This paper develops a critical theory of artificial intelligence, within a historical constellation where computational systems increasingly generate cultural content that destabilises traditional distinctions between human and machine production. Through this analysis, I introduce the concept of the algorithmic condition, a cultural moment when machine-generated work not only becomes indistinguishable from human creation but actively reshapes our understanding of ideas of authenticity. This transformation, I argue, moves beyond false consciousness towards what I call post-consciousness, where the boundaries between individual and synthetic consciousness become porous. Drawing on critical theory and extending recent work on computational ideology, I develop three key theoretical contributions, first, the concept of the Inversion to describe a new computational turn in algorithmic society; second, automimetric production as a framework for understanding emerging practices of automated value creation; and third, constellational analysis as a methodological approach for mapping the complex interplay of technical systems, cultural forms and political economic structures. Through these contributions, I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism. The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it and that it represents a growing challenge for contemporary critical theory.

Anomalies represent deviations from the intended system operation and can lead to decreased efficiency as well as partial or complete system failure. As the causes of anomalies are often unknown due to complex system dynamics, efficient anomaly detection is necessary. Conventional detection approaches rely on statistical and time-invariant methods that fail to address the complex and dynamic nature of anomalies. With advances in artificial intelligence and increasing importance for anomaly detection and prevention in various domains, artificial neural network approaches enable the detection of more complex anomaly types while considering temporal and contextual characteristics. In this article, a survey on state-of-the-art anomaly detection using deep neural and especially long short-term memory networks is conducted. The investigated approaches are evaluated based on the application scenario, data and anomaly types as well as further metrics. To highlight the potential of upcoming anomaly detection techniques, graph-based and transfer learning approaches are also included in the survey, enabling the analysis of heterogeneous data as well as compensating for its shortage and improving the handling of dynamic processes.

Voice assistants, smart homes, or industrial 4.0 systems: artificial intelligence (AI) is increasingly automating processes in a wide variety of living and working environments. However, AI systems often prove to be not particularly competent because they lack either background or contextual knowledge, are unable to assess the scope and implications of assumptions and decisions, and cannot explain their actions. In the Joint Artificial Intelligence Institute (JAII), the two universities at Bielefeld and Paderborn are combining their research competencies in this field of research. The universities jointly founded the institute on July 14, 2020. In the JAII, future research will address the fundamentals of AI systems designed to focus on people.

In any normal year, Cambridge Analytica would have been the biggest story. Facebook alone had a royal flush of scandals, including a huge data breach in September, becoming the subject of multiple class action lawsuits for discrimination, accusations of inciting ethnic cleansing in Myanmar, potential violations of the Fair Housing Act, and hosting masses of fake Russian accounts. Throughout the year, Facebook executives were frequently summoned to testify, with Mark Zuckerberg himself facing the US Senate in April and the European Parliament in May. News broke in March that Google was building AI systems for the Department of Defense's drone surveillance program, Project Maven. The news kicked off an unprecedented wave of tech worker organizing and dissent.

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In the 1970s, Dr. Geoffrey Franglen of St. George's Hospital Medical School in London began writing an algorithm to screen student applications for admission.

Computer science seems still to be one of the most popular majors for college students. Most students, of course, don't go on to become computer scientists; instead, they fill the ranks of the vast upper-middle-class of business managers and professionals by utilizing their analytical thinking skills. The theoretical models they learn in their college classes inform the way they think about the world, even if they don't end up using them for coding purposes after final exams are over. There is at least one gaping hole in the education most computers science majors receive. They learn plenty of algorithmic models, but they aren't often taught to think critically about what they learn.

The second and the most extensive type in our Synthetic Intelligent Lifeforms classification is the Cyberphysical type. Cyberphysical type is a type of Synthetic Intelligent Lifeform based on technical and information systems and capable of perceiving, managing and changing both physical and virtual environments. Robots and autonomous machines can be either partially autonomous (have connection with the operator), or completely autonomous (act in accordance with the program laid down in it). There are the following types of robots and autonomous machines: - Industrial robots and autonomous machines; - Transport robots and autonomous machines; - Medical robots and autonomous machines; - Military robots and autonomous machines; - Information and communication robots and autonomous machines. This type of robots can be interacting with the surrounding space through sensors and other automated technical systems that allow to process and analyze information as well as to carry out certain actions on the basis of this information.

Increasing complexity of technical systems requires a precise fault localization in order to reduce maintenance costs and system downtimes. Model-based diagnosis has been presented as a method to derive root causes for observed symptoms, utilizing a description of the system to be diagnosed. Practical applications of model-based diagnosis, however, are often prevented by the initial modeling task and computational complexity associated with diagnosis. In the proposed thesis, we investigate techniques addressing these issues. In particular, we utilize a mapping function which converts fault information available in practice into propositional horn logic sentences to be used in abductive model-based diagnosis. Further, we plan on devising algorithms which allow an efficient computation of explanations given the obtained models.

The main prospective aim of modern research related to Artificial Intelligence is the creation of technical systems that implement the idea of Strong Intelligence. According our point of view the path to the development of such systems comes through the research in the field related to perceptions. Here we formulate the model of the perception of external world which may be used for the description of perceptual activity of intelligent beings. We consider a number of issues related to the development of the set of patterns which will be used by the intelligent system when interacting with environment. The key idea of the presented perception model is the idea of subjective reality. The principle of the relativity of perceived world is formulated. It is shown that this principle is the immediate consequence of the idea of subjective reality. In this paper we show how the methodology of subjective reality may be used for the creation of different types of Strong AI systems.