Science seeks to discover the order present in nature and describes this order in the form of laws. Religion is a response to a Transcendent Being Who transforms our lives and gives it a meaning. Traditionally, spirituality was regarded as an integral aspect of religious experience, and spirituality and religion were inseparable. But, the decline of organized religions and the growth of secularism in the western world have given rise to a broader view of spirituality which includes reference to those aspects of human experience which go beyond a purely materialist view of the world, without necessarily bringing in a supernatural reality. Scientific revolution started in the seventeenth century with the works of F. Bacon, Descartes, Galileo, Newton, Boyle, etc. and gave rise to the promotion of empiricism by Bacon and Galileo and the propagation of mechanical worldview by Descartes, Galileo and Newton. Gradually the power of Newtonian system impressed the scientists, and the role of God was first reduced to the initiator of the universe and with the French enlightenment it was eliminated. With the appearance of Philosophers and scientists like Hume ,Kant ,August Comte, Marx, Darwin , Durkhim , Freud and logical positivists, empiricism became the dominant philosophy and with that metaphysics ,religion and spirituality went into the sideline.Several important currents started during the second half of the twentieth century which had a revival effect on religion and spirituality and some eminent scientists of our era –including some non-theists – have emphasized the necessity of going beyond the material features of life and paying attention to its spiritual aspects (values, meanings, etc.).
Open-world object manipulation remains a fundamental challenge in robotics. While Vision-Language-Action (VLA) models have demonstrated promising results, they rely heavily on large-scale robot action demonstrations, which are costly to collect and can hinder out-of-distribution generalization. In this paper, we propose an explicit-world-model-based framework for open-world manipulation that achieves zero-shot generalization by constructing a physically grounded digital twin of the environment. The framework integrates open-set perception, digital-twin reconstruction, sampling and evaluation of interaction strategies. By constructing a digital twin of the environment, our approach efficiently explores and evaluates manipulation strategies in physic-enabled simulator and reliably deploys the chosen strategy to the real world. Experimentally, the proposed framework is able to perform multiple open-set manipulation tasks without any task-specific action demonstrations, proving strong zero-shot generalization on both the task and object levels. Project Page: https://bojack-bj.github.io/projects/thesis/
This paper presents the World-Action Model (WAM), an action-regularized world model that jointly reasons over future visual observations and the actions that drive state transitions. Unlike conventional world models trained solely via image prediction, WAM incorporates an inverse dynamics objective into DreamerV2 that predicts actions from latent state transitions, encouraging the learned representations to capture action-relevant structure critical for downstream control. We evaluate WAM on enhancing policy learning across eight manipulation tasks from the CALVIN benchmark. We first pretrain a diffusion policy via behavioral cloning on world model latents, then refine it with model-based PPO inside the frozen world model. Without modifying the policy architecture or training procedure, WAM improves average behavioral cloning success from 59.4% to 71.2% over DreamerV2 and DiWA baselines. After PPO fine-tuning, WAM achieves 92.8% average success versus 79.8% for the baseline, with two tasks reaching 100%, using 8.7x fewer training steps.
Pascale Fung, Yoram Bachrach, Asli Celikyilmaz
et al.
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which include virtual avatars, wearable devices, and robots, are designed to perceive, learn and act within their surroundings, which makes them more similar to how humans learn and interact with the environments as compared to disembodied agents. We propose that the development of world models is central to reasoning and planning of embodied AI agents, allowing these agents to understand and predict their environment, to understand user intentions and social contexts, thereby enhancing their ability to perform complex tasks autonomously. World modeling encompasses the integration of multimodal perception, planning through reasoning for action and control, and memory to create a comprehensive understanding of the physical world. Beyond the physical world, we also propose to learn the mental world model of users to enable better human-agent collaboration.
