The human capability to reason about one domain by using knowledge of other domains has been researched for more than 50 years, but models that are formally sound and predict cognitive process are sparse. We propose a formally sound method that models associative reasoning by adapting logical reasoning mechanisms. In particular it is shown that the combination with large commensense knowledge within a single reasoning system demands for an efficient and powerful association technique. This approach is also used for modelling mind-wandering and the Remote Associates Test (RAT) for testing creativity. In a general discussion we show implications of the model for a broad variety of cognitive phenomena including consciousness.

Augusta Ada King, Countess of Lovelace, widely regarded as the world's first computer programmer, when talking about the Analytical Engine said, "The Analytical Engine has no pretensions whatever to originate anything" [1]. Hence, it is safe to say that the question "Can computers think?", in some form, not only predates the concept of Artificial Intelligence (AI) but is almost as old as the Analytical Engine. This question has stimulated the minds of pioneers and researchers from different domains including computer science, mathematics, psychology and philosophy. This essay delves into some of the important facets of this question. It is primarily driven by the thoughts and arguments of Alan M. Turing and John R. Searle, two pioneers who have extensively explored this question.

Botnet is one of the major threats to computer security. In previous botnet command and control (C&C) scenarios using online social networks (OSNs), methods for finding botmasters (e.g. ids, links, DGAs, etc.) are hardcoded into bots. Once a bot is reverse engineered, botmaster is exposed. Meanwhile, abnormal contents from explicit commands may expose botmaster and raise anomalies on OSNs. To overcome these deficiencies, we propose an AI-powered covert C&C channel. On leverage of neural networks, bots can find botmasters by avatars, which are converted into feature vectors. Commands are embedded into normal contents (e.g. tweets, comments, etc.) using text data augmentation and hash collision. Experiment on Twitter shows that the command-embedded contents can be generated efficiently, and bots can find botmaster and obtain commands accurately. By demonstrating how AI may help promote a covert communication on OSNs, this work provides a new perspective on botnet detection and confrontation.

In this paper we study the connection between the structure and properties of the so-called free association network of the English language, and the solution of psycholiguistical Remote Association Tests (RATs). We show that average hardness of individual RATs is largely determined by the relative positions of the test words (stimuli and response) on the free association network. We argue that solution of RATs can be interpreted as a first passage search problem on a free association network and study a variety of different search algorithms. We demonstate that in easy RATs (those solved by more than 64% subjects in 15 seconds) there are strong links directly connecting stimuli and response, and thus an efficient strategy consist in activating these direct links. In turn, the most efficient mechanism of solving medium and hard RATs consists of preferentially following what we call "moderately weak" associations.