Various aspects of progress are investigated, the role of science in the realization of progress. In fact, science has also brought pseudo progress or even regression. Attempts to formulate conditions for increasing the chances of achieving progress, also with science.
Four topics are introduced they collectively argue that artificial intelligence—particularly neural networks—forces a rethinking of how we formalize knowledge, discovery, and mental processes. Part 1 “From Organization to Generation: Rethinking Formalization in Light of AI” proposes that AI can give us a genuine logic of discovery. Part 2 “AI Learning and the Gettier Problem: A Solution Through Artificial Intelligence” applies this to epistemology, using AI to resolve the Gettier problem and arguing for a coherentist view of knowledge. Part 3 “The Chomskyan Challenge to Connectionism” reconciles Chomsky’s Universal Grammar with connectionism by recasting rules as neural constraints. Part 4 “AI and the Inadequacy of the Computational Theory of Mind” goes further, arguing that the success of modern AI challenges the computational theory of mind entirely, suggesting intelligence is fundamentally analog. Together, the four parts shift from explicit, rule-based formalizations to emergent, constrained, or analog models of cognition - while disagreeing on how much formal structure remains.
This study evaluates the performance of various machine learning models in detecting SSDP Flood attacks within network traffic using the Kitsune Network Attack Dataset. SSDP Flood attacks exploit the Simple Service Discovery Protocol (SSDP) to overwhelm target servers with a flood of unwanted traffic, effectively consuming bandwidth and resources and rendering services unavailable to legitimate users. We compare Linear Discriminant Analysis (LDA), Logistic Regression, and a hybrid Convolutional Neural Network (CNN) combined with Gated Recurrent Units (GRU) and Long Short- Term Memory (LSTM) networks. Our analysis focuses on accuracy, precision, recall, F1-score, and computational efficiency. The CNN- GRU-LSTM model demonstrates robust performance across both malicious and non-malicious classes but requires more computational resources. We also employ SHAP (SHapley Additive exPlanations) to interpret model decisions and identify key features influencing attack detection. Our findings highlight the trade-offs between model performance, complexity, and computational demands, providing valuable insights for selecting appropriate models in real-time network security applications.
Consciously designing and developing a personal lifestyle is one of the most vital tasks in a person's life. It is the architecture of colouring our existence. It shapes our individuality, forms the basis for our self-confidence and colours the perspective we have on our immediate environment and our future. A lifestyle is not a static whole, but a dynamic and holistic interplay of many facets. For some aspects, our lifestyle can be very detailed and consciously designed, while other facets remain unconscious and rudimentary. Here we explore the core aspects of lifestyle, place them in the context of life stages and approach crisis and crisis management of an individual, social, environmental and climate nature as a trigger and integral part of wise lifestyle management, development and adaptation.
In this article authors use as starting point the book "Europese Euculturalisatie". In the light of the cognitive and communicative challenges faced by today’s youth - exacerbated by the pervasive influence of social media - they also examine the transformative potential of after-school activities as a pragmatic avenue for fostering Euculturalisation in younger generations. In this perspective, a comparison is made between the Sumerian Harmony Euculturalisation and the Utopian TESCREAL Euculturalisation.
This paper argues that classical logic fundamentally fails as a tool for reasoning because it requires more intelligence to recognize that an inference instantiates a logical law than to recognize the validity of the inference directly. Drawing on evidence from artificial intelligence systems, particularly large language models, we propose an alternative "System L" that better captures how both human and artificial minds actually reason. This system emphasizes pattern explicit recognition, meta-reasoning templates, and defeasible inference rather than rule application, suggesting reconceptualization of logic's nature and purpose.
In recent years, AI-related technology referred to as RAG (Retrieval Augmented Generation) (Lewis, 2020) gained a lot of attention. In the RAG-approach, custom sources of information are used to seed the knowledge obtained from a LLM (Large Language Model), thus forming an approach which solves the issue of adapting the LLM to cope with custom external information. Using the RAGscenario, various information processing use cases can be implemented, such as AI-based document management, AI-enhanced web search, AI-based online service support, etc. This paper outlines the main components of the RAG-workflow such as chunking and embedding the input documents, as well as similarity search, and LLM -based user query processing. The RAG approach is illustrated via Python implementation which is used to validate the procedure via a simple example of processing a multi-topic document. Experimental results are discussed showing the feasibility of this approach, as well as illustrating the need for further research and enhancements, for example by the use of the RAPTOR concept (Sarthi, 2024).
Gnosts: concise expression of a perspective, which in one way or another, shed some light on an aspect of the immense field of wisdom, knowledge, intelligence, AI (Artificial Intelligence), AW (Artificial Wisdom ) are introduced.
