Past Knowledge Research Articles

The Power of Memory: How Lifelong Learning AI Models Are Reshaping Human-AI Collaboration

The way intelligence (AI) models are able to keep learning and adjusting without losing past knowledge. A concept known as lifelong learning. Is changing how humans and AI work together today in a significant way. Unlike AI systems that need to be trained again with new data and risk losing important information along the way lifelong learning AI models hold onto what they've learned while integrating new knowledge. This special capability makes lifelong learning models very flexible in paced settings where making quick decisions is crucial, to success. As artificial intelligence becomes more intertwined with human job functions in different fields of work continual learning plays an important role in boosting the performance of these systems so they can collaborate better with humans. This piece examines the progress of learning models in AI and how they are changing teamwork dynamics in areas such as healthcare, self-driving vehicles, and robotics. We look into the advancements in AIs ability to retain information over time the obstacles faced when constructing these adaptable frameworks and the significant impact they have on reshaping interactions, between humans and AI systems. Keywords: Lifelong Learning, AI Memory, Continuous Learning, Human-AI Collaboration, Autonomous Systems, Neural Networks, Catastrophic Forgetting, Persistent Memory, AI Adaptation, Robotics

Towards zero-shot cross-agent transfer learning via latent-space universal notice network

Despite numerous improvements regarding the sample-efficiency of Reinforcement Learning (RL) methods, learning from scratch still requires millions (even dozens of millions) of interactions with the environment to converge to a high-reward policy. This is usually because the agent has no prior information about the task and its own physical embodiment. One way to address and mitigate this data-hungriness is to use Transfer Learning (TL). In this paper, we explore TL in the context of RL with the specific purpose of transferring policies from one agent to another, even in the presence of morphology discrepancies or different state–action spaces. We propose a process to leverage past knowledge from one agent (source) to speed up or even bypass the learning phase for a different agent (target) tackling the same task. Our proposed method first leverages Variational Auto-Encoders (VAE) to learn an agent-agnostic latent space from paired, time-aligned trajectories collected on a set of agents. Then, we train a policy embedded inside the created agent-invariant latent space to solve a given task, yielding a task-module reusable by any of the agents sharing this common feature space. Through several robotic tasks and heterogeneous hardware platforms, both in simulation and on physical robots, we show the benefits of our approach in terms of improved sample-efficiency. More specifically we report zero-shot generalization in some instances, where performances after transfer are recovered instantly. In worst case scenarios, performances are retrieved after fine-tuning on the target robot for a fraction of the training cost required to train a policy with similar performances from scratch.

Democracy Ancient and Modern?

Abstract The idea that democracy is in crisis is nothing new; ancient Greek commentators like Plato and Thucydides argued that political instability was baked into democracy as a regime type. Can examples drawn from classical Athens make a useful contribution to discussions of the failings of contemporary democracy? This article considers a range of interventions from the past decade in the United States and the United Kingdom, carried out at different scales and through different methods. It considers the strengths of working with material from the distant past – its non-partisan nature offering a safe space to discuss political conflict without engaging in it – and also the weaknesses, such as the limited franchise of ancient democracies like classical Athens, and the need to consider specific historical issues in their own context. It also considers the unfamiliarity of ancient history and the way in which knowledge of the classical past can itself be perceived as class-bound and exclusionary.

Cultivating Environmental Ignorance: Non-Circulation of Ethnomedicinal Knowledge about Culén (Otholobium glandulosum) in the Atlantic World (1646–1810)

A growing body of literature recognises the importance of exploring the uses of plants in historical written sources. The Chilean native plant culén (Otholobium glandulosum) has a long history of medicinal use, with various parts of the plant, including its leaves, aerial parts, and roots, employed to treat numerous ailments. This study undertakes a Critical Discourse Analysis of historical sources, specifically books published between the 17th and early 19th centuries (1646–1810), focusing on the medicinal applications of culén. The research highlights the circulation of plant knowledge over time by adopting an interdisciplinary approach that bridges historical ethnobotany, environmental history, and the history of knowledge and ignorance studies. The study reveals how the evolution of the ethnobotanical and ethnomedicinal knowledge of culén reflects the broader environmental ignorance, wherein the scientific community excluded and oppressed the indigenous and local knowledge of the plant. This work underscores the importance of integrating historical studies into modern environmental and botanical research, emphasising the value of understanding past knowledge systems to inform contemporary conservation efforts and sustainable practices.

