Kind Of Intelligence Research Articles

Federated learning with hyper-network—a case study on whole slide image analysis

Federated learning(FL) is a new kind of Artificial Intelligence(AI) aimed at data privacy preservation that builds on decentralizing the training data for the deep learning model. This new technique of data security and privacy sheds light on many critical domains with highly sensitive data, including medical image analysis. Developing a strong, scalable, and precise deep learning model has proven to count on a variety of high-quality data from different centers. However, data holders may not willing to share their data considering the restriction of privacy. In this paper, we approach this challenge with a federated learning paradigm. Specifically, we present a case study on the whole slide image classification problem. At each local client center, a multiple-instance learning classifier is developed to conduct whole slide image classification. We introduce a privacy-preserving federated learning framework based on hyper-network to update the global model. Hyper-network is deployed at the global center that produces the weights of the local network conditioned on its input. In this way, hyper-networks can simultaneously learn a family of the local client networks. Instead of communicating raw data with the local client, only model parameters injected with noise are transferred between the local client and the global model. By using a large scale of whole slide images with only slide-level labels, we mensurated our way on two different whole slide image classification problems. The results demonstrate that our proposed federated learning model based on hyper-network can effectively leverage multi-center data to develop a more accurate model which can be used to classify a whole slide image. Its improvements in terms of over the isolated local centers and the commonly used federated averaging baseline are significant. Code will be available.

I, Robot: the three laws of robotics and the ethics of the peopleless economy

The world economy is currently witnessing an unprecedented surge in the ability of machines and algorithms to replace human labor, a surge with the potential to accomplish gains of efficiency and productivity that were unimaginable just a few years ago. Nonetheless, the wonders of the artificial intelligence economy are ultimately overshadowed by a series of concerns that decision-makers should account for: fast automation is synonymous with job losses, increasing income inequality, demand imbalances, and various sorts of insecurities and anxieties. The countervailing forces associated with the progress of automation raise doubts about whether the transition to the robot economy should advance full speed ahead. Rather than developing any artificial intelligence, it is necessary to develop the right kind of artificial intelligence. In particular, the progress of automation should be framed by fairness and ethical principles. This study discusses the ethical boundaries of the emergent robot economy. The discussion is supported on a theoretical model that is built and simulated with the goal of illustrating the transition to the robot economy. The virtues and flaws of this transition are considered.

The Role of Artificial Intelligence in Graphic Design

The research aims at studying the definition of artificial intelligence. It will present the history of AI. The research also demonstrates the different kinds of artificial intelligence. It also demonstrates the various applications of using AI. The research presents how graphic design uses AI in different purposes. Then the research discussed the impact of using artificial intelligence on the graphic design and how this will impact the profession.

Neural Networks, Fuzzy Systems and Pattern Recognition: A Comparative Study

Artificial Intelligence (AI) and Machine Learning (ML) have been rapidly advancing in recent years, with many new techniques and models being developed. One area of AI and ML that has more focuses on Pattern Recognition (PR). PR is a subfield of ML that deals with the identification and classification of patterns in data. This field is closely related to other subfields of AI and ML, such as Neural Networks (NNs) and Neuro-Fuzzy Systems (NFS). NNs are a kind of artificial intelligence inspired by the way our brains work. This paper will provide a comparative research of three fields: Neural Networks (NNs), Neuro-Fuzzy Systems (NFS) and Pattern Recognition (PR), highlighting their similarities and differences. NNs, NFS, and PR are three closely related fields of research in the field of AI and ML. The paper begins with a brief introduction to each of these fields, followed by a discussion of their similarities and differences. NNs are a type of AI that are modeled after the function and structure of the human brain system. They integrate a wide-range of interlinked processing nodes, known as neurons that are used to perform various tasks such as PR and control. NNs are particularly useful for tasks that involve large amounts of data, such as image and speech recognition.

The two kinds of artificial intelligence, or how not to confuse objects and subjects

The two kinds of artificial intelligence, or how not to confuse objects and subjects

Businesses Innovation & AI: How AI and Analytics Can Encourage Innovation

Artificial intelligence is about imbuing machines with a kind of intelligence that is largely attributed to humans. Extant literature, in addition to the encounters of ours as providers, indicates that while AI may not be entirely set to take over revolutionary areas within the improvement procedure, it indicates promise as a significant guidance to advancement superiors. In this particular content, we broadly relate to the derivation of PC enabled, data-driven insights, models, and visualizations within the development pastime as innovation analytics. AI may play a crucial role in the innovation exercise by switching many aspects of innovation analytics. We existing four case studies which are distinct of AI in motion according to the prior job of ours in the market. We highlight the benefits and limitations of utilizing AI in growth, and conclude with strategic implications and additional resources for development managers.

