μg/dl. We found that BLL was significantly associated with attention and concentration ability and 1 μg/dl increase in BLL decreases the performance by odds of 1.02 (95 % CI 1.01–1.03). Anaemia is found to be associated with ‘borderline or dull normal’ performance for general intelligence [Adjusted Odds Ratio (AOR) = 1.93 (95 % CI 1.53–2.45)], visual-spatial [AOR = 1.35 (95 % CI 1.03–1.76)], attention and concentration [AOR = 1.30 (95 % CI 1.02–1.66)], working memory [AOR = 1.69 (95 % CI 1.34–2.12)] abilities. ConclusionSince increasing BLL decreases cognitive performance and anaemia decreased both cognitive performance and intelligence, attempts must be made to keep BLL as low as possible and prevent exposure in school going children.

Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks.

Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.

Difficulties in social cognitive functioning among pediatric patients with muscular dystrophies.

Pediatric muscular dystrophies (MDs) are a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration. A neuropsychosocial approach is crucial for these patients due to associated cognitive, behavioral, and psychiatric comorbidities; however, the social cognitive domain has not been adequately addressed. This study aimed to analyze on social cognition performance in a pediatric MD patient cohort. This cross-sectional study included 32 pediatric patients with MD and 32 matched-healthy controls. The Social Perception Domain of the NEPSY-II, the Reading the Mind in the Eyes Test-Child and Happé's Strange Stories Test were administered. General intelligence and behavioral and emotional symptoms were controlled for to eliminate covariables' possible influence. The assessments were performed remotely. Children with MDs performed significantly worse on most of the social cognition tasks. The differences found between the groups could be explained by the level of general intelligence for some aspects more related to theory of mind (ToM) (TM NEPSY-II: F = 1.703, p = .197; Verbal task: F = 2.411, p = .125; RMET-C: F = 2.899, p = .094), but not for emotion recognition. Furthermore, these differences were also independent of behavioral and emotional symptoms. In conclusion, social cognition is apparently impaired in pediatric patients with MD, both for emotion recognition and ToM. Screening assessment in social cognition should be considered to promote early interventions aimed at improving these patient's quality of life.

A Novel Model of Narrative Memory for Conscious Agents

The aim of Artificial General Intelligence (AGI) is to build machines with cognitive abilities on par with humans. These abilities include sensing, reasoning, decision-making, and social interaction. Although great strides have been achieved in creating intelligent agents that can observe and form opinions about their surroundings, problems still arise when it comes to their ability to reason and make decisions. The lack of a narrative structure inside these intelligent beings is a major drawback. This study suggests creating a narrative memory model with a dedicated module for use by self-aware agents. The model incorporates narrative memory to improve the agent's perception, understanding, and decision-making capabilities. The conscious agent will be able to create, store, and recall narrative-like representations of prior experiences with the help of the narrative memory module. To help the agent make sense of its surroundings, this narrative structure will give a method for categorizing and linking data. The agent will be able to recognize patterns, determine causal linkages, and extrapolate future outcomes by drawing on its narrative memory. There are several benefits to incorporating narrative memory into the brain of a self-aware agent. First, it'll give the agent a richer context from which to draw insights and make judgments. Second, the agent's perception and interpretation of complicated circumstances, as well as its general reasoning abilities, will be bolstered by the narrative memory module. Thirdly, the agent will be able to develop over time by absorbing new information and incorporating it into its preexisting narrative memory. This study uses a narrative memory-based approach to close the gap between conscious agents and human-like reasoning. The goal of this study is to improve the cognitive capacities of conscious agents by giving them the capacity to create and employ narrative memories, thus facilitating more nuanced perception, comprehension, and decision-making. The findings of this study could have far-reaching implications for the fields of artificial intelligence, robotics, and human-computer interaction by accelerating the creation of AGI systems with human-level cognitive capacities.

Integrating prior knowledge to build transformer models

The big Artificial General Intelligence models inspire hot topics currently. The black box problems of Artificial Intelligence (AI) models still exist and need to be solved urgently, especially in the medical area. Therefore, transparent and reliable AI models with small data are also urgently necessary. To build a trustable AI model with small data, we proposed a prior knowledge-integrated transformer model. We first acquired prior knowledge using Shapley Additive exPlanations from various pre-trained machine learning models. Then, we used the prior knowledge to construct the transformer models and compared our proposed models with the Feature Tokenization Transformer model and other classification models. We tested our proposed model on three open datasets and one non-open public dataset in Japan to confirm the feasibility of our proposed methodology. Our results certified that knowledge-integrated transformer models perform better (1%) than general transformer models. Meanwhile, our proposed methodology identified that the self-attention of factors in our proposed transformer models is nearly the same, which needs to be explored in future work. Moreover, our research inspires future endeavors in exploring transparent small AI models.

Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures

The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simulating the processes of ontophylogenetic synthesis of multi-agent neurocognitive architectures has been developed and experiments have been carried out to create phenotypes of intelligent agents based on them. A complex genome of an intelligent agent has been developed, the features of a multichromosome genetic algorithm for organizing calculations in the paradigm of multigenerational optimization of multiagent neurocognitive architectures have been established and substantiated. It is shown that multigenerational optimization of the multi-agent neurocognitive architecture of intelligent agents can contribute to the achievement of adaptive resistance to the operating conditions of a general artificial intelligence agent, provide the synthesis of its suboptimal structural and functional scheme, accelerate learning and algorithms for finding solutions to a universal range of problems solved by this agent in its ecological niche.

Music and Dance Training are not Uniquely Associated with Memory Skills

Music training is robustly associated with cognitive skills, and with tonal and verbal memory more specifically. However, it is unclear whether these associations reflect near or far transfer and/or whether they indicate pre-existing differences between those who do or do not take music lessons for long durations. Dance training may similarly rely on or train visuospatial memory abilities and produce exercise-induced benefits for working memory, but there is far less research on its associations with cognition. In Study 1, women with varying durations of formal music and dance experience completed measures of visual and auditory memory, general intelligence, demographics, and personality. Music training was associated with auditory immediate and delayed memory, as well as visual working memory, but all associations disappeared when other variables were held constant. Furthermore, dance training was not associated with any memory measure. Study 2 was similar but focused on visual memory and included both men and women. We replicated the simple association between duration of music training and visual working memory, which once again ceased to remain significant when controlling for other variables. Similarly, dance training failed to correlate with any visual memory measure despite the use of more valid visual memory tasks. Our findings suggest that memory advantages among musicians most likely result from pre-existing differences rather than near transfer and provide no evidence of transfer from dance training to visual memory.

Assessing harmonized intelligence measures in a multinational study.

Studies examining the neurocognitive and circuit-based etiology of psychiatric illness are moving toward inclusive, global designs. A potential confounding effect of these associations is general intelligence; however, an internationally validated, harmonized intelligence quotient (IQ) measure is not available. We describe the procedures used to measure IQ across a five-site, multinational study and demonstrate the harmonized measure's cross-site validity. Culturally appropriate intelligence measures were selected: four short-form Wechsler intelligence tests (Brazil, Netherlands, South Africa, United States) and the Binet Kamat (India). Analyses included IQ scores from 255 healthy participants (age 18-50; 42% male). Regression analyses tested between-site differences in IQ scores, as well as expected associations with sociodemographic factors (sex, socioeconomic status, education) to assess validity. Harmonization (e.g., a priori selection of tests) yielded the compatibility of IQ measures. Higher IQ was associated with higher socioeconomic status, suggesting good convergent validity. No association was found between sex and IQ at any site, suggesting good discriminant validity. Associations between higher IQ and higher years of education were found at all sites except the United States. Harmonized IQ scores provide a measure of IQ with evidence of good validity that can be used in neurocognitive and circuit-based studies to control for intelligence across global sites.

Exploring the relationship of adolescents resilience with subjective well-being and general intelligence

Exploring the relationship of adolescents resilience with subjective well-being and general intelligence

Association of clozapine with structural and resting-state functional abnormalities of the hippocampus in chronic schizophrenia.

Abnormalities in the hippocampus have been extensively reported in schizophrenia research. However, inconsistent findings exist, and how structural and functional abnormalities of the hippocampus are associated with clinical symptoms in schizophrenia, especially concerning clozapine treatment, remains uncertain. We recruited 52 patients with schizophrenia, each with an illness duration of at least 5 years, and categorized them based on clozapine treatment. T1-weighted images and resting-state functional magnetic resonance imaging scans were obtained and analyzed to perform group comparisons of the structural and functional changes in the hippocampus. Volumes of the hippocampal subregions, as well as resting-state functional connectivity maps from these areas were compared between the groups. Associations with clinical symptoms, including the severity of psychiatric symptoms and cognitive functions, were investigated. The clozapine group (n=23) exhibited smaller volumes in several hippocampal subregions, including the CA1, CA4, granule cell and molecular layers of the dentate gyrus, compared to the non-clozapine group (n=29). Seven clusters with significant group differences in functional connectivity with these hippocampal subregions were identified, with six of these clusters showing increased functional connectivity in the clozapine group. The reduced volumes of the hippocampal subregions were moderately associated with the severity of negative symptoms, general intelligence, and executive function. Patients with schizophrenia undergoing clozapine treatment exhibited smaller volumes in the hippocampal subregions, which were moderately associated with negative symptoms and cognitive functions, compared to those without clozapine treatment.