The study of mental disorders in visually impaired people

The purpose of this review is to highlight the history of the study of mental characteristics in persons with eye pathology and visual impairment. In the period of the Ancient World and up to the XIV century, the knowledge of the human psyche passed through the prism of mystical and religious views on the essence of the human soul. The beginning of the application of the materialistic approach to the assessment of mental characteristics in the absence of vision can be considered the work of the French writer, philosopher and educator Denis Diderot. In his "Letters" he drew attention to the need to educate the blind, which became the impetus for the activities of future specialists. In the middle of the XIX century, active work began in the field of research on the psyche of the blind. The introspective description served as the basis for the beginning of the experimental study. There is a spontaneous release of typhlopsychology, which formed the theoretical basis for the further development of science. During the 20th century, a large amount of experimental data has been accumulated in the field of studying the psyche of the blind. Modern multicenter studies of the psychological characteristics of ophthalmic patients indicate a wide prevalence of mental disorders. Scientists are currently paying close attention to specific ophthalmological nosologies and their relationship with certain mental disorders. Research is being conducted to study potential ophthalmological markers for the development of mental disorders and to find ways to diagnose them instrumentally. Despite the long and difficult path of studying the characteristics of the psyche in visually impaired people, there is still a vast field for further research. Based on past knowledge, future developments will allow us to more accurately determine the relationship between eye pathology and mental disorders.

Editorial for the special issue on "Prediction, Creativity, and Cultural Evolution in Music Cognition".

Music making across cultures arguably involves a blend of innovation and adherence to established norms. This integration allows listeners to recognise a range of innovative, surprising, and functional elements in music, while also associating them to a certain tradition or style. In this light, musical creativity may be seen to involve the novel recombination of shared elements and rules, which can in itself give rise to new cultural conventions. Put simply, future norms rely on past knowledge and present action; this holds for music as it does for other cultural domains. A key process permeating this temporal transition, with regards to both music making and music listening, is prediction. Recent findings suggest that as we listen to music, our brain is constantly generating predictions based on prior knowledge acquired in a given enculturation context. Those predictions, in turn, can shape our appraisal of the music, in a continual perception-action loop. This dynamic process of predicting and calibrating expectations may enable shared musical realities, that is, sets of norms that are transmitted, with some modification, either vertically between generations of a given musical culture, or horizontally between peers of the same or different cultures. As music transforms through cultural evolution, so do the predictive models in our minds and the expectancy they give rise to, influenced by cultural exposure and individual experience. Thus, creativity and prediction are both fundamental and complementary to the transmission of cultural systems, including music, across generations and societies. For these reasons, prediction, creativity and cultural evolution were the central themes in a symposium we organised in 2022. The symposium aimed to study their interplay from an interdisciplinary perspective, guided by contemporary theories and methodologies. This special issue compiles research discussed during or inspired by that symposium, concluding with potential directions for the field of music cognition in that spirit.

Preschooler’s Understanding of Knowledge Change Across Time

The present study explored awareness of knowledge change across time in 3-, 4-, and 5-year-olds, and the potential contribution of metacognition and theory of mind to this understanding. Participants’ appreciation of knowledge was measured through the self-knowledge task, metacognition was assessed through the partial-exposure task, and theory of mind was evaluated on Wellman and Liu’s (2004) Theory of Mind scale. I hypothesized that children’s metacognitive awareness and theory of mind would each independently contribute to children’s ability to recognize that their past knowledge is less developed than their current knowledge. Analyses revealed that theory of mind appears to be a better predictor than metacognition for children’s understanding that their knowledge changes over time. Results also showed that performance on all 3 skills improved as age increased. My findings suggest that younger children are still developing the cognitive processes required to fully appreciate that their current knowledge differs from their past knowledge.

The impact of digital learning on Saudi nursing students' engagement: A qualitative study.

This study aimed to explore the perception of nursing students regarding their engagement in digital learning environments. At one of universities in Saudi Arabia, a qualitative descriptive study design was used among nursing students from various academic levels. Data were collected using focus group discussions among a sample of 23 participants. Data were analysed using thematic analysis. The COREQ Standards for Reporting Qualitative Research were used to report this study. While sharing their perceptions the nursing students reflected on the behavioural and cognitive aspects of engagement, yet they did not include emotional aspects of engagement in their conceptualization. However, the results showed that, while they reflected on their experiences they tended to reflect on emotional aspects of engagement. According to the study's results, self-paced studying, reflective exercises, and interaction with audiovisual and dynamic content all encourage the activation of past knowledge in online learning. The learning modality also was found to be a safe and convenient approach among the participating students. Digital learning presents as a promising educational approach that extends beyond the pandemic yet, efforts should be put in place to facilitate the optimal student engagement to meet learning outcomes. Further research is needed to measure its impact on engagement and other pertinent indicators objectively.

