Computed Research Articles

A Comparative Study of Artificial and Natural Intelligence from Ibn Sina’s Perspective

The differentiation between natural intelligence and artificial intelligence represents a significant concern among intellectuals. Artificial intelligence developers, leveraging advancements in neuroscience, cognitive sciences, and advanced theories in the philosophy of mind, aim to replicate the structure and functionality of the human brain through a functionalist and behaviourist lens. Broadly speaking, artificial intelligence can be categorized into two renowned types:Classical Artificial Intelligence or the “Computational Theory of Mind”: This perspective emphasizes the computational and algorithmic side of artificial intelligence and advocates for the mechanization and computerization of the mind.Connectionist Artificial Intelligence: This viewpoint focuses on recreating the “neural networks” of the brain. Additionally, the human soul, as the source of human intelligence, possesses cognitive and motivational powers that act as the soldiers of the soul, generating a variety of actions and effects. This research attempts to re-evaluate the fundamental differences between natural intelligence and artificial intelligence from Ibn Sina's perspective using a rational-analytical approach. According to Ibn Sina, natural intelligence and artificial intelligence differ in eight key areas: composite synthesis, intentionality, creativity and inventiveness, specialization focus, self-awareness and self-discovery, the internal evolution of natural intelligence, the impulsive power of desire, ethical conduct, and the ability to recall.

TOMA: Computational Theory of Mind with Abstractions for Hybrid Intelligence

Theory of mind refers to the human ability to reason about the mental content of other people, such as their beliefs, desires, and goals. People use their theory of mind to understand, reason about, and explain the behaviour of others. Having a theory of mind is especially useful when people collaborate, since individuals can then reason on what the other individual knows as well as what reasoning they might do. Similarly, hybrid intelligence systems, where AI agents collaborate with humans, necessitate that the agents reason about the humans using computational theory of mind. However, to try to keep track of all individual mental attitudes of all other individuals becomes (computationally) very difficult. Accordingly, this paper provides a mechanism for computational theory of mind based on abstractions of single beliefs into higher-level concepts. These abstractions can be triggered by social norms and roles. Their use in decision making serves as a heuristic to choose among interactions, thus facilitating collaboration. We provide a formalization based on epistemic logic to explain how various inferences enable such a computational theory of mind. Using examples from the medical domain, we demonstrate how having such a theory of mind enables an agent to interact with humans effectively and can increase the quality of the decisions humans make.

Philosophical Underpinnings of Artificial Intelligence and the Concept of Human Soul in Islam: Some Issues at the Interface

Can machines think? Are humans and their minds essentially physical entities? Particular views about the nature of intelligence and the human mind, i.e. the computational theory of mind, often appear either as the basis of or having taken inspiration from the field of Artificial Intelligence. It aims to explain intelligence by only resorting to physical explanation. From the Islamic standpoint, this might induce philosophical issues since Islam acknowledges in man the existence of spiritual substance, i.e. nafs (soul), in addition to the existence of the body and affirms its role in explaining human intelligence. Therefore, this article discusses some issues stemming from the interface between the philosophical underpinnings of Artificial Intelligence and Islam. Three issues are discussed: visual perception and abstraction, language and understanding, and knowledge and inference. The Islamic conception of human soul is taken from the works of Syed Muhammad Naquib Al-Attas. It is shown that intelligence is an aspect of the soul and its nature is spiritual and non-physical. Furthermore, intelligible forms imprinted upon the soul are also non-physical. This can also explain why the current AI lacks understanding, as shown in the ‘adversarial examples’ in visual abstraction, syntax/semantics distinction, and abduction as a ‘leap’ in reasoning.

