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The goal of this paper is to establish a hierarchical level of deception which does not apply only to humans and non-human animals, but also to the rest of the living world, including plants. We will follow the hierarchical categorization of deception, set forth by Mitchell (1986), in which the first level of deception starts with mimicry, while the last level of deception includes learning and intentionality, usually attributed to primates. We will show how such a hierarchy can be attributed to bacteria, plants, and fungi, see that self-deception is not inherent only to humans, and then connect the evolutionary roots of deception with the philosophical notion of intentionality.

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AIcomplete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deeplearning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI.

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Over the last years, much Egyptological research has been conducted in Croatia across various different fields. The Croato- Aegyptica Electronica (CAE) project has been in progress and, at several museums across the country, ancient Egyptian artefacts have been analysed and new exhibitions created. At the Archaeological Museum in Zagreb, a radiological study of Egyptian mummies has been conducted and open lectures with a variety of keynote speakers, as well as a workshop on the language of Middle Egypt, have been held. Finally, university curriculums have changed, new publications (articles, catalogues and books) have appeared and international conferences have taken place. In this paper, the authors aim to provide an overview of the Egyptological activity which has occurred in Croatia over the past decade.