We introduce PlayerOne, the first egocentric realistic world simulator, facilitating immersive and unrestricted exploration within vividly dynamic environments. Given an egocentric scene image from the user, PlayerOne can accurately construct the corresponding world and generate egocentric videos that are strictly aligned with the real scene human motion of the user captured by an exocentric camera. PlayerOne is trained in a coarse-to-fine pipeline that first performs pretraining on large-scale egocentric text-video pairs for coarse-level egocentric understanding, followed by finetuning on synchronous motion-video data extracted from egocentric-exocentric video datasets with our automatic construction pipeline. Besides, considering the varying importance of different components, we design a part-disentangled motion injection scheme, enabling precise control of part-level movements. In addition, we devise a joint reconstruction framework that progressively models both the 4D scene and video frames, ensuring scene consistency in the long-form video generation. Experimental results demonstrate its great generalization ability in precise control of varying human movements and worldconsistent modeling of diverse scenarios. It marks the first endeavor into egocentric real-world simulation and can pave the way for the community to delve into fresh frontiers of world modeling and its diverse applications.
Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World models offer a potential way to improve learning efficiency through simulated experience, but it remains unclear whether large language models can reliably serve this role and under what conditions they meaningfully benefit agents. We study these questions in text-based environments, which provide a controlled setting to reinterpret language modeling as next-state prediction under interaction. We introduce a three-level framework for evaluating LLM-based world models: (i) fidelity and consistency, (ii) scalability and robustness, and (iii) agent utility. Across five representative environments, we find that sufficiently trained world models maintain coherent latent state, scale predictably with data and model size, and improve agent performance via action verification, synthetic trajectory generation, and warm-starting reinforcement learning. Meanwhile, these gains depend critically on behavioral coverage and environment complexity, delineating clear boundry on when world modeling effectively supports agent learning.
Building an efficient and physically consistent world model from limited observations is a long standing challenge in vision and robotics. Many existing world modeling pipelines are based on implicit generative models, which are hard to train and often lack 3D or physical consistency. On the other hand, explicit 3D methods built from a single state often require multi-stage processing-such as segmentation, background completion, and inpainting-due to occlusions. To address this, we leverage two perturbed observations of the same scene under different object configurations. These dual states offer complementary visibility, alleviating occlusion issues during state transitions and enabling more stable and complete reconstruction. In this paper, we present DSG-World, a novel end-to-end framework that explicitly constructs a 3D Gaussian World model from Dual State observations. Our approach builds dual segmentation-aware Gaussian fields and enforces bidirectional photometric and semantic consistency. We further introduce a pseudo intermediate state for symmetric alignment and design collaborative co-pruning trategies to refine geometric completeness. DSG-World enables efficient real-to-simulation transfer purely in the explicit Gaussian representation space, supporting high-fidelity rendering and object-level scene manipulation without relying on dense observations or multi-stage pipelines. Extensive experiments demonstrate strong generalization to novel views and scene states, highlighting the effectiveness of our approach for real-world 3D reconstruction and simulation.
Towards the grand challenge of achieving human-level manipulation in robots, playing piano is a compelling testbed that requires strategic, precise, and flowing movements. Over the years, several works demonstrated hand-designed controllers on real world piano playing, while other works evaluated robot learning approaches on simulated piano scenarios. In this paper, we develop the first piano playing robotic system that makes use of learning approaches while also being deployed on a real world dexterous robot. Specifically, we make use of Sim2Real to train a policy in simulation using reinforcement learning before deploying the learned policy on a real world dexterous robot. In our experiments, we thoroughly evaluate the interplay between domain randomization and the accuracy of the dynamics model used in simulation. Moreover, we evaluate the robot's performance across multiple songs with varying complexity to study the generalization of our learned policy. By providing a proof-of-concept of learning to play piano in the real world, we want to encourage the community to adopt piano playing as a compelling benchmark towards human-level manipulation. We open-source our code and show additional videos at https://lasr.org/research/learning-to-play-piano .
Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review existing definitions of hallucination and fold them into a single, unified definition wherein prior definitions are subsumed. We argue that hallucination can be unified by defining it as simply inaccurate (internal) world modeling, in a form where it is observable to the user. For example, stating a fact which contradicts a knowledge base OR producing a summary which contradicts the source. By varying the reference world model and conflict policy, our framework unifies prior definitions. We argue that this unified view is useful because it forces evaluations to clarify their assumed reference "world", distinguishes true hallucinations from planning or reward errors, and provides a common language for comparison across benchmarks and discussion of mitigation strategies. Building on this definition, we outline plans for a family of benchmarks using synthetic, fully specified reference world models to stress-test and improve world modeling components.
We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model and world model in one single framework. The world model predicts future images by leveraging both action and image understanding, with the purpose of learning the underlying physics of the environment to improve action generation. Meanwhile, the action model generates the subsequent actions based on image observations, aiding in visual understanding and in turn helps visual generation of the world model. We demonstrate that WorldVLA outperforms standalone action and world models, highlighting the mutual enhancement between the world model and the action model. In addition, we find that the performance of the action model deteriorates when generating sequences of actions in an autoregressive manner. This phenomenon can be attributed to the model's limited generalization capability for action prediction, leading to the propagation of errors from earlier actions to subsequent ones. To address this issue, we propose an attention mask strategy that selectively masks prior actions during the generation of the current action, which shows significant performance improvement in the action chunk generation task.
As South Africa faced the new dawn of COVID-19 pandemic, questions were raised regarding the utilization of African traditional medicine as a treatment of the illness. Madagascar was viewed by African people as a pioneer to regard and declare Artemisia as treatment for COVID-19 and developed products known as Africa following its breakthrough in traditional herbal cure for COVID-19 known as COVID Organics or CVO to be used by their citizens and the rest of African states. In July 2020, the World Health Organization (WHO) and the Africa Centres for Disease Control and Prevention formed a Regional Expert Committee on
Conventional Medicine for COVID-19 in an attempt to enhance research and development of traditional medicines such as CVO, aiming to test traditional therapies and provide independent advice on their safety and efficacy. However, six months after its formation, it appeared that this attempt to regulate has failed to gain traction: None of the proposed remedies progressed beyond phase II clinical trials. Using existing secondary sources such as articles, government documents and media releases, this paper aims to look at the response from South African government on utilization of African Traditional Medicine as
COVID-19 treatment. A special attention is given to South African government’s response to the treatment proposed by the state of Madagascar and its way forward towards using traditional medicine as treatment to COVID-19.
Bioethics provides a new perspective for the comparative study of Christianity and Chinese Buddhism. This paper provides a comprehensive comparison of the sources, states of existence, and fundamental principles and purposes of the Christian and Chinese Buddhist perspectives on human life, focusing specifically on the realm of bioethics. It places special emphasis on teachings about God’s creation and dependent origination, original sin and Buddhist causality, as well as love and compassion. Despite the significant geographic distance between Christianity and Chinese Buddhism, the dialogue highlights potential cultural differences and interpretations. It also demonstrates mutual acceptance and the process of redefining one’s own identity. Religious bioethics greatly benefits from a comprehensive study of various religions from around the world. It aims to encourage cross-cultural and interdisciplinary research on different religions globally. It promotes religious bioethics as a relevant field of study.
Zero-shot generalization (ZSG) to unseen dynamics is a major challenge for creating generally capable embodied agents. To address the broader challenge, we start with the simpler setting of contextual reinforcement learning (cRL), assuming observability of the context values that parameterize the variation in the system's dynamics, such as the mass or dimensions of a robot, without making further simplifying assumptions about the observability of the Markovian state. Toward the goal of ZSG to unseen variation in context, we propose the contextual recurrent state-space model (cRSSM), which introduces changes to the world model of Dreamer (v3) (Hafner et al., 2023). This allows the world model to incorporate context for inferring latent Markovian states from the observations and modeling the latent dynamics. Our approach is evaluated on two tasks from the CARL benchmark suite, which is tailored to study contextual RL. Our experiments show that such systematic incorporation of the context improves the ZSG of the policies trained on the "dreams" of the world model. We further find qualitatively that our approach allows Dreamer to disentangle the latent state from context, allowing it to extrapolate its dreams to the many worlds of unseen contexts. The code for all our experiments is available at https://github.com/sai-prasanna/dreaming_of_many_worlds.