Philosophical Underpinnings of Artificial Intelligence and the Concept of Human Soul in Islam: Some Issues at the Interface

Can machines think? Are humans and their minds essentially physical entities? Particular views about the nature of intelligence and the human mind, i.e. the computational theory of mind, often appear either as the basis of or having taken inspiration from the field of Artificial Intelligence. It aims to explain intelligence by only resorting to physical explanation. From the Islamic standpoint, this might induce philosophical issues since Islam acknowledges in man the existence of spiritual substance, i.e. nafs (soul), in addition to the existence of the body and affirms its role in explaining human intelligence. Therefore, this article discusses some issues stemming from the interface between the philosophical underpinnings of Artificial Intelligence and Islam. Three issues are discussed: visual perception and abstraction, language and understanding, and knowledge and inference. The Islamic conception of human soul is taken from the works of Syed Muhammad Naquib Al-Attas. It is shown that intelligence is an aspect of the soul and its nature is spiritual and non-physical. Furthermore, intelligible forms imprinted upon the soul are also non-physical. This can also explain why the current AI lacks understanding, as shown in the ‘adversarial examples’ in visual abstraction, syntax/semantics distinction, and abduction as a ‘leap’ in reasoning.

What is Mind in Philosophy: An Introduction

The exploration of the mind is a fundamental pursuit spanning philosophy and psychology, with implications reaching into diverse practical realms. This paper delves into the intricacies of mental states, examining historical perspectives from ancient philosophers to modern theorists. Philosophical inquiries into intentionality, consciousness, and the nature of mental phenomena are scrutinized, alongside empirical investigations by psychologists. The discourse navigates through contrasting theories such as dualism, materialism, and functionalism, shedding light on the challenges of reconciling subjective experiences with objective obsNervations. The problem of other minds and the tension between internalism and externalism are dissected, revealing the complex interplay between individual cognition and external influences. Ultimately, this analysis underscores the intricate nature of philosophical inquiries into consciousness and the mind. KEY WORDS: Mind, consciousness, philosophy of mind, mental states, dualism, materialism, functionalism, internalism, externalism, intentionality, subjective experience, objectivity, psychology, other minds, Descartes, Hume, Kant, William James, behaviourism, computational theory of mind.

Scalable Distributed Inverted List Indexes in Disaggregated Memory

Memory disaggregation separates compute (CPU) and main memory resources into disjoint physical units to enable elastic and independent scaling. Connected via high-speed RDMA-enabled networks, compute nodes can directly access remote memory. This setting often requires complex protocols with many network roundtrips as memory nodes have near-zero compute power. In this paper, we design a scalable distributed inverted list index for disaggregated memory architectures. An inverted list index maps a set of terms to lists of documents that contain this term. Current solutions either partition the index horizontally or vertically with severe limitations in the disaggregated memory setting due to data and access skew, high network latency, or out-of-memory errors. Our method partitions lists into fixed-size blocks and spreads them across the memory nodes to balance skewed accesses. Block-based list processing keeps the memory footprint of compute nodes low and masks latency by interleaving remote accesses with expensive list operations. In addition, we propose efficient updates with optimistic concurrency control and read-write conflict detection. Our experiments confirm the efficiency and scalability of our method.

Smart Offloading Strategies by Optimizing Fog Computing Through Reinforcement Learning Strategies

With the proliferation of Internet of Things(IoT) devices and the exponential growth in data volume, fog computing has emerged as a promising paradigm to address the limitations of cloud-centric architectures by bringing computation and storage closer to the data source. In fog comp uting environments, efficient task offloading plays a crucial role in optimizing resource utilization and minimizing latency. This paper proposes a novel approach to smart offloading strategies utilizing reinforcement learning (RL) techniques. To validate the effectiveness of our approach, we conduct extensive simulations and experiments using representative fog computing scenarios. Results demonstrate significant improvements in offloading efficiency, latency reduction, and resource utilization compared to traditional static offloading methods. Keywords: Fog Computing, Internet of Things (IoT), offloading, reinforcement learning.