Enhancing Cybersecurity With Artificial Immune Systems and General Intelligence: A New Frontier in Threat Detection and Response

Enhancing Cybersecurity With Artificial Immune Systems and General Intelligence: A New Frontier in Threat Detection and Response

Parallel Driving with Big Models and Foundation Intelligence in Cyber-Physical-Social Spaces.

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the "6S" goals of parallel driving.

Proof of Achievement of the First Artificial General Intelligence (AGI)

Proof of Achievement of the First Artificial General Intelligence (AGI)

A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights from Highly Autonomous Aggressive Malware

A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights from Highly Autonomous Aggressive Malware

Lifelong Learning With Cycle Memory Networks.

Learning from a sequence of tasks for a lifetime is essential for an agent toward artificial general intelligence. Despite the explosion of this research field in recent years, most work focuses on the well-known catastrophic forgetting issue. In contrast, this work aims to explore knowledge-transferable lifelong learning without storing historical data and significant additional computational overhead. We demonstrate that existing data-free frameworks, including regularization-based single-network and structure-based multinetwork frameworks, face a fundamental issue of lifelong learning, named anterograde forgetting, i.e., preserving and transferring memory may inhibit the learning of new knowledge. We attribute it to the fact that the learning network capacity decreases while memorizing historical knowledge and conceptual confusion between the irrelevant old knowledge and the current task. Inspired by the complementary learning theory in neuroscience, we endow artificial neural networks with the ability to continuously learn without forgetting while recalling historical knowledge to facilitate learning new knowledge. Specifically, this work proposes a general framework named cycle memory networks (CMNs). The CMN consists of two individual memory networks to store short-and long-term memories separately to avoid capacity shrinkage and a transfer cell between them. It enables knowledge transfer from the long-term to the short-term memory network to mitigate conceptual confusion. In addition, the memory consolidation mechanism integrates short-term knowledge into the long-term memory network for knowledge accumulation. We demonstrate that the CMN can effectively address the anterograde forgetting on several task-related, task-conflict, class-incremental, and cross-domain benchmarks. Furthermore, we provide extensive ablation studies to verify each framework component. The source codes are available at: https://github.com/GeoX-Lab/CMN.

Evaluation of GPTs for Control Engineering Education: Towards Artificial General Intelligence

Recently introduced Generalized Pre-trained Transformers (GPT) and conversional chatbots such as ChatGPT are causing deep society transformations. The incorporation of these Artificial Intelligence technologies can be beneficial in multiple science and development areas including Control Engineering. The evaluation of GPTs within Control Engineering Education and PID control is addressed in this work. Different types of interactions with GPTs are evaluated and the use of a personalized GPT for PID tuning explored.

A Comprehensive Study on Self-Learning Methods and Implications to Autonomous Driving.

As artificial intelligence (AI) has already seen numerous successful applications, the upcoming challenge lies in how to realize artificial general intelligence (AGI). Self-learning algorithms can autonomously acquire knowledge and adapt to new, demanding applications, recognized as one of the most effective techniques to overcome this challenge. Although many related studies have been conducted, there is still no comprehensive and systematic review available, nor well-founded recommendations for the application of autonomous intelligent systems, especially autonomous driving. As a result, this article comprehensively analyzes and classifies self-learning algorithms into three categories: broad self-learning, narrow self-learning, and limited self-learning. These categories are used to describe the popular usage, the most promising techniques, and the current status of hybridization with self-supervised learning. Then, the narrow self-learning is divided into three parts based on the self-learning realization path: sample self-learning, model self-learning, and self-learning architecture. For each method, this article discusses in detail its self-learning capacity, challenges, and applications to autonomous driving. Finally, the future research directions of self-learning algorithms are pointed out. It is expected that this study has the potential to eventually contribute to revolutionizing autonomous driving technology.

Effective Surrogate Gradient Learning With High-Order Information Bottleneck for Spike-Based Machine Intelligence.