Assisted Active Learning for Model-Based Method Development in Liquid Chromatography.

In recent decades, there has been a growing interest in fully automated methods for tackling complex optimization problems across various fields. Active learning (AL) and its variant, assisted active learning (AAL), incorporating guidance or assistance from external sources into the learning process, play key roles in this automation by enabling the autonomous selection of optimal experimental conditions to efficiently explore the problem space. These approaches are particularly valuable in situations wherein experimentation is costly or time-consuming. This study explores the application of AAL in model-based method development (MD) for liquid chromatography (LC) by using Bayesian statistics to incorporate historical data and analyte information for the generation of initial retention models. The process involves updating the model parameters based on new experiments, coupled with an active data selection method to choose the most informative experiment to run in a subsequent step. This iterative process balances model exploitation and experimental exploration until a satisfactory separation is achieved. The effectiveness of this approach is demonstrated via two practical examples, resulting in optimized separations in a limited number of experiments by optimizing the gradient slope. It is shown that the ability of AAL to leverage past knowledge and compound information to improve accuracy and reduce experimental runs offers a flexible alternative approach to fixed design methods.

Profil Habits of Mind Siswa SMA dalam Pemecahan Masalah Matematika

This study aims to describe the habits of mind possessed by high, medium, and low class XI high school students in solving mathematical problems on linear program material studied from persisting, thinking flexibly, applying past knowledge to new situations, and metacognition. Habits of mind that are the focus of this study consist of four indicators, namely persisting, thinking flexibly, applying past knowledge to new situations, and metacognition. The research subjects were students of class XI MIPA 4 SMAS Mujahidin Pontianak and the object of research was habits of mind in mathematics problem solving. Explanatory sequential mixed method design is the method of this research with its instruments, namely math problem solving test questions, habits of mind questionnaire, and interview guidelines. The results of the discussion show that the habits of mind possessed by high category students in solving mathematics are persisting, thinking flexibly, applying past knowledge to new situations, and metacognition. Habits of mind owned by medium category students are persisting, thinking flexibly, and applying past knowledge to new situations. Habits of mind possessed by low category students are persisting.

Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex.

Since the beautiful images of Santiago Ramón y Cajal provided a first glimpse into the immense diversity and complexity of cell types found in the cerebral cortex, neuroscience has been challenged and inspired to understand how these diverse cells are generated and how they interact with each other to orchestrate the development of this remarkable tissue. Some fundamental questions drive the field's quest to understand cortical development: what are the mechanistic principles that govern the emergence of neuronal diversity? How do extrinsic and intrinsic signals integrate with physical forces and activity to shape cell identity? How do the diverse populations of neurons and glia influence each other during development to guarantee proper integration and function? The advent of powerful new technologies to profile and perturb cortical development at unprecedented resolution and across a variety of modalities has offered a new opportunity to integrate past knowledge with brand new data. Here, we review some of this progress using cortical excitatory projection neurons as a system to draw out general principles of cell diversification and the role of cell-cell interactions during cortical development.

THE MAIN PECULIARITIES OF THE INTEGRATED APPROACH AND THE ROLE OF TEACHERS IN IMPLEMENTATION

ntegrative learning involves the integration of past knowledge and experiences in order to enhance the learning process. This approach allows learners to leverage their existing skills and apply them in new and more challenging situations, ultimately leading to a deeper understanding and retention of new information. Through integrative learning, individuals can connect the dots between different subjects and concepts, creating a more holistic and comprehensive learning experience. An integrated approach to teaching language skills involves incorporating the development of all language skills (reading, writing, listening, and speaking) simultaneously, rather than isolating them for separate instruction. This approach emphasizes the interconnectedness of language skills and aims to develop learners’ overall language proficiency.

Improving search and rescue planning and resource allocation through case-based and concept-based retrieval

The need for effective and efficient search and rescue operations is more important than ever as the frequency and severity of disasters increase due to the escalating effects of climate change. Recognizing the value of personal knowledge and past experiences of experts, in this paper, we present findings of an investigation of how past knowledge and experts’ experiences can be effectively integrated with current search and rescue practices to improve rescue planning and resource allocation. A special focus is on investigating and demonstrating the potential associated with integrating knowledge graphs and case-based reasoning as a viable approach for search and rescue decision support. As part of our investigation, we have implemented a demonstrator system using a Norwegian search and rescue dataset and case-based and concept-based similarity retrieval. The main contribution of the paper is insight into how case-based and concept-based retrieval services can be designed to improve the effectiveness of search and rescue planning. To evaluate the validity of ranked cases in terms of how they align with the existing knowledge and insights of search and rescue experts, we use evaluation measures such as precision and recall. In our evaluation, we observed that attributes, such as the rescue operation type, have high precision, while the precision associated with the objects involved is relatively low. Central findings from our evaluation process are that knowledge-based creation, as well as case- and concept-based similarity retrieval services, can be beneficial in optimizing search and rescue planning time and allocating appropriate resources according to search and rescue incident descriptions.