‘Is it perfume from a dress / that makes me so digress?’ – the categorical differences between human experience and AI highlighted by literature

Since the term ‘Artificial Intelligence’ was coined, the respective research field has frequently emulated human mental faculties. Despite diverging viewpoints regarding the feasibility of achieving human-like cognition in machines, the very use of the word intelligence for complex computer systems evokes human consciousness. Likewise, there have been attempts to understand the human mind in terms of computers, exemplified by the computational theory of mind. By contrast, my article underscores the categorical difference between the mind and machines. Partly building upon arguments by David Gelernter and Bert Olivier, I focus on literary examples spanning from Shakespeare to T.S. Eliot that accentuate subjective experience, the intricate relationship between body and mind, and the anticipation of death as human characteristics beyond the reach of computational systems.

Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis.

The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set. For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57-0.57) for CRS, 0.61 (0.60-0.61) for RSF in combination with CRS, and 0.70 (0.68-0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59-0.59) for CRS, 0.57 (0.56-0.57) for RSF in combination with CRS, and 0.60 (0.58-0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33-0.33) for CRS, 0.77 (0.75-0.78) for radiomics signature alone, and 0.58 (0.57-0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61-0.61) for CRS, 0.57 (0.56-0.57) for radiomics signature, and 0.75 (0.74-0.76) for DeepSurv score alone. Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ineffective yet toxic chemotherapy or high-risk major hepatectomies.

Renal vascular disparities among kidney donors presented at renal transplant unit- A single centre study

Renal vasculature has been reported to show anatomical variations, which may lead to surgical complications. The pattern of renal vascular variations in Pakistani population is not well reported. Thus, this study was designed to assess the renal vascularity among kidney donors and correlate with age, gender and site of vessels. A total of 223 healthy volunteers underwent evaluation for kidney donation at the Radiology Department, DOW University Hospital, Karachi, Pakistan from January 2020 to July 2022 were included. The assessment of renal vasculature was done on computed (CT) angiogram. Renal vascular variations were found in 112 (50.2%), 59.8% were seen in males and 40.2% in females. On right side 50% vascular anomalies were found followed by left side with 27% and 22% on both sides. The arterial variants included accessory renal artery seen in 76 (67.8%) individuals, out of which 13.2% had in upper polar, 26.3% in hilum, 36.8% in lower polar, while 23.7% of early branching was observed. Ten retro-aortic, fourteen accessory renal veins and six bifurcation renal veins, along with six early branching renal veins, were present. Patient who received donor nephrectomy, the outcomes of the operation were completely consistent with the results of the CTA. According to this study, Pakistani population have differences in their renal venous and arterial anatomy, which can significantly influence surgical procedures.

A three-dimensional image reflecting the dispersion state of ceramic platelets in solvent: Observation of montmorillonite dispersed in silica gel by synchrotron X-ray multiscale tomography

Visualizing montmorillonite (MMT) platy particles dispersed in silica gel was firstly demonstrated by synchrotron X-ray multiscale tomography. The silica sol-gel reaction proceeded with the preservation of MMT dispersibility in water, MeOH, and DMF mixture. The 3D image reconstructed using high-resolution X-ray computed (CT) images (i.e., nano-CT) displayed platy particles in the range of 0.5—2.5 μm, which matched that observed in the field-emission microscope image of powder MMT. In addition, some platy particles are assembled by contacting their edge and layered surfaces, suggesting the presence of a house-of-cards structure. Given a decrease in the distance by 30% between platy particles roughly estimated using a decrease in volume by 65% before and after silica gelation, a dispersion state formed by micrometer-sized platy particles did not essentially differ after silica gelation. Therefore, these results indicated that the 3D image of MMT platy particles in silica gel reflected the dispersion state of MMT particles in the solvent.