In section 1 this paper argues that the demonstrated capabilities of large language models (LLMs) provide surprising empirical support for classical theories of meaning, particularly the distinction between semantics and pragmatics and the reality of compositional literal meaning (Partee, 2018). While LLMs employ connectionist architectures rather than classical computational ones, their ability to systematically process novel sentences and distinguish between literal and contextual meaning suggests that key insights of classical semantic theory capture genuine features of linguistic understanding, even if the underlying mechanisms differ from those traditionally posited (Bommasani et al., 2021). In section 2, this paper argues that the demonstrated capabilities of large language models (LLMs) provide surprising empirical support for classical theories of grammar, particularly regarding the relationship between syntax and semantics (Manning et al., 2022). While LLMs employ connectionist architectures rather than classical computational ones, their ability to process structural relationships independently of meaning while maintaining systematic syntaxsemantics mappings suggests that key insights of classical grammatical theory capture genuine features of language, even if the underlying mechanisms differ from those traditionally posited (Linzen & Baroni, 2021). In section 3, this paper argues that the demonstrated capabilities of large language models (LLMs) provide surprising empirical support for the alignment of grammatical and logical form (Chowdhury & Linzen, 2021). While philosophers have traditionally posited a divergence between grammatical and logical structure, LLMs' ability to make correct inferences without FOL-style logical forms suggests that grammatical structure itself guides valid reasoning (Manning et al., 2022). This indicates that the perceived misalignment between grammatical and logical form may be an artifact of our chosen formal systems rather than a feature of language itself.
Attention is paid to the scientific results concerning the advantages of being left-handed as well as to the disadvantages. Moreover what does science learn about how to exploit the advantages and how to defeat or even turn the disadvantages of left handedness into benefits, realizing success taking into account wisdom as a search for realising harmony: a dynamic process in always changing form, degree and adventure…
How to avoid that humans through computers with AI destroy their own environment and that of many others to such an extent that their survival, and possibly that of the ecosphere and of humanity itself, is threatened. This development we can call “tragical”. Why? Typical for a tragedy in its original Greek meaning, is that it leads to the opposite ultimately of the intended goal. The basic target of communication, cognition, intelligence, AI is more individual and general wellbeing, happiness… but de facto it is realizing again and again disasters, destructions, genocides… How to avoid these tragic developments? Is Wisdom and “AW: Artificial Wisdom” a relevant factor to avoid or minimalize these tragic risks ? How and why?
Nationalism is an enormous political and cultural force. It is a highly efficient mobilizer of individual and social resources. However, it is also a very dangerous phenomenon. It can easily escape control. Moreover, there are various processes active also in nationalist movements that can be highly destructive. These are (1) the risk that democracy will weaken under nationalism; (2) a strong trend is emerging towards creating greater scale; (3) a third process concerns Braudel's law with increasing violence as a result; (4) the individual but also social law of tendency of more or less unconscious transmission of historically experienced violence, exploitation and injustice, which in turn threatens to be used more or less consciously by an individual or group on other weak, defenseless individuals and groups… History repeats itself but in doing so sometimes transforms the victim into the executioner; (5) vagueness of the boundaries of a specific nationalism. What falls within, what falls outside? This easily leads to discord, conflict, violence…; (6) the danger of degeneration of the ambivalence of the interrelations between individuals and subgroups inside a nationalistic movement; (7) the risk of high investment in armament and welfare( economic success) that gets priority over individual and social wellbeing. To 52 avoid these risks a lot of wisdom, even AW: Artificial Wisdom, rather then only AI is needed!
The most prominent physicist of the twentieth century, Albert Einstein mentioned some important approaches to fundamental scientific research which he believed to be extremely useful in the quest for unveiling secrets of nature. The first part of this paper presents Einstein’s guides to scientific discoveries as a unity of 5 methodological principles. Being a founder of fundamental scientific concepts Einstein had a rich experience in proving and convincing his contemporaries to abandon old, classical theories and to develop natural sciences in the light of his revolutionary ideas. It is shown in the second part of this paper that Einstein’s scientific heritage contains several invaluable suggestions that could build the basis for the general theory of meta-argumentation.
In this paper, research results are presented concerning the fast improving capabilities of today’s Large Language Models (LLMs). The accessibility and the capabilities of state-of-the-art LLMs are illustrated based on their online versions provided by OpenAI, Google, and Anthropic. The initial focus is on accessing the LLMs via web APIs and Python client applications, and the key part of this work focuses on testing the capabilities of LLMs in tasks such as text-based q&a sessions, knowledge assistance, text- and scenario analysis, document summarisation, image interpretation, and more. Experimental results are based on top-ranked LLMs from chatbot ranking available on the Hugging Face website, which presently are GPT-4, Gemini 1.5 Pro, and Claude-3 Opus. For these 3 models, test outcomes are assessed and compared in the areas such as a stateful q&a sessions, among others concerning one of the most challenging books in English literature (“Ulysses”of James Joyce), an analysis of a false-belief Theory of Mind (ToM) scenario, and summarisation of scientific publications. In the final part, attention is given to text sentiment analysis approaches, and detailed experiments are presented concerning image description and mathematical operations on image elements carried out by the latest GPT-4o (omnium) multimodal LLM from OpenAI. Also, a literature study is provided concerning speech modules for OpenAI and Google Vertex AI LLMs. The major conclusion from this research is that fast-improving capabilities of today’s LLMs create high potential for their wide use.