MetaV: A Pioneer in feature Augmented Meta-Learning Based Vision Transformer for Medical Image Classification.

Image classification, a fundamental task in computer vision, faces challenges concerning limited data handling, interpretability, improved feature representation, efficiency across diverse image types, and processing noisy data. Conventional architectural approaches have made insufficient progress in addressing these challenges, necessitating architectures capable of fine-grained classification, enhanced accuracy, and superior generalization. Among these, the vision transformer emerges as a noteworthy computer vision architecture. However, its reliance on substantial data for training poses a drawback due to its complexity and high data requirements. To surmount these challenges, this paper proposes an innovative approach, MetaV, integrating meta-learning into a vision transformer for medical image classification. N-way K-shot learning is employed to train the model, drawing inspiration from human learning mechanisms utilizing past knowledge. Additionally, deformational convolution and patch merging techniques are incorporated into the vision transformer model to mitigate complexity and overfitting while enhancing feature representation. Augmentation methods such as perturbation and Grid Mask are introduced to address the scarcity and noise in medical images, particularly for rare diseases. The proposed model is evaluated using diverse datasets including Break His, ISIC 2019, SIPaKMed, and STARE. The achieved performance accuracies of 89.89%, 87.33%, 94.55%, and 80.22% for Break His, ISIC 2019, SIPaKMed, and STARE, respectively, present evidence validating the superior performance of the proposed model in comparison to conventional models, setting a new benchmark for meta-vision image classification models.

Study on Motivation Factors for English Learning among Middle School Students in Northwestern China

It is only when learners have the desire and need to learn a foreign language that they have the action of learning a foreign language (Gardener, 1985). Therefore, under the guidance of a variety of foreign language learning motivation theories, this research takes 150 students from Nanan Middle School in Longxi County as the research object, and collects data through a questionnaire on junior middle school students' English learning motivation. This thesis analyzes and probes into the present situation of middle school students' English learning motivation, and the main factors influencing middle school students' English learning motivation. The final conclusion is as follows: a number of junior high school students like to learn English and are willing to spend time and energy to learn English, the intensity of learning motivation is relatively high. According to the investigation and analysis, this thesis summarizes the factors that affect the students' English learning motivation in Nanan Middle School. From the external factors, there is a social environment that stimulates students' English learning motivation, a family environment that attaches importance to students' learning and a certain school environment. From the perspective of internal factors, students' past knowledge and experience, as well as students' attitude towards learning English will affect students' English learning motivation.

Examining the Applicability of Constructivist Theory in History Teaching in Bulo Parents Secondary School, Bulo Sub County of Butambala District, Uganda

This study examined the application of construction theory in history learning at Bulo Parents Secondary School. The study found that the procedure was time-consuming and needed to be replaced with others. The study recommends that history teachers must work hard to elicit students' past knowledge because new knowledge is built on pre-existing knowledge. Teachers should use pre-tests, informal interviews, and small group warm-ups that require prior knowledge to achieve this. History professors should challenge students' thinking with difficult issues and exercises to create cognitive dissonance. As learners solve difficult tasks and change schemas, they build knowledge. Teachers should empower students to evaluate and modify knowledge. This requires presentations, small group or class discussions, and tests to compare pre-existing schema to the fresh circumstance. Teachers should provide students a chance to demonstrate their learning through presentations, reflection articles, or step-by-step tutorials. Student instructors must be introduced to constructivism and its use in the classroom. The constructivist approach must be used to create and implement educational plans. In-service programmes should include conferences, workshops, and seminars to train instructors in the constructivist approach. Keywords: Application, Construction theory, History teaching, Students, Teachers.

