Digital platforms have become a fertile ground for the emergence of "digital nomads", "healers", "digital entrepreneurs" and so on. In this presentation, drawing on the latest research on the "spread of superstition", "evolutions of meme dynamic and use of hashtags", "resilience theory", and insights from Native Americans' rituals and habits aka ways and methods of communication with the unknown, we argue that our ability to create cohesion and resilience in this emerging data-driven and data-enabled future is highly dependent on our capacity to verify the large stream of data that percolates into our awareness. We propose the need for a fundamental education plan on the nature of data and information, and development of new code of ethics and behaviour (e.g., verification system) in the cyberspace.

Many people spend their lives searching for the secret to happiness, but to scientists, happiness is no mystery. Countless studies have revealed the foundations of a fulfilling life: regular exercise, time outdoors, a strong network of friends and family. These findings have positioned social media and the forces that guide it, as diametrically opposed to our wellbeing. Netflix CEO Ted Sarandos has said that his company is competing with Star Wars, Pokemon GO and, most recently, sleep. What Netflix, new media, and social media companies are really fighting for is your time. The fight for your attention is nothing new, but this time, it’s personal. This presentation will analyze the ways in which advances in AI and the rise of Big Data have allowed companies to capture your attention like never before. We will consider how websites can personalize content, test changes, and iterate their designs at incredible speed in order to manipulate your behavior and opinions. Whenever you open an app, cutting edge algorithms and some of the most powerful machine learning tools ever created are being deployed to keep you there. AI is being used to predictably influence how users spend their time, money, and lives. What are these tools, where are they being used, and how can understanding them help fight their influence?

The current machine learning method has the disadvantages of relying on data and statistics, and the model is uninterpretable. So, we propose a model based on chaos. The model reconstructs the original system equivalently and explores the causality so that it can achieve accurate prediction of complex systems without statistics. At the same time, causality can explain the model. By analyzing the properties of chaos, such model is sensitive and adaptable to the change of conditions, and its learning process is more like humans’. This method works well and makes precise predictions in two complex nonlinear systems. We think this model may provide a new idea for current research.

In his 2002 essay for the Cambridge Companion to Gadamer, Charles Taylor mused, “The great challenge of the coming century, both for politics and for social science, is that of understanding the other” (Taylor, 126). Nearly two decades later we are challenged by a new other – the technological other of Artificial Intelligences. At the root, the questions and hurdles pressed by both the human 'other' and the technological 'other' are intertwined, involving all the same re-evaluations of consciousness, language, self-identity, emotion, meaning, and ultimately knowledge itself. No recent philosopher has spent more time dealing with these very inquiries than Hans-Georg Gadamer, and it is on his thought this essay will focus. Gadamer’s work stands alongside three or four others as a pillar of contemporary philosophical discourse, and his particular focus on aesthetics, language, and interpretation provides a vast resource for technical AI researchers and practitioners seeking to describe and advance both the inner workings of artificial minds and the relationships we might have with them. This essay will critically engage with Gadamer’s unique definition of knowledge primarily via his short papers "The Relevance of the Beautiful" (1986) and "Language and Understanding" (1970). I will suggest how Gadamer’s pioneering work on the phenomenology of language, embodied cognition, and philosophical hermeneutics can provide a framework for understanding the AI 'other'. Exploring Gadamer’s insights will further our ability to describe the interrelation of experience and knowledge and provide a path towards a more holistic model of human and artificial intelligence.

The concept of the Global Sustainable Information Society sums up the necessary conditions that need to be met if the challenges to the continued existence of humanity on Earth shall be successfully tackled. Societies are seen as social systems the further development of which in times of global challenges need a transformation (metasystem transition) such that they become elements of a higher-order system that expresses a new state of human co-operation. That state would allow for keeping the self-inflicted systemic dysfunctions below a threshold that threatens with self-annihilation. Intelligence, AI and wisdom are deemed different manifestations of informational capacities. It will be argued that – intelligence is a property inherent in any self-organising system (agent), that – wisdom is the highest product of intelligence that social systems and actors share, and that – AI is a non-agential (not self-organising) extension of social systems and actors that needs to be designed so as to support wisdom, that is, to enable new synergy for social systems and actors but not constrain them and undermine their autonomy.

Information is the representation of energy,energy is the noumenon and essence of information. Information and energy are both non material and objective existence. They have identity.The information of subjectivity is also composed of quantum sequences. Humans consume a lot of energy in thinking, which also proves the identity of information and energy.Simple information does not exist and is only possible through the medium of energy. Information and energy constitute the two sides of an entity, which embodies the identity of information and energy ,information is energy. While material and energy are mutually transformed, Spirit is a special information of subjectivity. This unifies the material, the energy, the information and the spirit,thus, for the first time in history, the material and spirit are fundamentally linked.