Brain-inspired computing technique presents a promising approach to prompt the rapid development of artificial general intelligence (AGI). As one of the most critical aspects, spiking neural networks (SNNs) have demonstrated superiority for AGI, such as low power consumption. Effective training of SNNs with high generalization ability, high robustness, and low power consumption simultaneously is a significantly challenging problem for the development and success of applications of spike-based machine intelligence. In this research, we present a novel and flexible learning framework termed high-order spike-based information bottleneck (HOSIB) leveraging the surrogate gradient technique. The presented HOSIB framework, including second-order and third-order formation, i.e., second-order information bottleneck (SOIB) and third-order information bottleneck (TOIB), comprehensively explores the common latent architecture and the spike-based intrinsic information and discards the superfluous information in the data, which improves the generalization capability and robustness of SNN models. Specifically, HOSIB relies on the information bottleneck (IB) principle to prompt the sparse spike-based information representation and flexibly balance its exploitation and loss. Extensive classification experiments are conducted to empirically show the promising generalization ability of HOSIB. Furthermore, we apply the SOIB and TOIB algorithms in deep spiking convolutional networks to demonstrate their improvement in robustness with various categories of noise. The experimental results prove the HOSIB framework, especially TOIB, can achieve better generalization ability, robustness and power efficiency in comparison with the current representative studies.

A functional contextual, observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence: safe AI through intersecting computational psychological neuroscience and LLM architecture for emergent theory of mind.

There have been impressive advancements in the field of natural language processing (NLP) in recent years, largely driven by innovations in the development of transformer-based large language models (LLM) that utilize "attention." This approach employs masked self-attention to establish (via similarly) different positions of tokens (words) within an inputted sequence of tokens to compute the most appropriate response based on its training corpus. However, there is speculation as to whether this approach alone can be scaled up to develop emergent artificial general intelligence (AGI), and whether it can address the alignment of AGI values with human values (called the alignment problem). Some researchers exploring the alignment problem highlight three aspects that AGI (or AI) requires to help resolve this problem: (1) an interpretable values specification; (2) a utility function; and (3) a dynamic contextual account of behavior. Here, a neurosymbolic model is proposed to help resolve these issues of human value alignment in AI, which expands on the transformer-based model for NLP to incorporate symbolic reasoning that may allow AGI to incorporate perspective-taking reasoning (i.e., resolving the need for a dynamic contextual account of behavior through deictics) as defined by a multilevel evolutionary and neurobiological framework into a functional contextual post-Skinnerian model of human language called "Neurobiological and Natural Selection Relational Frame Theory" (N-Frame). It is argued that this approach may also help establish a comprehensible value scheme, a utility function by expanding the expected utility equation of behavioral economics to consider functional contextualism, and even an observer (or witness) centric model for consciousness. Evolution theory, subjective quantum mechanics, and neuroscience are further aimed to help explain consciousness, and possible implementation within an LLM through correspondence to an interface as suggested by N-Frame. This argument is supported by the computational level of hypergraphs, relational density clusters, a conscious quantum level defined by QBism, and real-world applied level (human user feedback). It is argued that this approach could enable AI to achieve consciousness and develop deictic perspective-taking abilities, thereby attaining human-level self-awareness, empathy, and compassion toward others. Importantly, this consciousness hypothesis can be directly tested with a significance of approximately 5-sigma significance (with a 1 in 3.5 million probability that any identified AI-conscious observations in the form of a collapsed wave form are due to chance factors) through double-slit intent-type experimentation and visualization procedures for derived perspective-taking relational frames. Ultimately, this could provide a solution to the alignment problem and contribute to the emergence of a theory of mind (ToM) within AI.

Reservoir geophysical monitoring supported by artificial general intelligence and Q-Learning for oil production optimization

<p>Artificial general intelligence (AGI), or strong AI, aims to replicate human-like cognitive abilities across diverse tasks and domains, demonstrating adaptability and learning like human intelligence. In contrast, weak AI refers to systems designed for specific tasks, lacking the broad cognitive flexibility of AGI. This paper introduces a novel approach to optimize oil production by integrating fundamental principles of AGI with geophysical data inversion and continuous monitoring techniques. Specifically, the study explored how AGI-inspired algorithms, combined with established reinforcement learning (RL) techniques, can enhance borehole electric/electromagnetic monitoring and reservoir fluid mapping technology. This integration aims to mitigate the risk of unwanted water invasion in production wells while optimizing oil extraction. The proposed methodology leverages real-time geophysical data analysis and automated regulation of oil production. The paper begins by outlining the key features of AGI and RL, and then discusses their application in electric/electromagnetic monitoring to define optimal production policies. The effectiveness of this approach was verified through synthetic tests, showing significant improvements in production efficiency, resource recovery, and environmental impact reduction.</p>