Humanoid and machine artificial intelligence in science fictions

There are a lot of different debates about artificial intelligence. Indeed, in the last several decades one can witness a shift in Hollywood SciFi movies. Many of the older stories were puzzled about human and machine identities as they appeared to become uncannily indistinguishable. More recent stories deal with disappointment and shock as the machines which emulate and exceed human reasoning prove to have a kind of intelligence that is very different from human thinking and feeling. The following observations seek to reflect the significance of this shift. On the one hand, it is referred to a change in the orientation of AI research itself - transition from humanoid AI to machine one. On the other hand, these movies do not so much analyze impact and future of two AI kinds, as they reflect on the cultural conditions, hopes and fears that motivate or ground AI research. Thus, science fiction can be a stage or a representational framework for an interpretative interaction with technology, that is, for understanding artificial intelligence, its significance and importance for various ways people understand and position themselves in the world of signs and symbols, objects and work products.

Artificial Intelligence Operating Model: A Proposal Framework for AI Operationalization and Deployment

At the heart of the newenterprise, across all activities, is a decision factory governed by some kindof intelligence. Among the great promises of Artificial Intelligence (AI) isits ability to lead to a significant evolution in the amount of data received,processed, or generates by companies, particularly those with a digitalconnotation. To bring about dramatic changes, AI does not need to be sciencefiction but simply a new way of approaching computerization subjects whether interms of design, development, or terms of expected results. It should be notedthat traditional IT solutions present a form of AI called - Weak AI - while theAI that is the subject of much noise, hype, and promises of transformation andpotential for growth is called - Strong AI -. This article aims to present, ina didactic way, a model called D2MO (For Data Ops, ML Ops, Model Ops, and AIOps) allowing the company to operationalize, in a structured approach, AIsubjects, activities, and projects. We target through this article to provideboth IT and business experts with a new framework offering a perfectarticulation between the different bricks and actors entering into thecomposition of an AI-based system thus allowing them to operate in harmony andan agile mode while taking advantage of this technology.

DAD-Net: Classification of Alzheimer's Disease Using ADASYN Oversampling Technique and Optimized Neural Network.

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.

Intelligence in light of perspectivalism and AI responsibilization

What does the I in the composite “AI” stand for? The answer to this guiding question appears to be straightforward, namely intelligence; however, at secunda facie, one might wonder what intelligence really is. Even though definitions of intelligence have been provided in psychology, neuroscience, and animal cognition research, among others, these crude approaches turn out to be overly systematic, rigid and reductive (Hoffmann, 2022b). At the same time, novel types of Artificial Intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. In this paper, we devote ourselves to addressing this need by proposing an open malleable and loose framework for making sense of intelligence in humans, other animals and AI, which is ultimately based on causal learning as the central theme of intelligence. The goal is to showcase the instrumental value of this framework for AI responsibilization.

Detection Technology of Social Robot: Based on the Interpretation of Botometer Model

In the era of Web 2.0, social media have been a significant place for democratic conversation about social or political issues. While in many major public events like the Russia-Ukraine war or U.S. Presidential election, enormous social bots were found on Twitter and Facebook, putting forward public opinion warfare. By creating the illusion of grassroots support for a certain opinion, this kind of artificial intelligence can be exploited to spread misinformation, change the public perception of political entities or even promote terrorist propaganda. As a result of that, exploiting detection tools has been a great concern since social bots were born. In this article, we focused on Botometer, a publicly available detection tool, to further explain the AI technologies used in identifying artificial accounts. By analyzing its database and combing the previous literature, we explained the model from the aspect of data augmentation, feature engineering, account characterization, and Ensemble of Specialized Classifier (ESC). Considering the consistent evolution of social bots, we propose several optimization suggestions and three other techniques or models to improve the accuracy of social bots detection.

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control.