Natralization without associationist reduction: a brief rebuttal to Yoshimi

AbstractYoshimi has attempted to defuse my argument concerning the identification of network abstraction with empiricist abstraction - thus entailing psychologism - by claiming that the argument does not generalize from the example of simple feed-forward networks. I show that such details of networks are logically irrelevant to the nature of the abstractive process they employ. This is ultimately because deep artificial neural networks (ANNs) and dynamical systems theory applied to the mind (DST) are both associationisms - that is, empiricist theories that derive the principles of thought from the causal history of the organism/system. On this basis, I put forward a new aspect of the old argument by noting that ANNs & DST are the causal bases of the phenomena of passive synthesis, whereas the language of thought hypothesis (LOT) and the symbolic computational theory of mind (CTM) are the causal bases of the phenomena of active synthesis. If the phenomena of active synthesis are not distinct in kind from and are thus reducible to those of passive synthesis, psychologism results. Yoshimi’s program, insofar as it denies this fundamental phenomenological distinction, is revealed to be the true anti-pluralist program, by essentially denying the causal efficacy of the mechanistic foundations of active synthesis by referring phenomenology exclusively to associationism for its causal foundation.

Between Michigan and Rochester: Identity-based thinking is cognitively primary

This article synthesizes a large body of research in the social and cognitive sciences to develop a distinctly cognitive understanding of political identity. Building on dual-process and computational theories of mind, the article defends three claims about the mental and behavioral implications of identity in political domains: (1) identity-based thinking is people’s default (often fast, automatic, and cognitively inaccessible) way of mentally representing politics and of drawing inferences based on those representations; (2) people are not limited to identity-based thinking and can sometimes learn to override it via slow, volitional, and conscious reasoning; and (3) the cognitive complexity of identity-based thinking is in-between the levels of mental sophistication that the Michigan and Rochester Schools in political science posit. This account of political identity illuminates, inter alia, why even low-information voters can quickly identify their political allies and opponents, why even high-information politicians can misperceive their constituents, why affective polarization has been increasing in the United States in recent decades, and why many normative theories of justice advise people to override identity-based thinking.

Рецензия на книгу П.Н. Барышникова «Вычислительные модели разума: От кода к смыслу» (М.: ЛЕНАНД, 2022. 320 с.)

The cognitive revolution of the mid-20th century allowed scientists to return consciousness into respectable scientific research, thereby bypassing the eliminativist approaches of behav­iorism and physicalism. Machine functionalism adopted the computer metaphor and became the philosophical foundation for this new scientific trend. In the second half of the 20th cen­tury, many significant data were obtained by the efforts of cognitive scientists on how men­tal processes are arranged, but the methodological boundaries of computational theories and the ontological status of mental states remained a serious problem. This complex issue is closely analyzed by P.N. Baryshnikov in his book “Computational models of the mind: From code to meaning”. Moving along the historical line of development of cognitive sci­ence and related philosophical conceptions, the author demonstrates that computational the­ories are not able to explain the semantic aspect of mental states, although they have heuris­tic potential for creating artificial intelligent systems that implement some of the cognitive functions of the human mind.

A review on the criteria for adoption of cloud computing in South African Institutions and Business enterprises

Cloud computing has taken over the IT industry. Big enterprises such as Oracle, Microsoft and Amazon have developed world class state of the art products and services for Cloud Computing. These products serve and the needs and power many corporations, business institutions and enterprises across the world. Cloud computing is continuing to growing and attracting more businesses and birthing and transforming industries such as music streaming and online gaming. As we progress and move towards a 5G and become a 4th industrial society more cloud computing products and services will be developed and used. However, there are many issues that arise such as stability, reliability, security and fraud. Addressing these problems introduces complexity towards adopting cloud comput ing for small business enterprises leaving only big enterprises to be able to afford reliable cloud computing products. There are many outstanding works that have been previously published by scholars on cloud computing. This research paper provides a systematic review and a summary of some the outstanding works of scholars recently published and the different criterias used for clouding computing adoption by South African Institutions and Business enterprises.

