http://dl.gi.de/handle/20.500.12116/36181 2026-07-21T13:27:38Z http://dl.gi.de/handle/20.500.12116/36192 Intelligent Behavior Depends on the Ecological Niche Eppe, Manfred; Oudeyer, Pierre-Yves 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36190 Assessing the Attitude Towards Artificial Intelligence: Introduction of a Short Measure in German, Chinese, and English Language Sindermann, Cornelia; Sha, Peng; Zhou, Min; Wernicke, Jennifer; Schmitt, Helena S.; Li, Mei; Sariyska, Rayna; Stavrou, Maria; Becker, Benjamin; Montag, Christian In the context of (digital) human–machine interaction, people are increasingly dealing with artificial intelligence in everyday life. Through this, we observe humans who embrace technological advances with a positive attitude. Others, however, are particularly sceptical and claim to foresee substantial problems arising from such uses of technology. The aim of the present study was to introduce a short measure to assess the Attitude Towards Artificial Intelligence (ATAI scale) in the German, Chinese, and English languages. Participants from Germany (N = 461; 345 females), China (N = 413; 145 females), and the UK (N = 84; 65 females) completed the ATAI scale, for which the factorial structure was tested and compared between the samples. Participants from Germany and China were additionally asked about their willingness to interact with/use self-driving cars, Siri, Alexa, the social robot Pepper, and the humanoid robot Erica, which are representatives of popular artificial intelligence products. The results showed that the five-item ATAI scale comprises two negatively associated factors assessing (1) acceptance and (2) fear of artificial intelligence. The factor structure was found to be similar across the German, Chinese, and UK samples. Additionally, the ATAI scale was validated, as the items on the willingness to use specific artificial intelligence products were positively associated with the ATAI Acceptance scale and negatively with the ATAI Fear scale, in both the German and Chinese samples. In conclusion we introduce a short, reliable, and valid measure on the attitude towards artificial intelligence in German, Chinese, and English language. 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36187 Towards Strong AI Butz, Martin V. Strong AI—artificial intelligence that is in all respects at least as intelligent as humans—is still out of reach. Current AI lacks common sense, that is, it is not able to infer, understand, or explain the hidden processes, forces, and causes behind data. Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic. In contrast, various sources of evidence from cognitive science suggest that human brains engage in the active development of compositional generative predictive models (CGPMs) from their self-generated sensorimotor experiences. Guided by evolutionarily-shaped inductive learning and information processing biases, they exhibit the tendency to organize the gathered experiences into event-predictive encodings. Meanwhile, they infer and optimize behavior and attention by means of both epistemic- and homeostasis-oriented drives. I argue that AI research should set a stronger focus on learning CGPMs of the hidden causes that lead to the registered observations. Endowed with suitable information-processing biases, AI may develop that will be able to explain the reality it is confronted with, reason about it, and find adaptive solutions, making it Strong AI. Seeing that such Strong AI can be equipped with a mental capacity and computational resources that exceed those of humans, the resulting system may have the potential to guide our knowledge, technology, and policies into sustainable directions. Clearly, though, Strong AI may also be used to manipulate us even more. Thus, it will be on us to put good, far-reaching and long-term, homeostasis-oriented purpose into these machines. 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36189 Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey Nguyen, Phuong D. H.; Georgie, Yasmin Kim; Kayhan, Ezgi; Eppe, Manfred; Hafner, Verena Vanessa; Wermter, Stefan Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration. 2021-01-01T00:00:00Z