By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined with mobile communication which led to the artificial intelligence online sports games teaching, pattern recognition, and virtual technology combined with innovative teaching interaction and experience. Promoting the development of green PE teaching machine learning can identify the types of PE activities and realize efficient PE learning diagnosis. Intelligent decision support system can identify sports talents and improve the effect of personalized PE teaching evaluation. From the perspective of psychological development and education, the key problems to be solved in the integration of artificial intelligence and physical education are examined. Then, the consistent model predictive control for feedback delay of nonlinear sports learning multiagent system with network induced delay and random communication protocol is studied. Under the communication waiting mechanism designed, each agent has a certain tolerance of delay, and this tolerance can be determined by ensuring the stability of the system. At the same time, a random communication protocol is designed to ensure the ordered communication of the multiagent system. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation. To solve the channel competition access problem of the sports intelligent learning system with special structure feedback delay model predictive control, a dual channel awareness scheduling strategy under the model predictive control framework was proposed, and the distributed threshold strategy of sensors and the priority threshold strategy of controllers were designed. It is proved that the sensor will eventually work at Nash equilibrium point under the policy updating mechanism, and the priority threshold strategy of the controller is better than the traditional independent and identically distributed access strategy. By avoiding the data transmission when the channel status is poor, the channel access of the system is efficient and saves energy.

Could a Computer Learn to Be an Appeals Court Judge? The Place of the Unspeakable and Unwriteable in All-Purpose Intelligent Systems

I will take it that general intelligence is intelligence of the kind that a typical human being—Fred, say—manifests in his role as a cognitive agent, that is, as an acquirer, receiver and circulator of knowledge in his cognitive economy. Framed in these terms, the word “general” underserves our ends. Hereafter our questions will bear upon the all-purpose intelligence of beings like Fred. Frederika appears as Fred’s AI-counterpart, not as a fully programmed and engineered being, but as a presently unrealized theoretical construct. Our basic question is whether it is in principle possible to equip Frederika to do what Fred does as an all-purpose participant in his own cognitive economy. Can she achieve a sufficiency of relevant similarity to him to allow us to say that she herself can do what Fred can do, perhaps even better? One of the things that Fred can do—or at least could learn from experience to do—is discharge the duties of an Appeals Court judge. As set down in the ancient doctrine of lex non scripta, Fred must be able to detect, understand and correctly apply certain tacit and implicit rules of law which defy express propositional formulation and linguistic articulation. Fred has an even more widespread capacity for the epistemically tacit and implicit, clearly one of his most cost-saving kinds of intelligence. Indeed, most by far of what Fred will ever know he will know tacitly and implicitly. So we must ask: how tightly bound to the peculiarities of Fred’s cognitive enablement conditions is the character of the intelligence that he manifests? And how far down Fred’s causal make-up does intelligence actually go?

Requirements and challenges for hybrid intelligence: A case-study in education

The potential for Artificial Intelligence is widely proclaimed. Yet, in everyday educational settings the use of this technology is limited. Particularly, if we consider smart systems that actually interact with learners in a knowledgeable way and as such support the learning process. It illustrates the fact that teaching professionally is a complex challenge that is beyond the capabilities of current autonomous robots. On the other hand, dedicated forms of Artificial Intelligence can be very good at certain things. For example, computers are excellent chess players and automated route planners easily outperform humans. To deploy this potential, experts argue for a hybrid approach in which humans and smart systems collaboratively accomplish goals. How to realize this for education? What does it entail in practice? In this contribution, we investigate the idea of a hybrid approach in secondary education. As a case-study, we focus on learners acquiring systems thinking skills and our recently for this purpose developed pedagogical approach. Particularly, we discuss the kind of Artificial Intelligence that is needed in this situation, as well as which tasks the software can perform well and which tasks are better, or necessarily, left with the teacher.

The Importance of Plants in Ancient Cultures Against the Background of New Research Concerning Intelligence of Plants

Juicy fruit, healthy vegetables, herbs for all the ailments, delightful flowers, a shelter under the spreading branches of an old tree - can plants be something more for man, and can they be even equal to him? Where is their place in the world, which is dominated by man and his helpers - animals? This article leads the reader through the thoughts and beliefs of ancient peoples, showing their respect for everything that lives until the modern times, when scientists try to relate the definition of intelligence and consciousness to plants. Myths from various parts of the globe, illustrate how important a role plants had in the past, that they were much more than food, medicine, or refuge. Since the beginning of time, without new technologies, people felt the power of nature and respected the otherness of creatures, wandering with them, step by step, with fear and awe. Plants, though “immobilised”, could create, and decided on the world’s fate, and were messengers between men and other organisms, an essential link, unique and holy. Today, when one can analyse plants’ functions and behaviour, observe how they communicate with the world, using all of the senses not located in specific organs, but spread all over the organism, man starts to notice what his ancestors saw a long time ago: that plants remember, decide, learn and give humans a wide range of opportunities. Having far more senses than man, showing humans a different kind of consciousness and intelligence, not centralised, to which one is used to, but dispersed, though not less efficient, plants are becoming very interesting subjects of scientific research.