James P. Sterba has provided a compelling argument to the effect that given the extent of significant, and indeed even horrendous, evil that an all-good and all-powerful being could have prevented, there is no God. There is a hidden assumption in Sterba’s reasoning, involving an inference from God being able to do anything metaphysically possible (omnipotence) to his being, after creation, able to prevent evil. As what follows shows, that isn’t a purely logical matter. It depends on ruling out a determinate theological account of how creation limits what is then metaphysically possible for God, an account set out in detail below. So Sterba’s argument is not deductively valid, unless that account is incoherent. Accordingly, we are back in the realm of total judgments of theoretical plausibility, and the effects of God-given grace on what then will strike one as the right view to live by.
Makalede Yahudi temel kaynaklarına göre kadınların yaratılışta ve dini hayattaki rolü ve Yahudi kutsal kitaplarına göre kadınların örtünme geleneği araştırılmıştır. Ülkemizde bu konuda Dinler tarihine katkı sağlanması amacıyla böyle bir konu ele alınmıştır. Aynı zamanda örtünmenin sadece İslam’la sınırlandıranlara Yahudilik üzerinden diğer dinlerde de kadınların örtünmesi gerektiği bilgisini aktarmaktır.
Bu makale literatür taramasına dayalı bir araştırmadır.
Çalışma özet, giriş, 3 alt başlık, sonuç ve kaynakça kısımlarından ibarettir.
• Eski Ahit’te kadınların yaratılış statüsünde Yahvist ve Elohist metinlere göre kadınların yaratılışı incelenmiştir. Her iki metinde kadınların yaratılış hikâyesi birbirinden farklı şekilde anlatılmaktadır. Nitekim Elohist metinde kadın yaratılışta erkeğe eşit şekilde, Yahvist metinde ise erkeğe hizmetkâr olarak anlatılmıştır.
• Eski Ahit’te kadınların dini statüsünde Yahudi kadınların ferdi ve cemaatle ibadetleri, Tevrat okunması ve Tevrat’ın öğretilmesi ve öğrenilmesi, kadın dua grupları gibi konular işlenmiştir. Kadınlar ferdi ibadetlerde erkeklerden farklı olarak ibadet sırasında okunacak bütün duaları söyleyemez, tefilin ve tzitzit giyemez. Kadınlar cemaatten sayılmaz, Tevrat okuyamaz ve öğretemez. Kadınlar sadece Hanuka mumunu yakmalı, Şabat akşamları Kiduş’u söylemeli, Yom Kipur’da oruç tutmalı ve bu gibi birkaç emri yerine getirmelidir.
• Eski Ahit, Mişna ve Talmud’da kadınların örtünmesi ile ilgili bilgiler aktarılmıştır. Hür ve iffetli kadınların örtünme şekli hayat kadınları, köleler ve cariyelerden seçilmiştir. Nitekim hür kadınlar peçe takarken, hayat kadınları renkli ve farklı şekillerde örtünme biçimi kullanmıştır. Evlenen köle kadınların seçilmesi için saçları kesilmiştir.
Zamanla örtünmenin yerini peruk tutsa da günümüzde sadece Ortodoks kadınlar dua esnasında peruk kullanmaktadırlar.
Religion (General), Philosophy of religion. Psychology of religion. Religion in relation to other subjects
Nurlan Orynbekov, Kakimzhan Bishmanov, Kantarbayeva Zhanna
et al.
