This paper aims to highlight the life of computer technologies to understand what kind of ‘technological intentionality’ is present in computers based upon the phenomenological elements constituting the objects in general. Such a study can better explain the effects of new digital technologies on our society and highlight the role of digital technologies by focusing on their activities. Even if Husserlian phenomenology rarely talks about technologies, some of its aspects can be used to address the actions performed by the digital (...) technologies by focusing on the objects’ inner ‘life’ thanks to the analysis of passive synthesis and phenomenological horizons in the objects. These elements can be used in computer technologies to show how digital objects are ‘alive.’ This paper focuses on programs developed through high-order languages like C++ and unsupervised learning techniques like ‘Generative Adversarial Model.’ The phenomenological analysis reveals the computer’s autonomy within the programming stages. At the same time, the conceptual inquiry into the digital system’s learning ability shows the alive and changeable nature of the technological object itself. (shrink)

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we (...) present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10. We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, “ironbar” and “Trrrrr,” from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines. (shrink)