Study on social competence and educational aspirations related to emotional intelligence

Social competence and emotional intelligence have become popular phrases in the current educational scenario. It is a kind of social intelligence that includes the ability to monitor one's feelings and emotions, discriminate among them, and utilize information to guide one's thinking and action. Emotional intelligence is a variety of non-intellectual capacities, capabilities, and abilities that impact coping with environmental demands and pressure. Education is essential for human development. But lack of institutions with sufficient infrastructural facilities, lack of competent teachers with practical communication skills in the tribal language, and lack of enough textbooks have been the leading cause of educational deprivation of some of the scheduled community members. To cope with the necessities of the modern world, the government and private schools should bring them realism in contemporary education. This paper investigates the social competence and educational aspirations related to the emotional intelligence of secondary school students. The sample size is 89 students from Private and Government schools. The researcher collected primary data from secondary school students using structured questionnaires and the secondary data from the school website and reports.

Multiple intelligences in making individual prospective vocation

Among the models of intelligence one of the most attentive is Gardner’s theory of multiple intelligences where he proposes its eight distinct forms viz. Linguistic, Logical-Mathematical, Spatial, Musical, Naturalistic, Bodily-Kinesthetic, Interpersonal and Intrapersonal Intelligence. By leaving the exceptional cases it can be said that each individual has these forms of intelligences altogether that constitute his/her total cognition. In diverse set up of the problems these forms emerge (sometimes alone or sometimes in a mixed form) as an individual’s ability to find solutions for the same. According to this theory an individual may own a profile of intelligences and he may be high in one while moderate or low in another. These intelligences influence one’s learning and approach to do any other task assigned to him. There is a need too peep into an individual being to know what kind of intelligence dominates there in. This paper would attempt to focus on such relatively high range of intelligences that may influence not only one’s learning within the classroom but also to his/her prospective vocation.

THE APPLICATION OF MUSICAL INTELLIGENCE-BASED MATHEMATICS LEARNING ON PLANE SHAPE DISCUSSION

In this study, Gardner's theory of multiple intelligences was used to conduct experimental research on several elementary school students at TPQ Al-Ikhwan Meruya Jakarta, Indonesia. The initial stage of this study was to classify the types of student intelligence using multiple intelligence instruments adopted from Shearer in 1997. Furthermore, learning strategies are applied to students according to the kind of intelligence. This study observed the differences in mathematics learning outcomes of students who have musical intelligence before and after the application of interactive learning media in the form of music containing a song with the title "Aku Bangun Datar" and the lyrics are examples of a two-dimensional figure with natural objects. The research method used was a quantitative quasi-experimental type with a pre-test-post-test design. Research data analysis using a statistical t-test for the difference in the mean of the two populations. The results of data analysis obtained a t value of -8,000, while the t table is -2.0686. The comparison between the t value and the t table is known that t is more significant than the t table, so the hypothesis is rejected. It means that there is a significant difference in students' knowledge about shapes before and after using music media.

Afterword: Some Illustrations

Afterword: Some Illustrations

Nietzschean Perspectives on Intelligence: In Need of More Plurality for Making Sense of Intelligence

Philosophers as well as scientists in psychology, neuroscience, animal cognition research etc. have not found satisfying answers to what intelligence is with their crude, overly systematic and reductive approaches. At the same time, both recent findings about the capabilities of smart animals such as corvids (Nieder et al., 2020) or octopi (Godfrey-Smith, 2018) and novel types of Artificial Intelligence (AI), from social robots to cognitive assistants, are provoking the demand for new answers for meaningful comparison with other kinds of intelligence. In this paper, we devote ourselves to addressing this need by proposing an open malleable and loose framework for making sense of intelligence in humans, other animals and AI, which is ultimately based on causal learning as the central theme of intelligence. The goal is not just to describe, but mainly to explain queries like why one kind of intelligence is more intelligent than another, whatsoever the intelligence.