This article delves into the intricate interplay between terrorism, its ideological
underpinnings, and their intersection with theology within a national context. Employing the principles of activity, historicism, system-structural, and comparative methods, this research scrutinizes the essence of terrorism as a unified entity shaped by its ideological roots and theological influences. The primary findings highlight terrorism as a complex, systematically organized phenomenon, shedding light on its evolution from antecedent forms of destructive activities. Moreover, the study elucidates the constitution of terrorism, emphasizing its distinct ideological and theological dimensions, unravelling their profound impact on the national landscape. The implications of this study are far-reaching. Firstly, it contributes significantly to the theoretical advancements in understanding terrorism, extremism, and radicalism. Secondly, it offers actionable insights for practical interventions aimed at preempting and dismantling terrorist groups and cells. Lastly, it enriches educational curricula across disciplines like social philosophy, sociology, political science, and psychology. The novelty of this study lies in its holistic approach, viewing
terrorism as an integrated system, and uncovering the evolutionary trajectory from earlier forms of destructive behavior. By exploring the amalgamation of ideology and theology within terrorism, this research breaks new ground, offering a comprehensive framework for understanding and addressing this complex socio-political phenomenon.
It is undeniable that civilization during the Abbasid Dynasty was the heyday of Islam with the development of education and science. The rapid development of education during the Abbasid Dynasty was due to the large number of Islamic educational institutions at that time. The characteristics of Islamic education management at that time were a benchmark of educational progress, so in this case a library research study was needed in describing the form of Islamic education management, this research is a type of library research research with data sources coming from library data from various books and articles published relevant to the object of research and data analysis is carried out by collecting raw data, data reduction, data display, and verification/conclusion. The characteristics of Islamic education management in the Abbasid dynasty have an inclusive character towards students from various ethnic backgrounds, races, religions and between groups, the existence of self-supporting education financing through waqf, participation and support of the caliphs to the world of education, high welfare of educators, facilities and adequate educational infrastructure. Based on the results of the study in the form of a description of the characteristics of Islamic education management during the Abbasid Dynasty, it can add to the repertoire of the realm of education management to improve the quality of education, especially in Indonesia.
The enduring challenge in the field of artificial intelligence has been the control of systems to achieve desired behaviours. While for systems governed by straightforward dynamics equations, methods like Linear Quadratic Regulation (LQR) have historically proven highly effective, most real-world tasks, which require a general problem-solver, demand world models with dynamics that cannot be easily described by simple equations. Consequently, these models must be learned from data using neural networks. Most model predictive control (MPC) algorithms designed for visual world models have traditionally explored gradient-free population-based optimisation methods, such as Cross Entropy and Model Predictive Path Integral (MPPI) for planning. However, we present an exploration of a gradient-based alternative that fully leverages the differentiability of the world model. In our study, we conduct a comparative analysis between our method and other MPC-based alternatives, as well as policy-based algorithms. In a sample-efficient setting, our method achieves on par or superior performance compared to the alternative approaches in most tasks. Additionally, we introduce a hybrid model that combines policy networks and gradient-based MPC, which outperforms pure policy based methods thereby holding promise for Gradient-based planning with world models in complex real-world tasks.
World models are a fundamental component in model-based reinforcement learning (MBRL). To perform temporally extended and consistent simulations of the future in partially observable environments, world models need to possess long-term memory. However, state-of-the-art MBRL agents, such as Dreamer, predominantly employ recurrent neural networks (RNNs) as their world model backbone, which have limited memory capacity. In this paper, we seek to explore alternative world model backbones for improving long-term memory. In particular, we investigate the effectiveness of Transformers and Structured State Space Sequence (S4) models, motivated by their remarkable ability to capture long-range dependencies in low-dimensional sequences and their complementary strengths. We propose S4WM, the first world model compatible with parallelizable SSMs including S4 and its variants. By incorporating latent variable modeling, S4WM can efficiently generate high-dimensional image sequences through latent imagination. Furthermore, we extensively compare RNN-, Transformer-, and S4-based world models across four sets of environments, which we have tailored to assess crucial memory capabilities of world models, including long-term imagination, context-dependent recall, reward prediction, and memory-based reasoning. Our findings demonstrate that S4WM outperforms Transformer-based world models in terms of long-term memory, while exhibiting greater efficiency during training and imagination. These results pave the way for the development of stronger MBRL agents.