From MNIST to ImageNet and back: benchmarking continual curriculum learning

Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop models and learning strategies that present high robustness in dynamic environments. This goal is realized by designing strategies that simultaneously foster the incorporation of new knowledge while avoiding forgetting past knowledge. The landscape of CL research is fragmented into several learning evaluation protocols, comprising different learning tasks, datasets, and evaluation metrics. Additionally, the benchmarks adopted so far are still distant from the complexity of real-world scenarios, and are usually tailored to highlight capabilities specific to certain strategies. In such a landscape, it is hard to clearly and objectively assess models and strategies. In this work, we fill this gap for CL on image data by introducing two novel CL benchmarks that involve multiple heterogeneous tasks from six image datasets, with varying levels of complexity and quality. Our aim is to fairly evaluate current state-of-the-art CL strategies on a common ground that is closer to complex real-world scenarios. We additionally structure our benchmarks so that tasks are presented in increasing and decreasing order of complexity—according to a curriculum—in order to evaluate if current CL models are able to exploit structure across tasks. We devote particular emphasis to providing the CL community with a rigorous and reproducible evaluation protocol for measuring the ability of a model to generalize and not to forget while learning. Furthermore, we provide an extensive experimental evaluation showing that popular CL strategies, when challenged with our proposed benchmarks, yield sub-par performance, high levels of forgetting, and present a limited ability to effectively leverage curriculum task ordering. We believe that these results highlight the need for rigorous comparisons in future CL works as well as pave the way to design new CL strategies that are able to deal with more complex scenarios.

Gluteal femoral subcutaneous and dermal adipose tissue in female.

During the sexual maturation, gluteal femoral adipose tissue is subjected to numerous modifications, not observable in other regions, in particular in women and less in men. Other authors described this region, but they used imaging techniques having lower resolution, than MRI proposed in this study. High resolution imaging techniques might provide important and more detailed information about the anatomy of gluteal femoral region. This study has been performed using 7 T-magnetic resonance imaging and ultrastructural analysis in order to provide accurate description of the subcutaneous adipose tissue and dermis of gluteal femoral region. In this study specimens harvested from cadavers and form living patients have been analyzed. The results showed the presence of three layers: superficial, middle, and deep, characterized by different organization of fat lobules. High resolution imaging showed the adipose papilla that originates from dermis and protrude in subcutaneous adipose tissue. Adipose papilla is characterized by a peculiar morphology with a basement, a neck and a head and these elements represent the functional subunits of adipose papilla. Moreover, ultrastructural study evidenced the relationship between adipocytes and sweat glands, regulated by lipid vesicles. This study provides important information about subcutaneous and dermal fat anatomy of gluteal femoral region, improving the past knowledge, and move toward a better understanding of the cellulite physiopathology.

21st-century stewardship: infusing environmental stewardship education with global citizenship

Environmental stewardship education, as a locally oriented concept, benefits from being recast to account for the emergent, multidimensional, and interdisciplinary nature of today’s most pressing socio-ecological issues. In an effort to infuse environmental stewardship education with global citizenship, instructional materials on community-based conservation in Peru were delivered to high school students in New York, USA. The purpose of this study was to assess the impact of instructional materials on student learning outcomes related to the practice of global citizenship in environmental stewardship education. The CLIA model for powerful learning environments was used to convey the competencies, learning conditions, interventions, and assessments that structured the delivery of the instructional materials. To evaluate learning outcomes, written student responses were analyzed, using the qualitative coding methodology. Additionally, transformative learning was assessed using the Learning Activities Survey. Results suggest a trajectory past knowledge comprehension and toward transformative, empowered action, progressing from a cognitive understanding to affective and ultimately behavioral shifts. Based on these findings, this study conceptualized and operationalized the concept 21st-century stewardship – a term coined here as an updated stewardship model that is infused with global citizenship’s multi-scalar perspectives, global compacts, and layered identities to achieve its maximum impact in our interconnected world.

Winning the Ad Battle: Exploring the Influence of Subtle Design Elements and Gaming Platform on Consumer Attention and Brand Memory in In-Game Advertising

Research on gamification of advertising concerning subtle design elements of computer games and their effects on consumers’ psychological responses are limited. The present research addresses this gap by examining how consumers’ attention and memory of brands advertised in a game, popularly called in-game advertising (IGA), are positively affected by the type of the gaming platform (mobile vs. PC) and rule orientation (or game rules) in the presence of potential moderators such as interactivity with the ad and congruence between the ad and game’s goals. Two experimental studies are conducted that investigate these afore-mentioned effects. The results indicate an interaction effect such that rule enactment under high ad interactivity and high ad–game goal congruence increases attention and memory of ads in mobile-based IGAs. Further, these affects are mediated through media and ad transportation, that is, players’ immersion experience in the flow of a story while playing the game. The research advances past knowledge on advertising effectiveness in computer games by exploring a less studied area, that is, the independent and interactive effects of game design elements. It also contributes to the flow experience literature in the domain of gamification by revealing a sequential pathway involving media and ad transportation. Also, it adds novelty to the gamification literature by exploring the role of gaming platforms and their interaction with design elements. For the game developers and advertisers, the research provides strategic guidance to manipulate these elements and to choose suitable platforms for the purpose of maximizing consumers’ favorable psychological responses.