Cryptogenic Organizing Pneumonia Mimicking a Mass Lesion: An Unusual Case

Cryptogenic organizing pneumonia (COP) is less common interstitial lung disease with varying clinical picture. It can be misdiagnosed as lung cancer as COP usually presents with malaise, fever, weight loss, and myalgia, which overlap with clinical features of lung cancer. Here, we present a rare case of a 68-year-old male smoker admitted with cough, hemoptysis, arthralgia, fever, and chest pain. On chest X-ray, unilateral homogenous opacity was seen. Blood culture and sputum for Ziehl–Neelsen stain were negative. High-resolution computed tomography chest revealed consolidation in middle and lower zone of the left lung with ground-glass opacity with centrilobular nodules. Bronchoalveolar lavage specimen was negative for bacteria, Mycobacterium tuberculosis, and atypical cells. Transbronchial lung biopsy showed evidence of OP. The patient responded well to steroids.

Metaphor and the scientific method: Why Lacan’s perspective isn’t helpful yet

Keefer (2022) argues that theoretical commitments to conceptual metaphor theory (CMT) impede research on metaphor in psychology. To jumpstart the field, he suggests adopting Lacan’s perspective. I disagree with this argument for a few reasons. First, CMT has a much more nuanced place in current research on metaphor than it would seem from the target article. Second, the field does engage with the hypotheses enumerated by Keefer: that metaphors emerge from a complex web of associations, have unconscious influence, and reflect deep-seated motivations. I review how contemporary research has approached these questions and show how the constraints cited in Keefer’s article stem from the field’s commitments to the scientific method and the computational theory of mind, rather than CMT. Finally, contrary to how they are framed in Keefer’s article, I argue that these constraints have enabled scientific progress to date and limit the impact of Lacan’s perspective moving forward.

The case for distributed shared-memory databases with RDMA-enabled memory disaggregation

Memory disaggregation (MD) allows for scalable and elastic data center design by separating compute (CPU) from memory. With MD, compute and memory are no longer coupled into the same server box. Instead, they are connected to each other via ultra-fast networking such as RDMA. MD can bring many advantages, e.g., higher memory utilization, better independent scaling (of compute and memory), and lower cost of ownership. This paper makes the case that MD can fuel the next wave of innovation on database systems. We observe that MD revives the great debate of "shared what" in the database community. We envision that distributed shared-memory databases (DSM-DB, for short) - that have not received much attention before - can be promising in the future with MD. We present a list of challenges and opportunities that can inspire next steps in system design making the case for DSM-DB.

Parameter-free differential evolution algorithm for the analytic continuation of imaginary time correlation functions.

We report on differential evolution for analytic continuation: a parameter-free evolutionary algorithm to generate the dynamic structure factor from imaginary time correlation functions. Our approach to this long-standing problem in quantum many-body physics achieves enhanced spectral fidelity while using fewer compute (CPU) hours. The need for fine-tuning of algorithmic control parameters is eliminated by embedding them within the genome to be optimized for this evolutionary computation-based algorithm. Benchmarks are presented for models where the dynamic structure factor is known exactly and experimentally relevant results are included for quantum Monte Carlo simulations of bulk ^{4}He below the superfluid transition temperature.

Dynamical Systems Implementation of Intrinsic Sentence Meaning

This paper proposes a model for implementation of intrinsic natural language sentence meaning in a physical language understanding system, where 'intrinsic' is understood as 'independent of meaning ascription by system-external observers'. The proposal is that intrinsic meaning can be implemented as a point attractor in the state space of a nonlinear dynamical system with feedback which is generated by temporally sequenced inputs. It is motivated by John Searle's well known (Behavioral and Brain Sciences, 3: 417–57, 1980) critique of the then-standard and currently still influential computational theory of mind (CTM), the essence of which was that CTM representations lack intrinsic meaning because that meaning is dependent on ascription by an observer. The proposed dynamical model comprises a collection of interacting artificial neural networks, and constitutes a radical simplification of the principle of compositional phrase structure which is at the heart of the current standard view of sentence semantics because it is computationally interpretable as a finite state machine.