General Intelligence Research Articles

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)

Artificial General Intelligence for Medical Imaging Analysis.

Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized domains like medical imaging, which require in-depth expertise, these models face notable challenges arising from the medical field's inherent complexities and unique characteristics. In this review, we delve into the potential applications of AGI models in medical imaging and healthcare, with a primary focus on LLMs, Large Vision Models, and Large Multimodal Models. We provide a thorough overview of the key features and enabling techniques of LLMs and AGI, and further examine the roadmaps guiding the evolution and implementation of AGI models in the medical sector, summarizing their present applications, potentialities, and associated challenges. In addition, we highlight potential future research directions, offering a holistic view on upcoming ventures. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.

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

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>

On Hardware Flexibility and Heterogeneity: A Vision for Monte Carlo Codes on Incoming RISC-V Computing Devices with AI-based Cross Section

As an open-sourced instruction set and being flexible in hardware extension, RISC-V begins its pace to enter the world of high performance computing. One of the distinguished feature of processing units adopting RISC-V is its ability to add custom circuits with special purpose accelerators. As the artificial general intelligence becomes practical, AI accelerators become an indispensable part of computing devices, where RISC-V is a great fit for the CPU to glue accelerators together. A system of chip designed by Alibaba T-head is one of the early chip in the massive production adopting RISC-V CPU, where the CPU, named Xuantie-910, has a high performance design with 128-bit RISC-V vector processing units, which are designed for accelerating AI applications. OpenMC has been adapted to run on Xuantie-910. In the Monte Carlo method for reactor physics, fetching the neutron cross sections is the hotspot that takes the majority of the computational burden. The traditional point-wise cross sections are slow because of memory latency caused by accessing many nonconsecutive memory addresses. An AI model for cross section is hence proposed. With 2.2 KB of runtime size, the smallest in the published work, the data can be fetched entirely in the L1 cache during on-the-fly cross section evaluation through single memory read. The in-house AI model also covers the entire energy range, unlike only the resonance range is supported in previous work. So, the effects from memory latency is minimized. The average relative error in AI modeled U-238 elastic cross section is 0.6% from point-wise cross section. With a modified version of OpenMC on Apple M3 Max, for a VERA pin-cell problem, compared to the point-wise cross section, the adoption of AI modeled cross section reduces the total runtime by 7%, although the runtime for calculating U-238 elastic cross section causes 40% more runtime. The adoption of AI modeled U-238 elastic cross section leads to K-effective 302 pcm higher than the case of adoption of point-wise cross sections. Advantage of AI model has been verified. With AI modeled cross section, the neutron slowing down problems with pure elastic scattering on U-238 has been studied on Xuantie-910. The average relative error in 65,536 group fluxes is about 0.9% from using point-wise cross section. However, with accelerating with the 128-bit vector processing units, the performance degrades by 35%, because of the narrow 64-bit load and store interface to the vector register files. The performance with Al modeled cross section is about 1/4 of the case with point-wise cross sections. In addition, the 1,024-bit width Ara RISC-V vector processing has been used to study the cost of AI modeled cross section evaluation. Being able to access the open-sourced hardware design in SystemVerilog, cycle accurate circuit simulation is performed. Using the vector processing units, the cost is reduced to 65% of the case using scalar instructions. The 128-bit load and store interface to vector processing units is a major contributor to the speeding up. The width of the load and store interface to vector processing units should be the main optimization factor in chip design to accelerate the AI modeled cross section evaluation.

Centralization of power as a result of the development of the egyptian elite during 2014-2023. Forward to the past?

This article attempts to analyze the key groups of the Egyptian elite in 2014-2023 in accordance to the elite theory. A.F. El-Sisi was first elected to the presidency of Egypt in 2014 after the 2013 military coup that overthrew former Egyptian President Mohamed Morsi (in position 2012-2013). There is an opinion that with the advent of A.F. El-Sisi to power there was a rollback of the political system to pre-revolutionary times until 2011. Nevertheless, as a result of an analysis, the article shows significant differences between the regimes of Presidents H. Mubarak and A.F. El-Sisi, especially in relation to the opposition. Despite the relative success in security issues, Egypt remains in a tense economic situation, rising inflation and dependence on external debt (among the creditors both the Gulf States and international institutions like IMF). The mega-projects in accordance with the “Sustainable Development Strategy (SDS): Egypt Vision 2030” do not have enough positive impact on the economy. In this regard, Egypt is facing a complex of structural geopolitical and economic challenges. The paper demonstrates the relationship between key state institutions, and also reveals the strengthening of the role and influence of the General Intelligence Service (GIS) in the political process. At the same time, GIS itself underwent serious personnel changes - infiltration by the military intelligence of the Armed Forces (where the president came from).Despite the repeating resistance of certain parts of the elite (both business elites and the religious establishment), President A.F. El-Sisi consistently promotes the centralization of power.

Activation Functions Study for the Trustworthiness Supervisor Artificial Neural Networks

Examining and potentially adjusting one’s cognitive processes in response to dissatisfaction with one’s performance is a fundamental aspect of intelligence. Remarkably, such sophisticated abstract concepts necessary for achieving Artificial General Intelligence can be effectively incorporated into basic Machine Learning algorithms. In this study, we introduce a method for replicating self-awareness through a supervisory Artificial Neural Network (ANN), which monitors patterns in the activation functions of an underlying ANN to identify signs of substantial uncertainty within the underlying ANN and, consequently, the reliability of its predictions. The underlying ANN in this context is a Convolutional Neural Network (CNN) ensemble primarily utilized for tasks related to facial recognition and facial expression analysis. We evaluate the performance of the supervisory ANNs using various activation functions as they learn to gauge the dependability of predictions made by the Inception v3 CNN ensemble. To conduct computational experiments, we employ a facial data set that incorporates makeup and occlusion factors. These experiments are designed to mimic real-world conditions where the training data set exclusively consists of images without makeup or occlusion, while the test data set comprises images featuring makeup and occlusion. This partitioning ensures the model is tested under challenging out-of-training data distribution scenarios.

Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals' neural states during a semantic inference reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use feature engineering to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60% across 12 subjects. Words highly relevant to the inference keyword received significantly more eye fixations per word: 1.0584 compared to 0.6576, including words with no fixations. This study represents the first attempt to classify brain states at a word level using LLM-generated labels. It provides valuable insights into human cognitive abilities and Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies.

Infinity: A Unified Solution of General Intelligence

Infinity: A Unified Solution of General Intelligence

An Ethic of Military Uses of Artificial Intelligence: Sustaining Virtue, Granting Autonomy, and Calibrating Risk

Artificial intelligence in military operations comes in two kinds. First, there is narrow or specific intelligence – the autonomous ability to identify an instance of a species of target, and to track its changes of position. Second, there is broad or general intelligence – the autonomous ability to choose a species of target, identify instances, track their movements, decide when to strike them, learn from errors, and improve initial choices. These two kinds of artificial intelligence raise ethical questions mainly because of two features: the physical distance they put between the human agents deploying them and their targets, and their ability to act independently of those agents. The main ethical questions these features raise are three. First, how to maintain the traditional martial virtues of fortitude and chivalry while operating lethal weapons at a safe distance? Second, how much autonomy to grant a machine? And third, what risks to take with the possibility of technical error? This paper considers each of these questions in turn.

Impact of AI-Powered Technology on Religious Practices and Ethics: The Road Ahead

This article addresses the time lag problem with which human beings catch up with the speed with which technology in general and artificial intelligence (AI) in particular advance. The purpose of this exploratory research is to examine the impact of AI on religious practices and ethics, exploring the ways in which AI influences religious practices as well as the manner by which religious institutions may respond to ethical concerns and other challenges. This study will answer the following questions: Considering the ethical implications: 1) What are the potential positive benefits and 2) negative impacts of the use of technology, AI, and mobile apps on religious practices? 3) What are some ethical considerations? 4) What are the tasks ahead to promote ethical standards in the use of AI in religious practices? As there are ethical issues involved, religious institutions can play an active role in promoting ethical practices in the use of AI in religious practices. Keywords: artificial intelligence, ethics, religious practices, mobile apps, technology

Quotient Types from Mahabharata & Ramayana

Purpose: Our world is shifting at an exponential rate. Difficulties are more complicated. Uncertainty is the norm. Improving our Qs is seen as one of the most commanding quotients for success. The quotients in today’s concern are ones that will have a predominant influence on life and business. Quotients are the degree or expanse of a specified quality/characteristic. We know there are several quotients used to portray specific characteristics of an individual. For example, most individuals have heard of IQ, also known as the Intelligence Quotient, which consists of a score designed to gauge the overall intelligence of a human being. However, not everyone knows other quotient types exist and represent much more about an individual, including their spiritualness, level of creativity, and ability to communicate and learn in specific ways. The researcher’s paper also throws light on how it works and how to recharge Qs tests & training at a workplace if one tries it as his/her daily menu. Which of these Qs is most essential for a leader? or which combination? Does a leader need to score strongly in all areas, or to employ those with the necessary scores to complement their own deficiencies? Are the questions to be answered? Thus, linking mythological themes & management with the present-day materialistic world is not easy. The present research links and contextualizes the concepts of Qs through Mahabharata & Ramayana with the ancient Mythology. Objectives: The researchers have set the undermentioned primary objectives and the existing study investigates 3 broad research questions: (1) To evaluate quotients types that have an impact on individual from Mahabharata & Ramayana characters. (2) To examine the need for Qs for workplace tests & training. (3) To identify the Q's impact on individual behaviour. Design/Methodology/Approach: The study is based on both secondary & primary evidence. Hypotheses are stated for the various variables selected and analyzed to prove or disprove the same without any errors. Findings/Result: Qs tests & training are a leading light in the corporate learning and team building industries with large and small organizations to assist team members in better understanding one another while effectively collaborating and boosting individual and team morale and productivity in the workplace with Qs test. Trying workplace Qs tests & training is a new catchword. Researchers have found when co-workers participate in Qs-related training programs, it may help improve their: teamwork, conflict management abilities, job performance & general job satisfaction. Qs also gives you a chance to submerge yourself in the know-how of other people whether those people really exist or are fictional characters. Studies show that Qs may help improve your social thinking skills, which researchers think is noteworthy for building empathy. Researchers have found that when people practice Qs between ideas and objects, scores on measures of general intelligence tend to improve. Research Limitations/Implications: There’s no proof that cognitive training raises overall Qs. Though, you can and should continue to learn throughout your life. The keys to learning tend to encompass curiosity and being receptive to new information. With those qualities Qs, you can enhance your ability to: Concentrate, remember details, empathize, grasp new concepts, enrich your imagination, research & add to your knowledge base. Originality/New Knowledge/Interpretation/Value: Qs tests have gone through substantial changes through the decades to correct racial, gender, and social biases, as well as cultural norms. Today, there are several versions in use. They may have different methods of scoring, but they all are used in one way or the other. Qs tests can gauge language, processing, memory, and reasoning and can tend to check whether Qs remain consistent from childhood to adulthood in an individual. Paper Type: The study is based on both secondary & primary evidence.