Early and accurate detection of breast cancer is crucial for public health. This research explores using machine learning (ML) to address this challenge. The goal is to develop models that can analyze clinical data and differentiate between benign and malignant cases. By evaluating different ML algorithms, the study hopes to identify effective tools for breast cancer diagnosis. This strategy includes data processing, feature selection, developing a complete model, and scoring it with the WBCD dataset. The study involved a comprehensive ensemble of ML classifiers: logistic regression, decision trees, random forest, and support vector machine. We operationalize these models into careful experiments and evaluations, preserving accuracy, precision, recall, and F1-score together with AUC. However, the results provide a hint for testing the ML model on breast cancer diagnosis. SVM was one of the best models able to attain the highest accuracy rate, highest precision, highest recall, and highest F1. The other models, like logistic regression and random forest, used to show good performances, whereas decision trees showed interpretability advantages with a little lower accuracy. This finding will be applicable as a substitution in early detection and intervention for breast cancer, in the long run for better patient outcomes, and the advancement of medical diagnostics using ML techniques. Much interdisciplinary effort, therefore, is necessary to provide robust diagnostic tools with an understanding of breast pathologies. Much work is expected to be focused on further fine-tuning the ML models, integration of such multimodal data, and clinical validation to the highest standard so that it becomes easy to use
Humanity had up to the industrial revolution an entirely different life style, a way of organisation of day and night, of sleeping and waking up. It is important to dare to question our present customs, habits, practices and organizations. To confront these with past practices, with habits of other cultures, even of other animals. All these can be heuristic sources for elaborating and building way of lives, to get more cognitive and communicative efficiency in view of more wellbeing and happier developments and better changes for survival in the fast changing environment… The options on Biphasic sleep are challenging examples. For elderly people, for instance, the biphasic sleep organization perhaps can help to better realize and stimulate their cognitive and communicative skills and challenges… Moreover, the preindustrial way of life based on Biphasic sleep, can also be more useful and generate a society with more wellbeing, also in the future in view of the expected 28 hour (or even less) working week in the modern industry and business week planning.
From the study of Physical and Social Dissipative Structures we learn that dispersion, destruction (see Hurricanes) is a very important factor in creating a new order, a new harmony. Mostly, however, it is an order, a harmony from a much lower quality. The use of wisdom is the greatest priority to target realizing Euculturalisation and to minimalize dispersion, destruction and to eliminate the blinding progress myth that hides and is a cover up for the destruction under the pretext of progress and which is responsible for the decline of the level of harmony and the loss of wellbeing of mankind, living beings in general and climate disasters.
This paper elaborates on the attempts in medical science to tackle complex or non-linear phenomena at the microscopic scale by a process of linearization. Questions of predictability and computability of stochastic phenomena are at the basis of the extrapolation of molecular interactions from the microscale to the macroscale of the cellular machinery and of the multicellular organism. The effects of this linearization procedure not only are observed in the handling of cellular processes in medicine, but also generate consequences at the population level in the organization of public health. The notion of dissipative structure thus is inferred at several levels of organization. This paper is centered around the discovery of the (messenger) RNA concept by the Brussels Rouge-Cloître Group, a discovery that played a key-role in the postwar developments of molecular biology and genomics, and consequently also in epidemiology and vaccine development. In the footsteps of the philosophy of Peter Sloterdijk, the conceptual framework of immunology not only is found to constitute a medical discipline, but it is also identified as a group-forming (neuro-) semantic mechanism. As such it forms a target in AI-generated networks. A final note is added to the need and urgency of limitations set to the exploitation of these semantic mechanisms, for the sake of human social cohesion.
The present focus of my research, extension online courses for teaching early literacy is aimed at eradicating functional illiteracy in Brazil, to form critical readers of texts circulating socially, able of writing what is appropriate to their pragmatic intentions, in different communicative situations. The instrument to achieve this goal is Scliar Early Literacy System (SSA), which contains the proposal foundations, the methodology, the instructions for applying the Units in the classroom, the reading and writing books for both teachers and students and the students' respective activity notebooks. This research goal is the teachers’ continuous training for improving young people and adult education, adopting a cutting-edge methodology, inspired by linguistic, neuroscience, neuropsychology, psycholinguistic and sociolinguistic advances. In this paper, I will deal with how to reach a qualitative leap in early literacy among young people and adults, changing the foundations that guide the mediator’s training, the methodology and the pedagogical material, from a perspective associating human and biological sciences. We cannot separate culture from the biological, from the brain, whose structure and functioning make its emergence possible.
The focus is on the relations between intelligence, Wisdom, Artificial Intelligence, Artificial Wisdom. We elaborate some of the main forms of generation of Wisdom, and the application of Wisdom. This as introduction to preparation of the elaboration and application of AI and AW: Artificial Wisdom and in view of acceptable uses. Special attention is given to self-correcting driven Wisdom, Business Wisdom, Nominalistic Wisdom, Design Wisdom and the question of danger of dehumanisation, decognition and decommunication by AI and even by AW: Artificial Wisdom