Operators have become one of the most important tools in theoretical physics and are also becoming an important tool in information theory. Computing devices are information transformers and generators. That is why in this work, we develop operator models of computing devices and study their properties based on the ontological operator theory. An operator is an object (system) that operates, i.e., performs operations on, some objects, systems or processes which are called operands of this operator. In other words, an operand is an object, system or process operated by an operator. Thus, being an operator or an operand is a role and a characteristic of a system. One and the same system/object can be an operator in some situations and an operand in other situations, as well as an operator with respect to some systems and not an operator with respect to other systems. All operators are systems, but not all systems are operators as they can exist and function in sizeable isolation from their environment. To construct a mathematical operator theory, it is necessary to organize or represent operands in the form of an operating space, i.e., the space which is transformed by an operator. Different types of operators function in distinct operating spaces. For instance, operators of quantum mechanics use Hilbert spaces as their operating space. Information operators function in information spaces. As there are diverse types of information, representation of them by operators demands different types of information spaces. According to the general theory of information, we differentiate syntactic, semantic, pragmatic, algorithmic, cognitive, emotional and effective information. Consequently, modeling computing devices by information operators, we can treat them as tools for transformation and generation of distinct kinds of information. Here we consider syntactic, semantic, and pragmatic information. This brings us to syntactic, semantic, and pragmatic information spaces.

Ritual artifacts are produced by individuals and/or small groups and left over-there, in the environment, perceivable, sharable, and more or less available. Artifacts of this type can be considered cognitive mediators in so far as they are collective memory stores of related habits, in the sense that they mediate and make available the story of their origin and the actions related to it, which can be learnt and/or re-activated when needed. Indeed, symbolic habits embedded in rites can also be seen as memory mediators which maximize abducibility, that is human capacity to guess hypotheses, because they maximize recoverability of already stored cognitive contents. In sum, once suitable representations are externalized in a ritual artifact, they can be sensorially picked up and manipulated to re-internalize them, when humans attend the rite: the externalization can be seen as the fruit of the so-called “disembodiment of the mind” as a significant cognitive perspective able to show some basic features of what I called manipulative abduction, that I will describe in my presentation. When analyzing artifacts and habits in ritual settings it is important to remember that also interesting cases of creative meaning formation are at play. Actually we can distinguish two kinds of habits that are at play in rites: (a) a knowledge-based kind of habit, for the analysis of which the concept of “affordance” is useful, which also plays a pivotal role in the justification of the agent’s own beliefs; and (b) an ignorance-based kind of habit, which will be proved as necessary for the beginning of thought, and which is at the base of the ampliative abductive reasoning.

Digital platforms have become a fertile ground for the emergence of "digital nomads", "healers", "digital entrepreneurs" and so on. In this presentation, drawing on the latest research on the "spread of superstition", "evolutions of meme dynamic and use of hashtags", "resilience theory", and insights from Native Americans' rituals and habits aka ways and methods of communication with the unknown, we argue that our ability to create cohesion and resilience in this emerging data-driven and data-enabled future is highly dependent on our capacity to verify the large stream of data that percolates into our awareness. We propose the need for a fundamental education plan on the nature of data and information, and development of new code of ethics and behaviour (e.g., verification system) in the cyberspace.

The approaches of symbolism, connectionism and actionism in the research of artificial intelligence all have important implications of knowledge theory, and have their respective status and functions in the research of AI. The key to the breakthrough of the core mechanism of artificial intelligence lies in the mechanism integration of three approaches. The mechanism-based approach of artificial intelligence has made an important research in this respect; this article tries to make further exploration in the perspective of integration of philosophy and science. The approach of symbolism is to establish formal relation system on the basis of prescription. Formal deduction is the basis for AI to establish relational system, but AI cannot really have the ability to understand only via formal deduction. An intelligent agent is a complex relation system. The connection in connectionism approach involves the mechanism of establishing internal relation, which is closely related to the evolution of internal structure in the process of intelligence evolution. The actionism approach involves a mechanism for establishing external relation and is closely related to the evolution of external structures in the evolution of intelligence. The integration of artificial intelligence research should be based on the formal deduction of symbolism, combined with the internal relations of connectionism and the external relations of actionism, and achieves intelligence evolution through firsthand experience in an intelligent agent community. Only in the evolution of an intelligent agent community, can the internal and external relations of an intelligent agent be formed.

Since the birth of artificial intelligence, the application of its methods and technologies has gradually penetrated into many fields, such as information security. With the development of information technology, the importance of information system security is also increasing. The security operation and maintenance of information systems requires a lot of work, so the use of artificial intelligence methods to enable machines to solve problems in the field of information security is inevitably an option. In recent years, information security has become an important area of artificial intelligence applications. Mostly it is necessary to judge the security status of systems according to security standards. However, these security standards are basically written in natural languages at the semantic level. Therefore security ontologies are developed by researchers to clearly express concepts and their relations in the information security domain. With these efforts researchers aim to share domain knowledge and to introduce the logical strictness of problem solving in their work. In this paper the existing main security ontologies are summarized, classified and compared from the perspective of knowledge representation, according to their concept classification, the complexity of the relationship between concepts, the purpose of knowledge representation, and the process of knowledge application. As a result of the analysis, we propose an ontology-based problem-solving framework in information security domain. Future research directions of knowledge representation in information security domain are also suggested.